2014/12/19 by Davide Chicco, Marco Masseroli · 1 citation
Biochemistry, Genetics and Molecular Biology · #Biomedical Text Mining and Ontologies #Bioinformatics and Genomic Networks #Gene expression and cancer classification #Suite #Computer science #Software #Software suite #Annotation #Exploit #Similarity (geometry) #Semantic similarity #Web service #Information retrieval #Key (lock) #Data mining #Artificial intelligence #World Wide Web #Programming language
paper · doi:10.1109/tcbb.2014.2382127
openalex publication_date 2014/12/19 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/30
In the computational biology community, machine learning algorithms are key instruments for many applications, including the prediction of gene-functions based upon the available biomolecular annotations. Additionally, they may also be employed to compute similarity between genes or proteins. Here, we describe and discuss a software suite we developed to implement and make publicly available some of such prediction methods and a computational technique based upon Latent Semantic Indexing (LSI), which leverages both inferred and available annotations to search for semantically similar genes. The suite consists of three components. BioAnnotationPredictor is a computational software module to predict new gene-functions based upon Singular Value Decomposition of available annotations. SimilBio is a Web module that leverages annotations available or predicted by BioAnnotationPredictor to discover similarities between genes via LSI. The suite includes also SemSim, a new Web service built upon these modules to allow accessing them programmatically. We integrated SemSim in the Bio Search Computing framework (http://www.bioinformatics.deib. polimi.it/bio-seco/seco/), where users can exploit the Search Computing technology to run multi-topic complex queries on multiple integrated Web services. Accordingly, researchers may obtain ranked answers involving the computation of the functional similarity between genes in support of biomedical knowledge discovery.