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products = new Object();
products["be"] = new Object();
products["be"].toc = "mes_toc";
products["be"].title = "../common/images/single_product/titles/bioexplorer.gif";
products["be"].location = "Metalife Trinity > Explorer > BioExplorer";
products["be"].keyFeatures = new Array();
products["be"].keyFeatures.push("automatic compilation of protein dossiers from more than 80 data sources");
products["be"].keyFeatures.push("detailed information that is structured according to the biological principles");
products["be"].keyFeatures.push("text extracted information about interaction partners, pathway, diseases, etc.");
products["be"].keyFeatures.push("minimized redundancy by integration of equivalent information");
products["be"].keyFeatures.push("back traceable information sources");
products["be"].keyFeatures.push("extensive search options including ontology or pathway browsing");
products["be"].keyFeatures.push("classification of search results");
products["be"].keyFeatures.push("precise filtering of results");
products["be"].keyFeatures.push("rating and ranking of result output");
products["be"].keyFeatures.push("intrinsic documentation of analyses");
products["be"].description = "Metalife BioExplorer automatically compiles detailed dossiers about genes and proteins providing fast \
and easy access to the relevant information. These dossiers contain information compiled from more than \
80 databases. Additionally, each entry includes information extracted from scientific publications. By utilizing \
metalife’s sophisticated text extraction procedures BioExplorer summarizes knowledge about interaction partners, \
pathways or diseases that is otherwise hidden in the plethora of published text. Metalife BioExplorer offers \
detailed information that is categorized according to biological criteria. Among others, these categories comprise \
structure, function or variants of proteins. The information content of individual categories is compiled from diverse \
data sources. Equivalent information (e.g. variants, isoforms or fragments of proteins) is combined in single entries \
which dramatically reduces redundancy.
Hyperlinks allow the user to verify the information presented in the dossiers by \
tracing it back to its sources. For example, information extracted from text is always hyperlinked to the original \
citation.
Our powerful and flexible search engine provides extremely fast access to all information. The extensive \
search options give the opportunity for single box queries, specialized searches, and BLAST searches. The hierarchically \
categorized pathways and the ontology driven classification system allow browsing through the information with respect to \
particular functions and processes. The query results are classified according to the category that matches the \
query. The results are scored and ranked respectively. By selecting individual categories the user may restrict the \
number of hits. Also, multiple filtering options may be selected to refine the query further.";
products["be"].pic = "../common/images/single_product/scrnshot_bioexplorer.jpg";
products["be"].icon = "../common/images/single_product/icons/bioexplorer.gif";
/*Interaction Viewer*/
products["iv"] = new Object();
products["iv"].toc = "mes_toc";
products["iv"].title = "../common/images/single_product/titles/interaction_viewer.gif";
products["iv"].location = "Metalife Trinity > Explorer > Interaction Viewer";
products["iv"].keyFeatures = new Array();
products["iv"].keyFeatures.push("visualization of protein-protein interactions");
products["iv"].keyFeatures.push("interactive graphical exploration of interaction networks");
products["iv"].keyFeatures.push("visualization in the context of subcellular localization");
products["iv"].keyFeatures.push("identification of indirect interactions");
products["iv"].keyFeatures.push("incorporation of information about interactions that is extracted from scientific publications");
products["iv"].keyFeatures.push("easy integration of proprietary interaction data");
products["iv"].keyFeatures.push("analysis of proprietary data in conjunction with worldwide knowledge");
products["iv"].keyFeatures.push("filtering according to data source, localization or type of interaction");
products["iv"].keyFeatures.push("back traceable information sources");
products["iv"].keyFeatures.push("intrinsic documentation of analyses");
products["iv"].description = "Metalife Interaction Viewer is a tool for visualization and analysis of molecular interaction networks. \
The program allows to explore graphically and interactively interaction networks. It visualizes interactions \
in the context of their subcellular localization. In addition to direct interactions the viewer allows to identify \
distant indirect interactions mediated by several interaction partners.
\
The Interaction Viewer visualizes both data from specialized interaction databases and interactions \
that is collected from scientific publications by the Metalife’s text extraction procedure. The easy \
integration of proprietary data into the metalife knowledge base allows to compare interaction data \
that is generated from different experiments and to analyze them in conjunction with the worldwide knowledge. \
The filtering options allow to determine the data sources to display. Data from distinct sources can be \
added or removed. Additional filters are designed to select types of interaction or subcellular compartments. \
The reliability of the text extracted information is scored and can be filtered according to given confidence criteria.
\
All information presented by the interaction viewer is directly linked to its source allowing fast and easy verification. \
The application provides intrinsic documentation of all analyses.";
products["iv"].pic = "../common/images/single_product/scrnshot_interacton_viewer.jpg";
products["iv"].icon = "../common/images/single_product/icons/interaction_viewer.gif";
/*Pathway Viewer*/
products["pv"] = new Object();
products["pv"].toc = "mes_toc";
products["pv"].title = "../common/images/single_product/titles/pathway_viewer.gif";
products["pv"].location = "Metalife Trinity > Explorer > Pathway Viewer";
products["pv"].keyFeatures = new Array();
products["pv"].keyFeatures.push("automatic visualization of pathways");
products["pv"].keyFeatures.push("interactive graphical exploration");
products["pv"].keyFeatures.push("visualization in the context of subcellular localization");
products["pv"].keyFeatures.push("visualization of regulatory elements");
products["pv"].keyFeatures.push("calculation of pathways from single interactions and reactions");
products["pv"].keyFeatures.push("pathway data from specialized data sources");
products["pv"].keyFeatures.push("pathway data that is extracted from scientific texts");
products["pv"].keyFeatures.push("easy integration of proprietary data");
products["pv"].keyFeatures.push("extensive search options (e.g. pathway browsing)");
products["pv"].keyFeatures.push("filtering options (e.g. filter for disease related pathways)");
products["pv"].description = "The Metalife Pathway Viewer is an universal tool for visualization of metabolic and signaling pathways. \
It calculates complete pathways that can be interactively and graphically explored using the data about single \
reactions and interactions. All elements are displayed in the context of their subcellular localization. \
The pathways are complemented by enzymatic and genetic elements of regulation.
\
The Pathway Viewer visualizes data from different sources that contains information about whole pathways \
or single interactions attributed to defined pathways. More and more pathways are calculated from interactions \
described in scientific articles and identified by the Metalife’s text extraction procedure. \
The easy integration of proprietary data into the metalife knowledge base allows to compare pathways and \
interactions generated from experiments and analyze them in conjunction with the worldwide knowledge.
\
The user may browse the pathways with respect to particular functions and processes through the hierarchical classification \
system. Additional filters enable the user to specify the query further and to search selectively (e.g. for disease related pathways).";
products["pv"].pic = "../common/images/single_product/scrnshot_pathway_viewer.jpg";
products["pv"].icon = "../common/images/single_product/icons/pathway_viewer.gif";
/*Genome Viewer*/
products["gv"] = new Object();
products["gv"].toc = "mes_toc";
products["gv"].location = "Metalife Trinity > Explorer > Genome Viewer";
products["gv"].title = "../common/images/single_product/titles/genome_viewer.gif";
products["gv"].keyFeatures = new Array();
products["gv"].keyFeatures.push("interactive and graphical exploration of genomes");
products["gv"].keyFeatures.push("visualization at different levels of detail: from caryotype to sequence");
products["gv"].keyFeatures.push("visualization of annotated elements from various sources");
products["gv"].keyFeatures.push("visualization of genes, transcripts, ESTs, mRNAs, intron-exon structure, etc.");
products["gv"].keyFeatures.push("visualization of variants (SNPs, splice variants)");
products["gv"].keyFeatures.push("visualization of the distribution of marker genes across the genome");
products["gv"].keyFeatures.push("additional information about genes (pathways, diseases, etc.)");
products["gv"].keyFeatures.push("additional information that is extracted from scientific publications");
products["gv"].keyFeatures.push("various filtering options");
products["gv"].description = "Metalife Genome Viewer visualizes annotated elements of whole genomes. \
Complete genomes can be explored graphically and interactively. \
The user may display genomes at different levels of detail simultaneously: from caryotype and cytoband structure \
of chromosomes, down to single annotated elements and their sequence.
\
In addition to sequence data like ests, mrnas, intron-exon structures, predicted transcripts \
and annotated genes from different sources it displays cytogenetic and physical data as well as \
associations of genes with pathways or diseases. It integrates also information about variants, like snps and proven \
and predicted splice variants. More and more additional information is included from the Metalife’s text extraction \
procedures. The extensive filtering options allow to selectively choose individual elements and different sources of \
information to display.
\
The chromosomal location of markers (e.g. genes related to a certain pathway) can be queried and identified directly. \
The simultaneous graphical display of the location of different elements on the caryomap allows to analyze the distribution \
of sets of genes (like genes that are generated from experimental results) across the genome.
\
All elements are connected with the appropriate entries in the Metalife Explorer and with their sources. \
The resulting renderings may be zoomed and the searches can be interactively refined.";
products["gv"].pic = "../common/images/single_product/scrnshot_genome_viewer.jpg";
products["gv"].icon = "../common/images/single_product/icons/genome_viewer.gif";
/*Genome Comparison Tool*/
products["gct"] = new Object();
products["gct"].toc = "mps_toc";
products["gct"].title = "../common/images/single_product/titles/genome_comparison_tool.gif";
products["gct"].location = "Metalife Trinity > Predictor > Genome Comparison Tool";
products["gct"].keyFeatures = new Array();
products["gct"].keyFeatures.push("analysis and comparison of genomes, contigs, CDSs, protein sequences and user defined regions in genomes on a large scale");
products["gct"].keyFeatures.push("exploration, analysis and visualization of biological data in the context of completely and incompletely sequenced genomes");
products["gct"].keyFeatures.push("integrated algorithms for sequence comparison");
products["gct"].keyFeatures.push("transfer of sequences from other Metalife tools or integrated proprietary databases");
products["gct"].keyFeatures.push("upload of user defined sequences");
products["gct"].keyFeatures.push("user-defined jobs to analyze genomes");
products["gct"].keyFeatures.push("quick navigation and access to identified regions and their features");
products["gct"].keyFeatures.push("alignment of ortologues and homologues regions");
products["gct"].description = "Due to the fast growing number of newly sequenced genomes, the demand on software tools \
that are able to gather biological information by multiple comparison of genomes is increasing.
\
One of the most useful tools for a molecular biologist to handle the vast amount of genomes, genes and proteins \
is the automated large scale genome comparison using conventional local alignment tools like blast.
\
Metalife Genome Comparison Tool incorporates a graphical user interface that allows our customers to analyze and \
compare genomes, contigs, cds and protein sequences, and various user defined regions on the genomes on a large \
scale, and also to look for their sequence annotations in selected databases.
\
The visualized data is easy to read and offers a graphical representation and text information for the \
compared genomes and sequences, thus allowing multiple genome comparisons to detect orthologues and homologues genes, \
identical parts among multiple genomes, clustering of ortologue genes and much more.
\
Genome Comparison Tool is a product that aims to provide the functionality to query, explore, analyze \
and visualize biological data in the context of completely and incompletely sequenced genomes. The functionality of \
the product is structured in several modules due to the complexity of the information.
\
Metalife Genome Comparison Tool integrates algorithms for pairwise local alignments like BLAST, MegaBLAST and BLASTZ. \
Other algorithms such as cognitor and cogfinder are also integrated in the metalife genome comparison tool.";
products["gct"].pic = "../common/images/single_product/scrnshot_gct.jpg";
products["gct"].icon = "../common/images/single_product/icons/genome_comparison_tool.gif";
/*Epitope Validator*/
products["ev"] = new Object();
products["ev"].toc = "mps_toc";
products["ev"].location = "Metalife Trinity > Predictor > Epitope Validator";
products["ev"].title = "../common/images/single_product/titles/epitope_validator.gif";
products["ev"].keyFeatures = new Array();
products["ev"].keyFeatures.push("prediction of B-cell epitopes with unprecedented accuracy");
products["ev"].keyFeatures.push("customizable combination of different prediction algorithms");
products["ev"].keyFeatures.push("graphical presentation of calculated antigenicity");
products["ev"].keyFeatures.push("scoring and ranking of the predicted epitopes depending on the order of the antigenic probability");
products["ev"].keyFeatures.push("high throughput epitope prediction and analysis");
products["ev"].description = "Metalife Epitope Validator is one of the major tools that are developed by the Metalife AG specialists in the predictor package. \
The Epitope Validator contains several highly efficient antigenic determinant (B-cell epitopes) prediction algorithms. \
It employs methods and algorithms for predicting the secondary structure, hydropathy, hydrophobicity, flexibility and \
surface probability, that help to predict real epitopic regions in a given protein sequence.
\
Several methods are calculating a statistical score for the antigenicity value of each residue in a given sequence. \
Two of the algorithms are dealing with the calculation of hydrophobicity, hydrophilicity and hydropathy of the sequence based \
on its primary sequence. The antigenic index method is used to create a linear surface contour profile of the protein.
\
The graphical user interface is intuitive and close to the most commonly used desktop applications. Its purpose is to \
facilitate the research in the field of immunology and other related fields, and to support it by achieving more reliable \
results with less effort.
\
Metalife Epitope Validator has the functionality to export the results in both ways - to a text document and to a \
graphical file. It allows the user to send found epitopic regions back to the sequence sets as separate sequences for \
further analysis.
\
Combination of the state-of-the-art algorithms for calculating sequence properties in real time is among the main \
advantages. Its fast and reliable results are easily manageable and compatible with other products. High quality prediction \
and validation of epitopes reduces the number of false positive epitope sequences. Thus, high accuracy in epitope prediction \
along with validation of these epitopes using information stored in scientific literature both speeds up and lowers costs \
of antibody production.";
products["ev"].pic = "../common/images/single_product/scrnshot_epitope_validator.jpg";
products["ev"].icon = "../common/images/single_product/icons/epitope_validator.gif";
/*Patent Analyzer*/
products["pa"] = new Object();
products["pa"].toc = "mas_toc";
products["pa"].title = "../common/images/single_product/titles/patent_analyzer.gif";
products["pa"].location = "Metalife Trinity > Analyzer > Patent Analyzer";
products["pa"].keyFeatures = new Array();
products["pa"].keyFeatures.push("import, organisation and index of the data contained in patents");
products["pa"].keyFeatures.push("search through collections of patent documents");
products["pa"].keyFeatures.push("extraction of fielded data or keywords from a patent document and analyzis of the data by different criteria");
products["pa"].keyFeatures.push("automatical generation of keyword trees");
products["pa"].keyFeatures.push("systematical clustering and categorization of documents");
products["pa"].keyFeatures.push("identification of relations between biological objects");
products["pa"].keyFeatures.push("identification of patents that have “indirect but meaningful” relationships with a subject patent");
products["pa"].keyFeatures.push("diagrams with hierarchical trees, time-series charts, tables, and graphs");
products["pa"].description = "Metalife Patent Analyzer is a software tool for patent analysis, classification and categorization that presents textual \
as well as graphical reports of the performed analysis.
\
The knowledge mining industry has changed a lot in the past years with the extensive use of computers and software \
techniques. These trends are affecting the patent sector too. The process of categorizing, classifying or \
analyzing a patent has become easier by using the computer power in conducting effective searches.\
Big companies are placing a strong emphasis on obtaining intellectual property protection for new products. \
They have also realized that their already existing portfolio could contain unused potential that can lead to \
significant benefits and bring millions or even billions of profit to their organization.\
The benefits of patent mining go far beyond the royal profit - a company that realizes the \
hidden potential in the existing portfolio is in a much better position when negotiating with companies that are \
interested in buying company's patents. Reducing risk \
is another advantage in patent mining.\
The careful use and management of a company’s patent portfolio is a prerequisite for leading the r&d process in the \
right direction and plugging existing holes in the patent coverage.";
products["pa"].pic = "../common/images/single_product/scrnshot_patent_analyzer.jpg";
products["pa"].icon = "../common/images/single_product/icons/patent_analyzer.gif";
/*Recognition Tool*/
products["rt"] = new Object();
products["rt"].toc = "mas_toc";
products["rt"].title = "../common/images/single_product/titles/recognition_tool.gif";
products["rt"].location = "Metalife Trinity > Analyzer > Recognition Tool";
products["rt"].keyFeatures = new Array();
products["rt"].keyFeatures.push("sophisticated linguistically motivated statistical methods");
products["rt"].keyFeatures.push("embedded semantic and syntactic relation recognition routines");
products["rt"].keyFeatures.push("finding biologically meaningful relations between objects within text (e.g. the association between a given disease and known drugs)");
products["rt"].keyFeatures.push("false positive results recognition");
products["rt"].keyFeatures.push("unique Metalife developed algorithm to find alternative names (e.g. synonyms, acronyms, abbreviations) automatically from text");
products["rt"].keyFeatures.push("unique Metalife developed algorithm for automatic biological objects recognition within text (e.g. recognizes chemical structures within text without a priori knowledge)");
products["rt"].description = "Metalife Recognition Tool is a software application for online analysis of any type of scientific text.
\
The possibilities in mining in text are broadening with the advance of modern technology. Text expresses vast, \
reach range of information, which is not kept in a structured format and it is not easy to decipher that information \
automatically.
\
Going beyond the familiar systems that are dealing with information retrieval (which has the main goal of \
finding the query relative information) Metalife Recognition Tool is a software application for text mining in \
scientific literature. The main goal of Metalife when creating this tool was to design a software \
application that will help the researchers to extract new information from the big unexplored knowledgebase- \
the world of scientific texts, find common patterns, distinguish meaningful from meaningless information, find \
answers that are not currently known.
\
Via using text mining, new hypothesis could be found and later on they can be experimentally proven. However, in order to achieve that goals, sophisticated algorithms taking in mind different semantic constraints for finding these concepts within text are needed. Test a demo version of the Metalife text mining approaches!";
//Via using text mining new hypothesis could by found and later on they can be experimentally proven. \
//However, in order to achieve that goals sophisticated algorithms taking in mind different semantic constraints for \
//finding these concepts within text are needed.";
products["rt"].pic = "../common/images/single_product/scrnshot_recognition_tool.jpg";
products["rt"].icon = "../common/images/single_product/icons/recognition_tool.gif";
/*Text Analyzer*/
products["ta"] = new Object();
products["ta"].toc = "mas_toc";
products["ta"].title = "../common/images/single_product/titles/text_analyzer.gif";
products["ta"].location = "Metalife Trinity > Analyzer > Text Analyzer";
products["ta"].keyFeatures = new Array();
products["ta"].keyFeatures.push("identification of reliable biological entities (protein names, gene names, chemical compounds, drugs, diseases)");
products["ta"].keyFeatures.push("identification of inter-entity relationships, such as enhancement and inhibition between drugs, biological substances and diseases");
products["ta"].keyFeatures.push("effective generation of complex relationship networks based on selected biological entities that could be used in the inference steps");
products["ta"].keyFeatures.push("generation and validation of hypothesis overcoming the limitation of the number of relationships between entities");
products["ta"].keyFeatures.push("easy detection of false positive relationships");
products["ta"].keyFeatures.push("generation of pathways from text");
products["ta"].keyFeatures.push("mapping of generated networks onto known pathways");
products["ta"].description = "The volume of biomedical publications continues to expand rapidly. With such tremendous growth it is challenging to \
keep up to date with the current knowledge even in one’s specific field. Computational methods should be developed \
to help the researchers to make a practical use of the existing discoveries and to publish new innovative theories.
\
The text mining techniques are of great help to researchers in reducing the information overload. They are offering the \
researchers ways to identify important connections between objects in the text. They also contribute to solutions of specific \
problems that can not be obtained by using the usual search engines.
\
Metalife Text Analyzer is a software application for text analysis. It employs the powerful heuristic and semantic \
Metalife developed algorithms in order to offer the user the possibility to generate on-the-fly relationship networks \
from text and afterwards to validate the extracted information by mapping these networks to known pathways.
\
Metalife Text Analyzer addresses the increasing demand for extracting knowledge from text repositories. It is \
designed especially to work with the particularities of the texts from the scientific domain. Test a demo version of the Metalife text mining approaches!";
products["ta"].pic = "../common/images/single_product/scrnshot_text_analyzer.jpg";
products["ta"].icon = "../common/images/single_product/icons/text_analyzer.gif";
/*MetaFind*/
products["mf"] = new Object();
products["mf"].toc = "mes_toc";
products["mf"].title = "../common/images/single_product/titles/metafind.gif";
products["mf"].location = "Metalife Trinity > Explorer > MetaFind";
products["mf"].keyFeatures = new Array();
products["mf"].keyFeatures.push("common search interface for various databases with ultrafast search routines");
products["mf"].keyFeatures.push("automatic update of target databases");
products["mf"].keyFeatures.push("guaranteed quality of data");
products["mf"].description = "Metalife MetaFind unites all primary, secondary and proprietary data sources (Metalife Finder databases, \
Metalife BioExplorer and Metalife High-Value Sequence Repository respectively) in a common user interface \
with a single search engine.
\
The user interface designers at metalife are investing time and resources for implementing Metalife MetaFind in \
the best way, thus keeping up with the standards of the interface usability issue. The database model- a quite \
challenging one, is designed by the company to fulfill the estimation of minimum time and error rate.
\
The biological data sources are integrated in the database model. a short description of each one of them is loaded \
in the information panel. After MetaFind is launched a list of all databases included in the current release of Metalife \
Explorer will be displayed. The primary databases are listed in alphabetical order including information about their release \
and release dates.
\
MetaFind is not built as a static resource that is filled with enormous data, but as dynamically configured and \
continuously updated entity. Our specially designed software is dealing with the automatic download of the new \
releases of all databases included in the product. Furthermore, this files are processed by the automatic database flat \
files readers, and at last the stage, if errors are reported during the processes, our personnel is reviewing all the \
cases and the trouble tickets.";
products["mf"].pic = "../common/images/single_product/screenshots/metafind.jpg";
products["mf"].icon = "../common/images/single_product/icons/metafind.gif";
/*Keyword Search*/
products["ks"] = new Object();
products["ks"].toc = "mes_toc";
products["ks"].title = "../common/images/single_product/titles/keyword_search.gif";
products["ks"].location = "Metalife Trinity > Explorer > Keyword Search";
products["ks"].keyFeatures = new Array();
products["ks"].keyFeatures.push("simple and advanced boolean queries of more than 50 public databases with biological knowledge");
products["ks"].keyFeatures.push("ultrafast search engine");
products["ks"].keyFeatures.push("cross-linked entries that allow to find easily additional information");
products["ks"].keyFeatures.push("intuitive interface that allows to track down all sources of information");
products["ks"].description = "Metalife Keyword Search is an integrated system for querying local copies of around 50 of the most important \
bioinformatical databases like UniProt, UniProtKB, NCBI's GenBank, RefSeq, Entrez Gene etc.It provides also an \
interface to the Metalife knowledge databases including PhenomicDB and BioExplorer being among the most \
important ones.
\
The entries that are found by the Metalife Keyword Search may be individually reviewed in detail inside the \
application. Each entry is crosslinked with various other data sources like PubMed, PDB, Interpro etc, thus \
giving the user the opportunity to track down the information only by a couple of mouse clicks.
\
Metalife Keyword Search is also in tight cooperation with the rest of the Metalife tools (e.g. it allows an \
easy and automated transfer of sequence entries for algorithmic analysis inside the Metalife Predictor Suite).
";
products["ks"].pic = "../common/images/single_product/screenshots/keyword_search.jpg";
products["ks"].icon = "../common/images/single_product/icons/keyword_search.gif";
/*HVSR*/
products["hvsr"] = new Object();
products["hvsr"].toc = "mes_toc";
products["hvsr"].title = "../common/images/single_product/titles/hv_sequence_repository.gif";
products["hvsr"].location = "Metalife Trinity > Explorer > High-Value Sequence Repository";
products["hvsr"].keyFeatures = new Array();
products["hvsr"].keyFeatures.push("efficient storage of large amounts of data");
products["hvsr"].keyFeatures.push("user-defined annotation and ranking");
products["hvsr"].keyFeatures.push("built-in redundancy check routines");
products["hvsr"].keyFeatures.push("intuitive interface that allows to track down all sources of information");
products["hvsr"].description = "Metalife High-Value Sequence Repository is built as a server side repository for biological objects.\
The repository itself has the purpose to serve as a knowledge based system for information retrieval and submission.
\
New biological objects are registered in the repository only if they pass the Metalife developed redundancy check routine, which \
allows establishing relationships with already annotated entries.The database uses also homology models for detecting similarities \
in newly suggested entries. Each registered object could be annotated with information from the primary database sources and also \
with data from in silico prediction and analysis.
\
Metalife High-Value Sequence Repository is a highly optimized and scalable system, designed to meet the need of enormous content \
management and knowledge based discovery models by the pharmaceutical industry.";
products["hvsr"].pic = "../common/images/single_product/screenshots/hvsr.jpg";
products["hvsr"].icon = "../common/images/single_product/icons/hvsr.gif";
/*Phenomic DB*/
products["phdb"] = new Object();
products["phdb"].toc = "mes_toc";
products["phdb"].title = "../common/images/single_product/titles/phenomic_db.gif";
products["phdb"].location = "Metalife Trinity > Explorer > PhenomicDB";
products["phdb"].keyFeatures = new Array();
products["phdb"].keyFeatures.push("integrated genotype/phenotype relationships");
products["phdb"].keyFeatures.push("integrated ortholog relationships");
products["phdb"].keyFeatures.push("cross-linked entries that help to find easily additional information");
products["phdb"].keyFeatures.push("intuitive interface that allows to track down the information about all sources of data");
products["phdb"].description = "Metalife PhenomicDB is a multi-species genotype/phenotype database created by merging public genotype/phenotype \
data from a wide range of model organisms and human. The data in PhenomicDB is integrated in our online resource \
by coarse-grained semantic mapping of the phenotypic data fields, by including common gene indices (NCBI Gene), \
and by the use of associated orthology relationships.
\
Most of the valuable phenotypic data reside in the public literature and is not captured in databases. Metalife \
PhenomicDB is gathering phenotype and genotype data from different public resources and maps the data semantically into a \
single data model. It was built as a step towards comparative phenomics and will improve our understanding of gene function \
by combining the knowledge about phenotypes from several different organisms.
\
The advanced query interface finds results that show the user genotypes and phenotypes of further interest and expand \
these with their orthologues genes and the phenotypes of their orthologues genes. This allows direct and simultaneous \
comparison of all available phenotipic data for a certain gene in all available organisms. Each result is \
hyperlinked to a dedicated genotype/phenotype report and directly to the source database.";
products["phdb"].pic = "../common/images/single_product/screenshots/phenomic_db.jpg";
products["phdb"].icon = "../common/images/single_product/icons/phenomic_db.gif";
/*Gene Nominator*/
products["gs"] = new Object();
products["gs"].toc = "mes_toc";
products["gs"].title = "../common/images/single_product/titles/gene_nominator.gif";
products["gs"].location = "Metalife Trinity > Explorer > Gene Nominator";
products["gs"].keyFeatures = new Array();
products["gs"].keyFeatures.push("project-oriented management of sequence related documents");
products["gs"].keyFeatures.push("flexible gene ranking and overall project scoring schemes");
products["gs"].keyFeatures.push("project versioning");
products["gs"].description = "Metalife Gene Nominator is a supplement to the sequence repository, where selected entries can be further evaluated and filtered in the context of a given laboratory project. The application extends the abilities of the sequence repository for manual annotation and serves as a central repository for storing all kind of documents related to a given entry from the sequence repository. Each registered gene receives a dedicated score card, that stores the user’s evaluation of the gene’s relevance by a configurable number of criteria. Then, an overall score is calculated based on the single scores inside the score card. by comparing the overall scores of different genes. Priviledged users may decide whether a particular gene has to be submitted further through the research and development pipeline.
\
Metalife Gene Nominator provides also a full score card versioning. Snapshots of the current state of a score card can be taken and used for further reference. The application is also fully integrated with the Metalife Keyword Search tool.";
products["gs"].pic = "../common/images/single_product/screenshots/gene_nominator.jpg";
products["gs"].icon = "../common/images/single_product/icons/gene_nominator.gif";
/*Job Designer*/
products["jd"] = new Object();
products["jd"].toc = "mps_toc";
products["jd"].title = "../common/images/single_product/titles/job_designer.gif";
products["jd"].location = "Metalife Trinity > Predictor > Job Designer";
products["jd"].keyFeatures = new Array();
products["jd"].keyFeatures.push("custom built algorithmic pipelines");
products["jd"].keyFeatures.push("more than 140 integrated ready-to-use and easy-to-pick bioinformatics algorithms");
products["jd"].keyFeatures.push("full parameters control");
products["jd"].keyFeatures.push("storage of workflows for further reuse");
products["jd"].keyFeatures.push("hierarchical project management");
products["jd"].keyFeatures.push("DB oriented storage of results");
products["jd"].description = "Metalife Job Designer provides the functionality to query, explore, analyze and visualize sequences in the context of genomics and proteomics. It is integrating many algorithmic packagers such as Emboss, several types of BLAST algorithms, secondary antigenicity and structure prediction algorithms, and more than 140 other state-of-the-art algorithms for primary structure feature extraction.
\
Metalife Job Designer is not a static user interface for program execution, because it is building dynamically pipelines of algorithms. These pipelines are predefined workflows represented by connecting the inputs and outputs of different algorithms. Also, they provide the ability to perform step-by-step investigations by selecting the query objects only once for the first algorithm in a row.
\
Metalife Job Designer offers an automatic high-throughput annotation of sequences based on a graphical workflow design. The set of algorithms and their individual parameters are fully customizable and are documented along with the results. All analysis results are stored in a database that can be interactively explored or used to generate a fully customizable result outputs to provide both quick overview and in-depth analysis.
\
Algorithms can be individually chosen from a categorized algorithm repository when the user performs a search by category or by keyword. The graphical workspace allows to individually combine different algorithms and to change their default command line and transaction parameters. All workflows in Metalife Job Designer can be saved by the user in order to be reused in the future.
\
Metalife Job Designer has a customizable view and export of results, drag-and-drop functionality for selection of algorithms and inputs and many other useful features.
\
The module for the results of a user’s project is based on a hierarchical tree of projects and their subdivisions. Every project may be shared with other users that are logged in the Metalife software system.";
products["jd"].pic = "../common/images/single_product/screenshots/job_designer.jpg";
products["jd"].icon = "../common/images/single_product/icons/job_designer.gif";
/*Blast Search*/
products["bs"] = new Object();
products["bs"].toc = "mps_toc";
products["bs"].title = "../common/images/single_product/titles/blast_search.gif";
products["bs"].location = "Metalife Trinity > Predictor > BLAST Search";
products["bs"].keyFeatures = new Array();
products["bs"].keyFeatures.push("fast BLAST Search in various databases");
products["bs"].keyFeatures.push("graphical visualisation of results that offers better exploration");
products["bs"].keyFeatures.push("multiple Alignments of BLAST hits with ClustalW");
products["bs"].keyFeatures.push("integration with other Metalife algorithmic tools");
products["bs"].description = "Metalife BLAST Search offers a fast way to find homologue sequences in the primary databases, sequence repositories and our client’s proprietary data. For this purpose we employ the BLAST algorithm to perform a rapid scanning of nucleotide and protein databases that may be selected by the users.
\
The analysis of the homologue sequences found by the Metalife BLAST Search may be extended by computing their multiple alignments with ClustalW - a general purpose multiple sequence alignment program for nucleic acid or protein sequences. A multiple sequence alignment may reveal very important biological properties that remain hidden for a single pairwise alignment like transcription factor binding sites, protein domains etc.
\
The analysis with the application could be also extended by applying various other algorithms for sequence analysis, for example, to predict the presence and the location of signal peptide cleavage sites or to identify transmembrane helices in amino acid sequences from different organisms."; ///The analysis with the application could be also extended by applying various other algorithms for sequence analysis. For example, to predict the presence and the location of signal peptide cleavage sites or to identify transmembrane helices in amino acid sequences from different organisms.
products["bs"].pic = "../common/images/single_product/screenshots/blast_search.jpg";
products["bs"].icon = "../common/images/single_product/icons/blast_search.gif";
/*ECT*/
products["ect"] = new Object();
products["ect"].toc = "mps_toc";
products["ect"].title = "../common/images/single_product/titles/est_clustering_tool.gif";
products["ect"].location = "Metalife Trinity > Predictor > EST Clustering Tool";
products["ect"].keyFeatures = new Array();
products["ect"].keyFeatures.push("project-based model for EST clustering and contig assembly");
products["ect"].keyFeatures.push("integrated algorithms for sequence masking, filtering, vector removal, pairwise alignments, clustering, assembly and annotation");
products["ect"].keyFeatures.push("intelligent wizard-based graphical user interfaces");
products["ect"].keyFeatures.push("comprehensive vizualization of results");
products["ect"].keyFeatures.push("sophisticated management of results");
products["ect"].description = "Metalife EST Clustering Tool is built to contribute to users’ automatic contig assembly, annotation and visualization. It may be used also as a research tool because it contains integrated data sets like predefined nematode ESTs and others.
\
Metalife EST Clustering Tool gives the ability to join results that are executed with different parameters together with visualization in the same results’ pane and to map the results of the EST clustering and assembly process to a desired genome, which is previously imported or predefined. The results of the user’s projects are organized in a hierarchical tree of projects with subdivisions. Every project may be shared with other users that are logged in the Metalife software system.
\
Metalife EST Clustering Tool employs algorithms for sequence masking, filtering, vector removal, pairwise alignments, clustering, assembly and annotation of clusters with gene ontologies. By setting the system permissions it is possible to allow the user to create own jobs and manage other users’ pipelines.
\
Our primary concern in developing Metalife EST Clustering Tool was usability. An inexperienced user must be able to get familiar and work with the tool in a short time. For this reason, the interface is wizard-based, which offers a great help in the process of decision-making. It is intuitive and close to the one of most commonly used desktop applications. The wizard mode provides an easy way to create a job with algorithms for EST clustering and assembly. The sophisticated context menus speed up the work and thus save lots of time for our customers as well. The user interface designers at Metalife are investing time and resources for implementing the EST Clustering Tool in the best way, thus keeping up with the standards for the interface usability.";
products["ect"].pic = "../common/images/single_product/screenshots/est_clustering_tool.jpg";
products["ect"].icon = "../common/images/single_product/icons/est_clustering_tool.gif";
/*patent generator*/
products["pt"] = new Object();
products["pt"].toc = "mps_toc";
products["pt"].title = "../common/images/single_product/titles/patent_generator.gif";
products["pt"].location = "Metalife Trinity > Predictor > Patent Generator";
products["pt"].keyFeatures = new Array();
products["pt"].keyFeatures.push("a multi-user project-based management of patent applications");
products["pt"].keyFeatures.push("automatic collecting of sequence related information (eg. orthologues and homologues via BLAST searches, patterns, primer sequences etc.)");
products["pt"].keyFeatures.push("management of patent related documents within a project");
products["pt"].keyFeatures.push("automatic export in the format of the World Intellectual Property Organization - WIPO St.25");
products["pt"].description = "Metalife Patent Generator generates automatically the patent applications in a required format using the relevant sequence data, \
received from similarity searches, generation of consensus and pattern sequences, and primers.
\
This application manages the information by keeping track of its biological significance. A patent application within\
the program can be exported to an Excel file or to a text file according to the format of the World Intellectual Property Organization \
Standard 25 (WIPO St.25).
";
products["pt"].pic = "../common/images/single_product/screenshots/patent_generator.jpg";
products["pt"].icon = "../common/images/single_product/icons/patent_generator.gif";
/*Document Manager*/
products["dm"] = new Object();
products["dm"].toc = "mas_toc";
products["dm"].title = "../common/images/single_product/titles/document_manager.gif";
products["dm"].location = "Metalife Trinity > Analyzer > Document Manager";
products["dm"].keyFeatures = new Array();
products["dm"].keyFeatures.push("optimization of the company’s effectiveness by providing a centralized management of collaborative documents ");
products["dm"].keyFeatures.push("search options that vary from simple interface for the casual user to full support of boolean logic and search trough specified fields of the document for the sophisticated user");
products["dm"].keyFeatures.push("builds a powerful company’s knowledgebase");
products["dm"].keyFeatures.push("classification of documents relative to their context");
products["dm"].keyFeatures.push("relevance ranking");
products["dm"].keyFeatures.push("speeds the decision process by reusing the information stored in company documents rather than recreating it");
products["dm"].keyFeatures.push("lowers the administration costs");
products["dm"].description = "Metalife Document Manager is a practical, comprehensive software application for intranet indexing of documents, powerful searches, document classification and analysis.
\
Since there are no two companies with the same requirements for a document management system Metalife Document Manager is the closest to the ‘perfect’ solution that you could get as it is easy customizable to meet the precise organization requirements.
\
Document management within an organization is a complex issue. the documents within the organization are an important part of the company’ s management assets. Every company spends significant time and efforts to create and organize documents in the internal network. The larger the collection, the harder and harder becomes to find that important presentation, contract, or HR form. The rapid lowering of the costs of computer technologies removed all the barriers for companies to create documents locally and make them available to the organizational community. Document management is much more than publication and organization. It also needs efficient access and retrieval. Metalife Document Manager provides all this and much more to optimize the performance of the customers’ administration and production process.
\
Metalife Document Manager supports the following file formats:\
\
- text files- .txt \
- rich text format files- .rtf \
- microsoft word files- .doc \
- microsoft excel files- .xls \
- microsoft power point files- .ppt \
- html files- .html, .htm \
- adobe acrobat files- .pdf \
- delimited files- .csv, .tsv
";
products["dm"].pic = "../common/images/single_product/screenshots/document_manager.jpg";
products["dm"].icon = "../common/images/single_product/icons/document_manager.gif";
/*MetaText*/
products["mt"] = new Object();
products["mt"].toc = "mas_toc";
products["mt"].title = "../common/images/single_product/titles/metatext.gif";
products["mt"].location = "Metalife Trinity > Analyzer > MetaText";
products["mt"].keyFeatures = new Array();
products["mt"].keyFeatures.push("Semantically consistent and biologically meaningful entities recognition");
products["mt"].keyFeatures.push("Identification of associations and functional relationships between recognized entities");
products["mt"].keyFeatures.push("Normalization, Unification and Disambiguation of found terms to avoid redundancy and distinguish results for seemingly equal terms which have different categorization");
products["mt"].keyFeatures.push("Result filtering options: by categories, relation stringency levels, taxonomic specificity");
products["mt"].keyFeatures.push("Schematic representation of relationships’ networks and table-view arrangement of the extracted information");
products["mt"].keyFeatures.push("Save an entire workflow of analysis and later reload it for further examination and manipulation");
//products["mt"].keyFeatures.push("Quantitative estimation of the extracted information by various statistical parameters");
products["mt"].description = "Metalife MetaText is a text mining tool designed to cover different aspects of information extraction from biomedical literature in a variety of knowledge domains. Currently 23 distinct ontology-based vocabularies (thesauri) are available, structured to integrate different aspects of genome, proteome and metabolome analysis, cell structure, functions and processes, chemical and biochemical mechanisms, as well as organisms' taxonomy.
\
Metalife thesauri are derived by using various publicly available sources (NCBI, KEGG, UniProt, TAIR, GO, EMBL-EBI, CTD, MeSH, etc.) and are being further enriched and sophisticated in the process of doing miscellaneous tasks intending to meet customers’ requirements.
\
MetaText combines powerful heuristic algorithms that are able to extract various biological objects (recognizing their synonyms, acronyms and spelling variants), construct phrases on the basis of syntax rules, give each of them specific ontology-based categorization, and further extract associations and functional relationships between terms and phrases. Thus, by distinguishing information from different knowledge domains Metalife MetaText is at the same time capable of integrating recognized knowledge by extracting relationships between combinations of different vocabularies (categories). Knowledge domains are being further expanded by building association networks, and analyzed data is being selectively filtered by user-defined criteria. Test a demo version of the Metalife text mining approaches!";
products["mt"].pic = "../common/images/single_product/screenshots/metatext.jpg";
products["mt"].icon = "../common/images/single_product/icons/metatext.gif";
/*Thesaurus Tool*/
products["tht"] = new Object();
products["tht"].toc = "mas_toc";
products["tht"].title = "../common/images/single_product/titles/thesaurus_tool.gif";
products["tht"].location = "Metalife Trinity > Analyzer > Thesaurus Tool";
products["tht"].keyFeatures = new Array();
products["tht"].keyFeatures.push("provides support for adapting to the background of the specific user");
products["tht"].keyFeatures.push("information in the different thesauri is accessible via a sophisticated GUI");
products["tht"].keyFeatures.push("embedded world wide known ontologies and Metalife developed thesauri");
products["tht"].keyFeatures.push("proprietary thesauri are integrated via import wizards embedded in the system");
products["tht"].keyFeatures.push("no limitation on the number of thesauri to be used");
products["tht"].keyFeatures.push("no limitation on the number of levels of hierarchy");
products["tht"].keyFeatures.push("support of synonyms");
products["tht"].keyFeatures.push("version control- allows access to multiple versions of the same thesauri");
products["tht"].description = "Metalife Thesaurus Tool is a complete thesaurus/ontology management system designed to \
help researchers capture and classify relevant information stored in free text.
\
The scientists require the ability to use information from various sources in order to make meaningful discoveries. The rapid increase of many different types of data in different data sources, with different accessions, annotated with inconsistent terminology have made it hard for the average user to identify and coherently query scientific data stored in text. There are many heterogeneous autonomous resources that cover genomic, cellular, structure, phenotype and other biologically relevant information but there are a lot of differences between them- terminological, syntactic and semantic, not to mention the different meaning of identically named categories, overlapping meaning of different categories and conflicting meaning of different categories.
\
Until now if a researcher submits an ambiguous term to query a database he/she had the full responsibility to take care of the congruence of the search and the result. Ontologies help here to identify the right type of concept to be searched.
\
The advent of the micro array analysis for mRNAs added more requirements for terminology standardization. Another reason demanding the use of standards is the merging of fields that historically started as separate disciplines. The fast discovery of cellular, expression, structure, metabolic and regulatory data in many organisms puts additional pressure on utilizing compatible nomenclatures. Another field that benefits from the use of ontologies is the field of text mining and knowledge extraction from text. Where currently mostly statistical and proximity approaches are employed this approach can support parsing and disambiguating sentences and constructing grammatically compatible concepts. ";
products["tht"].pic = "../common/images/single_product/screenshots/thesaurus_tool.jpg";
products["tht"].icon = "../common/images/single_product/icons/thesaurus_tool.gif";
/*Array Analyzer*/
products["aa"] = new Object();
products["aa"].toc = "mas_toc";
products["aa"].title = "../common/images/single_product/titles/array_analyzer.gif";
products["aa"].location = "Metalife Trinity > Analyzer > Array Analyzer";
products["aa"].keyFeatures = new Array();
products["aa"].keyFeatures.push("import of data ");
products["aa"].keyFeatures.push("filtering of the data: missing values, standard deviation, absolute value, variation filter");
products["aa"].keyFeatures.push("data transformation: replicates, missing values, logarithm, data normalization, divide by mean, subtract mean, z- transformation");
products["aa"].keyFeatures.push("analysis of significance and comparisons");
products["aa"].keyFeatures.push("k-means and hierarchical clustering");
products["aa"].keyFeatures.push("annotation of the genes with information from primary databases and text extracted information");
products["aa"].description = "Metalife Array Analyzer provides a computational and graphical environment for storing, preprocessing, analyzing, clustering and saving results from DNA microarray experiments. The program can organize and analyze the data in a number of different ways dedicated to helping researchers design and analyze expression profiling experiments to generate high-quality, statistically sound, and biologically interesting results.
\
Clusters of genes can be analyzed with respect to common features and common concepts among subsets of genes. Time courses can be mapped on pathways. Hidden correlations within and between sets of experimental data can be automatically analyzed in minimal time. The need to manually revise annotations, extensively scan literature to uncover complex correlations in an unmanageable amount of data is dramatically reduced.
\
Micro-arrays are revolutionizing the drug development. This technology helps in cancer research. For example, studying the different expressions of genes in tumor and normal cells may lead to new discoveries of drugs that are targeting the molecular characteristics of the cancer rather than focusing on the conventional cytotoxic agents.
\
Different than the common applications for analysis of array data, Metalife Array Analyzer combines both conventional mathematical, statistical clustering algorithms and clustering based on classified annotated information.
\
It profits strongly from the information content of the metalife knowledgebase. The information is derived both from integrated databases and extracted from text and is used to automatically annotate biological objects.
\
This enriched body of information is subject to Metalife’s advanced classification. The ontology based multi-dimensional classification builds the framework for a new method of functional clustering. Due to the fast functional analysis, statistical parameters can be adjusted in an iterative process to optimize results and improve the quality of analyses.";
products["aa"].pic = "../common/images/single_product/screenshots/array_analyzer.jpg";
products["aa"].icon = "../common/images/single_product/icons/array_analyzer.gif";
/*Semantic Search - SS*/
products["ss"] = new Object();
products["ss"].toc = "mas_toc";
products["ss"].title = "../common/images/single_product/titles/semantic_search.gif";
products["ss"].location = "Metalife Trinity > Analyzer > Semantic Search";
products["ss"].keyFeatures = new Array();
products["ss"].keyFeatures.push("integrates broad array of biological entities from multiple proved data sources");
products["ss"].keyFeatures.push("common search gateway suitable for performing simple quests as well as sophisticated inquiries");
products["ss"].keyFeatures.push("elaborate inference machinery for automated conclusions");
products["ss"].keyFeatures.push("natural language and diagrammatical expressive output");
products["ss"].description = "Metalife Semantic Search integrates multiple data sources: from protein and nucleotide sequence databases, through classification and nomenclature ones, through molecule structure ones, to highly specialized databases. These include UniProt, PhenomicDB, GenBank, Entrez Gene, GO, PDB, NCBI Taxonomy, Pfam, Enzyme, and RefSeq.
All biological entities from these databases are integrated into a single semantically organized Knowledge Base. This allows searching for connections and relationships between biological objects from different levels of organization of living matter by using an extended biologically oriented ontology.
The key component of the Metalife Semantic Search system is the inferencing of new data interrelations. The underlying rule based inference acts as knowledge generator, meaning that new relationships are established based on a priori defined biological rules. This process might be iterated – relations generated by one rule are automatically subjected as an input of another rule-based iteration.
The semantic data organization, the biologically oriented ontology and the inference machinery all together provide a new approach to the users’ search activities. One does not obtain a set of different entries from distinct databases but could examine a semantic network which brings and visualizes together related biological entities from different sources. This facilitates and makes the search more efficient, as well as reveals new relations through the inference.
Metalife Semantic Search provides an integrative approach to search for biological objects' characteristics and find or infer information of interest such as what is their function in relation to other entities.";
products["ss"].pic = "../common/images/single_product/screenshots/semantic_search.jpg";
products["ss"].icon = "../common/images/single_product/icons/1x1.gif";
}