A flowchart depicting the work.

(A) Step 1: obtaining a mitochondrial proteins inventory utilizing machine learning classification. (B) Step 2: constructing the FLN by integrating 11 genomic features including protein-protein interaction, domain-domain interaction, shared domains, genomic context, genetic interaction, phenotypic semantic similarity, co-expression, GO semantic similarity, protein expression profiles, disease involvement and operon based on the Naïve bayes model. (C) Step 3: ranking the disease candidate genes utilizing the FLN and a network-based algorithm. The table on the right shows the ranking scores of the top 5 candidate genes for mitochondrial complex I deficiency.