PosMed (Positional Medline)

Category Genomics>Genetic Data Analysis/Tools

Abstract PosMed (Positional Medline) is an artificial intelligent web-based system that can be used to prioritize candidate genes for positional cloning studies.

PosMed was created by employing the manufacturer's original database search engine General and Rapid Association Study Engine (GRASE).

GRASE uses an inferential process similar to an artificial Neural Network (NN) comprising documental neurons (or ‘documentrons’) that represent each document contained in databases such as MEDLINE and the Online Mendelian Inheritance in Man (OMIM) database.

Given a user-specified query, PosMed initially performs a 'full-text search' of each documentron in the first-layer of artificial neurons and then calculates the statistical significance of the connections between the hit documentrons and the second-layer of artificial neurons representing each gene.

When a chromosomal interval(s) is specified, PosMed explores the second-layer and third-layer of artificial neurons representing genes within the chromosomal interval by evaluating the combined significance of the connections from the hit documentrons to the genes.

PosMed is, therefore, an advanced tool that immediately ranks the candidate genes by connecting phenotypic keywords to genes through connections representing Not only gene-gene interactions but also other 'biological interactions' (e.g. metabolite-gene, mutant mouse- gene, drug-gene, disease-gene and protein-protein interactions) and ortholog data.

By utilizing orthologous connections, PosMed facilitates the ranking of human genes based on evidence found in other model species such as the mouse.

Currently, PosMed, an 'artificial super-brain' that has learned and contains a vast amount of 'biological knowledge' ranging from genomes to phenomes (or ‘omic space’), supports the prioritization of 'positional candidate genes' in human, mouse, rat and Arabidopsis thaliana species.

General usage of PosMed --

To use this system, users need to input a species, a keyword and 'genome version' and additionally select the genomic interval.

Users can download the candidate genes together with the relevant gene annotation information using the ‘download rank list’ button.

Users can also select the ‘expert mode’ in the ‘All Hits’ tab to enable detailed retrieval.

With this expert mode, users can check all the direct and inferential paths of the PosMed search as well as the number of hit genes.

Moreover, users can change the threshold of the P-value to increase or decrease the number of genes shown.

Clicking on the gene name reveals supporting evidence for each candidate gene.

Data Sources --

Currently, PosMed uses more than 17 million documents.

For inference-type searches, the manufacturer employs document sets including MEDLINE (title, abstract and MeSH term), genome annotation, phenome information, protein-protein interaction, co-expression, drugs and metabolite records.

PosMed Ranking --

In order to prioritize the positional candidate genes, PosMed first calculates the 'statistical significance' between the user's keyword and each gene.

Then, a 2 x 2 contingency table is generated and this consists of the following:

1) The number of documents that match with both the keyword and the gene;

2) The number of documents that match the keyword but Not the gene;

3) The number of documents that match the gene but Not the keyword;

4) The number of documents that match Neither the keyword Nor the gene.

The P-value is then computed using Fisher's exact test.

For an inference search, the manufacturer statistically evaluates the relevance between gene1 and gene2 using the above-mentioned Fisher's exact test.

To treat biological data such as protein-protein interaction, all biological data are described as sentences (e.g. protein A interacts with protein B) and they are stored as document sets in PosMed.

System Requirements

Web-based.

Manufacturer

Manufacturer Web Site PosMed

Price Contact manufacturer.

G6G Abstract Number 20432

G6G Manufacturer Number 104060