Cell metabolism is critical for cancer cell transformation and progression. In this study, we have developed a novel method, named Met-express, that integrates a cancer gene co-expression network with the metabolic network to predict key enzyme-coding genes and metabolites in cancer cell metabolism. Met-express successfully identified a group of key enzyme-coding genes and metabolites in lung, leukemia, and breast cancers. Literature reviews suggest that approximately 33–53% of the predicted genes are either known or suggested anti-cancer drug targets, while 22% of the predicted metabolites are known or high-potential drug compounds in therapeutic use. Furthermore, experimental validations prove that 90% of the selected genes and 70% of metabolites demonstrate the significant anti-cancer phenotypes in cancer cells, implying that they may play important roles in cancer metabolism. Therefore, Met-express is a powerful tool for uncovering novel therapeutic biomarkers.Keywords: cancer; drug targets; gene co-expression network; metabolic network; metabolism; network integration;

Classical algorithms aiming at identifying biological pathways significantly related to studying conditions frequently reduced pathways to gene sets, with an obvious ignorance of the constitutive non-equivalence of various genes within a defined pathway. We here designed a network-based method to determine such non-equivalence in terms of gene weights. The gene weights determined are biologically consistent and robust to network perturbations. By integrating the gene weights into the classical gene set analysis, with a subsequent correction for the “over-counting” bias associated with multi-subunit proteins, we have developed a novel gene-weighed pathway analysis approach, as implemented in an R package called “Gene Associaqtion Network-based Pathway Analysis” (GANPA). Through analysis of several microarray datasets, including the p53 dataset, asthma dataset and three breast cancer datasets, we demonstrated that our approach is biologically reliable and reproducible, and therefore helpful for microarray data interpretation and hypothesis generation.

•GoFDR predicts GO functions using sequence alignment prepared from PSI-BLAST.•GoFDR identifies the Functional Discriminating Residues for each target GO.•GoFDR applies score-probability table to convert raw scores to probabilities.•GoFDR ranked one of the best methods in CAFA2 experiment.In this study, we developed a method named GoFDR for predicting Gene Ontology (GO)-based protein functions. The input for GoFDR is simply a query sequence-based multiple sequence alignment (MSA) produced by PSI-BLAST. For each GO term annotated to the sequences in the MSA, GoFDR identifies a number of functionally discriminating residues (FDRs) specific to the GO term, and scores the query sequence using a position specific scoring matrix (PSSM) constructed for the FDRs. The raw score is then converted into a probability score according to a score-to-probability table prepared from training sequences. GoFDR outperformed three sequence-based methods for predicting GO functions in a benchmark of 18,520 sequences. In addition, GoFDR was ranked one of the top methods according to the preliminary evaluation report released by the 2nd Critical Assessment of Function Annotation (CAFA2) project. Finally, we applied GoFDR to the complete human proteome sequences, and showed that the predictions made by GoFDR with high confidence significantly expanded current annotations of human proteome. As such, GoFDR is of great value not only for annotating protein functions in newly sequenced genomes, but also for characterizing the function of proteins of interest.