Biological Network Inference from Microarray Data, Current Solutions, and Assessments.
Currently in bioinformatics and systems biology there is a growing interest for the analysis of associations among biological molecules at a network level. A main research in this area is represented by the inference of biological networks from experimental data. Biological network inference aims to reconstruct network of interactions (or associations) among biological molecules (e.g., genes or proteins) starting from experimental observations. The current scenario is characterized by a growing number of algorithms for the inference, while few attention has been posed on the determination of fair assessments and comparisons. Current assessments are usually based on the comparison of the algorithms using reference networks or gold standard datasets. Here we survey some selected inference algorithms and we compare current assessments. We also present a systematic listing of freely available inference and assessment tools for easy reference. Finally we outline some possible future directions of research, such as the use of a prior knowledge into the assessment process.
Department of Information Technology, North-Eastern Hill University, Shillong, India. swarup@nehu.ac.in.
Swarup
S
Roy
Department of Surgical and Medical Sciences, University of Catanzaro, Catanzaro, Italy. hguzzi@unicz.it.
Pietro Hiram
PH
Guzzi
D000465:Algorithms; D000818:Animals; D019295:Computational Biology / Q000379:methods*; D003198:Computer Simulation; D030541:Databases, Genetic; D053263:Gene Regulatory Networks; D006801:Humans; D020411:Oligonucleotide Array Sequence Analysis; D012984:Software; D064878:Web Browser
Assessment; Biological network inference; Gene Ontology; Gene regulatory network; Gold standard; Graph theory