biostudies-literatureMei SNIAID NIH HHSNIMHD NIH HHSNCI NIH HHSNIGMS NIH HHS505https://www.ebi.ac.uk/biostudies/studies/S-EPMC6027805biostudies-literatureUnknown19(1)<h4>Background</h4>Bacterial invasive infection and host immune response is fundamental to the understanding of pathogen pathogenesis and the discovery of effective therapeutic drugs. However, there are very few experimental studies on the signaling cross-talks between bacteria and human host to date.<h4>Methods</h4>In this work, taking M. tuberculosis H37Rv (MTB) that is co-evolving with its human host as an example, we propose a general computational framework that exploits the known bacterial pathogen protein interaction networks in STRING database to predict pathogen-host protein interactions and their signaling cross-talks. In this framework, significant interlogs are derived from the known pathogen protein interaction networks to train a predictive l₂-regularized logistic BMC genomicsTransferring knowledge of bacterial protein interaction networks to predict pathogen targeted human genes and immune signaling pathways: a case study on M. tuberculosis.PMC6027805R01 AI106676P01 CA214091G12 MD007595P20 GM103424R01 AI101046Zhang KMei SFlemington EKfalseTransferring knowledge of bacterial protein interaction networks to predict pathogen targeted human genes and immune signaling pathways: a case study on M. tuberculosis.<h4>Background</h4>Bacterial invasive infection and host immune response is fundamental to the understanding of pathogen pathogenesis and the discovery of effective therapeutic drugs. However, there are very few experimental studies on the signaling cross-talks between bacteria and human host to date.<h4>Methods</h4>In this work, taking M. tuberculosis H37Rv (MTB) that is co-evolving with its human host as an example, we propose a general computational framework that exploits the known bacterial pathogen protein interaction networks in STRING database to predict pathogen-host protein interactions and their signaling cross-talks. In this framework, significant interlogs are derived from the known pathogen protein interaction networks to train a predictive l₂-regularized logistic 2018-01-01T00:00:00Z2018 Jun2026-05-02T17:40:05.846Z2026-04-07T18:18:48.44ZS-EPMC60278052995433010.1186/s12864-018-4873-9