Clustering microarray-derived gene lists through implicit literature relationships

Abstract

We have developed a novel method that uses implicit literature relationships (concepts related via shared, intermediate concepts) to cluster related genes. Genes are evaluated for implicit connections within a network of biomedical objects (other genes, ontological concepts and diseases) that are connected via their co-occurrences in Medline titles and/or abstracts. On the basis of these implicit relationships, individual gene pairs are scored using a probability-based algorithm. Scores are generated for all pairwise combinations of genes, which are then clustered based on the scores. We applied this method to a test set composed of nine functional groups with known relationships. The method scored highly for all nine groups and significantly better than a benchmark co-occurrence-based method for six groups. We then applied this method to gene sets specific to two previously defined breast tumor subtypes. Analysis of the results recapitulated known biological relationships and identified novel pathway relationships unique to each tumor subtype. We demonstrate that this method provides a valuable new means of identifying and visualizing significantly related genes within gene lists via their implicit relationships in the literature.

An integration of complementary strategies for gene-expression analysis to reveal novel therapeutic opportunities for breast cancer

“…In the example shown in Figure 2a and quantified in Figure 2b , it is evident that distinct patterns of pathway activation can be seen as a function of subtype. For example, high predicted activity of RAS, CTNNB1 (β-catenin), TP53, and HER1 pathways was associated with the Basal-like subtype, in some cases consistent with previous observations [ 22 - 24 ]. Additionally, RAS pathway activation was seen in Basal-like, HER2-enriched, and Normal-like subytpes, whereas both Luminal A and Luminal B subtypes have low predicted RAS activity.…”

Biomedical question answering using semantic relations

“…Most methods use co-occurring text words [ 23 ], often along with either additional information such as MeSH indexing or structured information from related databases such as the Gene Ontology [ 24 , 25 ]. Some systems use a thesaurus to identify concepts in text [ 26 ] or compute implicit information on the basis of terms related through co-occurrence with shared, intermediate terms [ 27 ].…”

Discovering gene functional relationships using FAUN (Feature Annotation Using Nonnegative matrix factorization)

“…The first dataset (50TG collection) is a gene document collection of 50 manually selected genes related to development, Alzheimer’s disease, and cancer biology [33]. The second dataset (BGM collection) is composed of three non-overlapping gene lists from the Biocarta, Gene Ontology and MeSH databases [52]. The third dataset (NatRev collection) is composed of five gene lists selected from Nature Reviews papers [53-57].…”