Pack: Pathway to Phenotype using Text Mining

Description:

This file contains a list of each pathway identified from day 7 post infection and linked to the Tir1 QTL. With each pathway is a list of terms that are common to both pathway and phenotype corpora. These terms were ranked accoring to their enrichement scores. The higher the score, the more significant the term is in relation to correlating the pathway with the African trypanosomiasis resistance phenotype.

Description:

This file contains all the abstracts for pathways found to be differentially expressed at day 7 post infection and intersect the Tir1 QTL region, from the African Trypanosomiasis project.

Each pathway is listed as ">> [Pathway Name]", together with a PubMed identifier, date, and abstract for each article. Each pathway has been restricted to 500 abstracts, and is given in the date range 31/12/2007 to 01/01/2009. Note, some pathways do not have any abstracts available due to these restrictions. The date these results were gathered was 25/02/2009.

A MeSH tag was appended to each pathway name, when conducting a seacrh through PubMed: "Metabolic Networks and Pathways"[MeSH Terms].

Description:

This file contains a count of each phenotype term extracted from corpus of phenotype abstracts. Each value represents the number of articles in MEDLINE the term appears. The use of this file is to calculate a cosine vector score for correlating a given concept (e.g. pathway or gene) with a phenotype.

Description:

This file contains the number of articles available in MEDLINE, through PubMed, at the time of performing these data analyses. The date of identifying these publications was 25/02/2009.

Description:

This file contains a count of each phenotype term extracted from corpus of phenotype abstracts. Each value represents the number of articles in the phenotype corpus the term appears. The use of this file is to calculate a cosine vector score for correlating a given concept (e.g. pathway or gene) with a phenotype.

Description:

This file contains a list of all terms extracted from the phenotype corpus, relating to African trypanosomiasis resistance in the mouse model.

These terms were extracted using the following service: http://gopubmed4.biotec.tu-dresden.de/GoPubMedTermGenerationService/services/GoPubMedTermGeneration?wsdl

These terms represent the concept profile for the phenotype.

Description:

This file contains a list of published abstracts from MEDLINE, that are related to the African Trypanosomiasis resistance phentoype in the mouse. The term used in the PubMed search was: trypanosom* AND (tolerance OR resistance) .

The workflow limited the date of the search using PubMed between 31/12/2005 to 01/01/2009, and was restricted to 500 abstracts.

Description:

This excel file contains a list of genes linked to the resistance to African trypanosomiasis in the mouse. Genes from the Tir1 QTL were used in a search through PubMed. The results were then correlated to the trypanosomiasis resistance phenotype. The higher the score (and ranking) the more related to the phenotype the gene is likely to be. This is based on the co-occurrence of terms within the gene and phentoype corpora.

Description:

This excel file contains a list of all pathways found to be differentially expressed at day 7 post infection in the trypanosomiasis resistance phenotype, which contain genes in the Tir1 QTL.

The pathways in this file have been ranked according to the scores obtained after calculating a cosine vector value against the trypanosomiasis resistance phenotype. The higher the score, the more closely linked to a phentype a given pathway is.

This allows each pathway to be ranked giving biologists a means of triaging candidate pathways to test in the laboratory.

Description:

This workflow counts the number of articles in the pubmed database in which each term occurs, and identifies the total number of articles in the entire PubMed database. It also identified the total number of articles within pubmed so that a term enrichment score may be calculated.

The workflow also takes in a document containing abstracts that are related to a particular phenotype. Scientiifc terms are then extracted from this text and given a weighting according to the number of terms that appear in the document. The higher the value the better the score. This is given as: X = log((a / b) / (c / d))

where: a = number of occurnaces of individual terms in phenotype corpus b = number of abstracts in entire phenotype corpus c = number of occurnaces of individual terms in entire pubmed d = number of articles in entire pubmed

Once this has been created, the pathways obtained from the QTL and microarray pathway analysis workflows are analysed. The documents from a search of each pathway in pubmed are merged into a single document of pathway abstracts. The (unweighted) phenotype terms are then searched in the pathways corpus. This will determine if the phenotype term is listed with the given pathway. The higher the value the better the score. Each term is then assigned a weight as: Y = log((e / f) / (c /d))

where: a = number of occurnaces of individual terms in pathway corpus b = number of abstracts in pathway corpus (per pathway) c = number of occurnaces of individual terms in entire pubmed d = number of articles in entire pubmed

The weighted terms are then given a link score. This is the total of: X + Y. This gives the link between the pathway and the phenotype a score / significance value. The higher the score the more "appropriate/interesting" the link between the pathway and the phenotype.

The terms are also ranked according to the number of pathways which have been given a weight. This is calculated as: W = Sum( X + Y). The higher the value the better the score.

Description:

This workflow calculates the cosine vector space between two sets of corpora. The workflow then removes any null values from the output.

The result is a cosine vector score between 0 and 1, showing the significance of any links between one concept (e.g. pathway) to another (e.g. phenotype).

A score of 0 means there is no or an undetermined correlation between the two concepts. A score approaching 1 represents positive correlation.

Description:

This workflow takes in a document containg text and removes any non-ascii characters. The cleaned text is then sent to a service in Dresden, to extract all scientific terms. These terms represent a concept profile for the input concpet. Any null values are also removed.