Prediction Of The Arabidopsis Functional Interactome Based On Machine Learning And Network-driven Analysis System Of Omics Data

An advanced functional understanding of omics data is important for elucidating the design logic of physiological processes in planS and effectively controlling desired traiS in planS.We present the latest version of the predicted Arabidopsis interactome resource(PAIR)and of the gene set linkage analysis(GSLA)tool,which enable the interpretation of an observed transcriptomic change(differentially expressed genes,DEGs)in Arabidopsis with respect to iS functional impact for biological processes.PAIR v5.0 integrates functional association data between genes in multiple forms and infers 335301 putative functional interactions.GSLA relies on this high-confidence inferred functional association network to expand our perception of the functional impacS of an observed transcriptomic change.GSLA then interpreS the biological significance of observed DEGs using established biological concepS(annotation terms),describing not only the DEGs themselves,but also their potential functional impacS.This unique analytical capability can help researchers gain deeper insight into their experimental resulS and highlight prospective directions for further investigation.We demonstrate the utility of GSLA with two case studies in which GSLA uncovered how molecular evenS may have caused physiological changes through their collective functional influence on biological processes.Furthermore,we showed that typical annotation-enrichment tools were unable to produce similar insighS as PAIR/GSLA.The PAIR v5.0 inferred interactome and GSLA web tool can both be accessed at http://public.synergylab.cn/pair/.