Protein complexes are known to play a major role in controlling cellular activity in a living being. Identifying complexesfrom raw protein–protein interactions (PPIs) is an important area of research. Earlier work has been limited mostly to yeastand a few other model organisms. Such protein complex identification methods, when applied to large human PPIs oftengive poor performance. We introduce a novel method called ComFiR to detect such protein complexes and further rankdiseased complexes based on a query disease. We have shown that it has better performance in identifying proteincomplexes from human PPI data. This method is evaluated in terms of positive predictive value, sensitivity and accuracy.We have introduced a ranking approach and showed its application on Alzheimer’s disease.

A gene co-expression network (CEN) is of biological interest, since co-expressed genes share commonfunctions and biological processes or pathways. Finding relationships among modules can reveal inter-modularpreservation, and similarity in transcriptome, functional, and biological behaviors among modules of the sameor two different datasets. There is no method which explores the one-to-one relationships and one-to-manyrelationships among modules extracted from control and disease samples based on both topological andsemantic similarity using both microarray and RNA seq data. In this work, we propose a novel fusion measureto detect mapping between modules from two sets of co-expressed modules extracted from control and diseasestages of Alzheimer’s disease (AD) and Parkinson’s disease (PD) datasets. Our measure considers bothtopological and biological information of a module and is an estimation of four parameters, namely, semanticsimilarity, eigengene correlation, degree difference, and the number of common genes. We analyze the consensusmodules shared between both control and disease stages in terms of their association with diseases. Wealso validate the close associations between human and chimpanzee modules and compare with the state-ofthe-art method. Additionally, we propose two novel observations on the relationships between modules forfurther analysis.