| Flux balance analysis of biological systems: applications and challenges K Raman, N Chandra Briefings in bioinformatics 10 (4), 435-449, 2009 | 512 | 2009 |
| targetTB: a target identification pipeline for Mycobacterium tuberculosis through an interactome, reactome and genome-scale structural analysis K Raman, K Yeturu, N Chandra BMC systems biology 2 (1), 109, 2008 | 290 | 2008 |
| Assessment of network module identification across complex diseases S Choobdar, ME Ahsen, J Crawford, M Tomasoni, T Fang, D Lamparter, ... Nature Methods 16 (9), 843-852, 2019 | 268* | 2019 |
| Flux balance analysis of mycolic acid pathway: targets for anti-tubercular drugs K Raman, P Rajagopalan, N Chandra PLoS computational biology 1 (5), e46, 2005 | 221 | 2005 |
| Machine Learning Applications for Mass Spectrometry-Based Metabolomics UW Liebal, ANT Phan, M Sudhakar, K Raman, LM Blank Metabolites 10 (6), 243, 2020 | 218 | 2020 |
| Construction and analysis of protein–protein interaction networks K Raman Automated experimentation 2 (1), 2, 2010 | 215 | 2010 |
| SBML Level 3: an extensible format for the exchange and reuse of biological models SM Keating, D Waltemath, M König, F Zhang, A Dräger, C Chaouiya, ... Molecular systems biology 16 (8), e9110, 2020 | 211 | 2020 |
| Mycobacterium tuberculosis interactome analysis unravels potential pathways to drug resistance K Raman, N Chandra BMC microbiology 8 (1), 234, 2008 | 116 | 2008 |
| Metagenome-wide association analysis identifies microbial determinants of post-antibiotic ecological recovery in the gut KR Chng, TS Ghosh, YH Tan, T Nandi, IR Lee, AHQ Ng, C Li, ... Nature Ecology & Evolution 4 (9), 1256-1267, 2020 | 110 | 2020 |
| The organisational structure of protein networks: revisiting the centrality–lethality hypothesis K Raman, N Damaraju, GK Joshi Systems and Synthetic Biology 8, 73-81, 2014 | 103 | 2014 |
| Critical assessment of genome-scale metabolic networks: the need for a unified standard A Ravikrishnan, K Raman Briefings in bioinformatics 16 (6), 1057-1068, 2015 | 89 | 2015 |
| The evolvability of programmable hardware K Raman, A Wagner Journal of The Royal Society Interface 8 (55), 269-281, 2011 | 74 | 2011 |
| A systems perspective of host–pathogen interactions: predicting disease outcome in tuberculosis K Raman, AG Bhat, N Chandra Molecular BioSystems 6 (3), 516-530, 2010 | 65 | 2010 |
| Fast-SL: an efficient algorithm to identify synthetic lethal sets in metabolic networks A Pratapa, S Balachandran, K Raman Bioinformatics 31 (20), 3299-3305, 2015 | 62 | 2015 |
| Deciphering the metabolic capabilities of Bifidobacteria using genome-scale metabolic models NT Devika, K Raman Scientific Reports 9 (1), 18222, 2019 | 59 | 2019 |
| A General Mechanism for the Propagation of Mutational Effects in Proteins N Rajasekaran, S Suresh, S Gopi, K Raman, AN Naganathan Biochemistry 56 (1), 294-305, 2016 | 55 | 2016 |
| Metabolome based reaction graphs of M. tuberculosis and M. leprae: a comparative network analysis KD Verkhedkar, K Raman, NR Chandra, S Vishveshwara PLoS One 2 (9), e881, 2007 | 51 | 2007 |
| Adapting Community Detection Algorithms for Disease Module Identification in Heterogeneous Biological Networks B Tripathi, S Parthasarathy, H Sinha, K Raman, B Ravindran Frontiers in genetics 10, 164, 2019 | 47 | 2019 |
| Strategies for efficient disruption of metabolism in Mycobacterium tuberculosis from network analysis K Raman, R Vashisht, N Chandra Molecular BioSystems 5 (12), 1740-1751, 2009 | 42 | 2009 |
| Network-based features enable prediction of essential genes across diverse organisms K Azhagesan, B Ravindran, K Raman PloS one 13 (12), e0208722, 2018 | 38 | 2018 |
Nagasuma ChandraIndian Institute of Science在 iisc.ac.in 的电子邮件经过验证
