Multilayer network centrality to uncover molecular mediators of tissue-tissue communication.
Biological systems are naturally multilayer, with interactions spanning tissues and molecular scales. This work develops multilayer network centrality measures and studies the effect of inter-layer coupling, applying them to uncover molecular mediators of tissue-tissue communication (MultiCens) and to predict cross-tissue hormone-gene relations via balanced word embeddings.
@article{kumar2023multicens,title={MultiCens: Multilayer Network Centrality Measures to Uncover Molecular Mediators of Tissue-Tissue Communication},author={Kumar, Tarun and Sethuraman, Ramanathan and Mitra, Sanga and Ravindran, Balaraman and Narayanan, Manikandan},journal={PLOS Computational Biology},volume={19},number={4},pages={e1011022},year={2023},publisher={Public Library of Science},}
2022
Bioinformatics
Predicting Cross-tissue Hormone-Gene Relations using Balanced Word Embeddings
Aditya Jadhav, Tarun Kumar, Mohit Raghavendra, and 2 more authors
@article{jadhav2022hormone,title={Predicting Cross-tissue Hormone-Gene Relations using Balanced Word Embeddings},author={Jadhav, Aditya and Kumar, Tarun and Raghavendra, Mohit and L., Tamizhini and Narayanan, Manikandan},journal={Bioinformatics},year={2022},publisher={Oxford University Press},}
2019
J. Indian Inst. Sci.
Effect of Inter-layer Coupling on Multilayer Network Centrality Measures
Tarun Kumar, Manikandan Narayanan, and Balaraman Ravindran
@article{kumar2019interlayer,title={Effect of Inter-layer Coupling on Multilayer Network Centrality Measures},author={Kumar, Tarun and Narayanan, Manikandan and Ravindran, Balaraman},journal={Journal of the Indian Institute of Science},volume={99},pages={237--246},year={2019},publisher={Springer},}