M
Madhulika Mishra
Researcher at Indian Institute of Science
Publications - 12
Citations - 195
Madhulika Mishra is an academic researcher from Indian Institute of Science. The author has contributed to research in topics: Gene & Refrigerant. The author has an hindex of 4, co-authored 8 publications receiving 137 citations.
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Journal ArticleDOI
Computational antimicrobial peptide design and evaluation against multidrug-resistant clinical isolates of bacteria
Deepesh Nagarajan,Tushar Nagarajan,Natasha Roy,Omkar Kulkarni,Sathyabaarathi Ravichandran,Madhulika Mishra,Dipshikha Chakravortty,Nagasuma Chandra +7 more
TL;DR: It is concluded that the LSTM-based peptide design approach appears to have correctly deciphered the underlying grammar of antimicrobial peptide sequences, as demonstrated by the experimentally observed efficacy of the designed peptides.
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Meta-analysis of host response networks identifies a common core in tuberculosis.
Awanti Sambarey,Abhinandan Devaprasad,Priyanka Baloni,Madhulika Mishra,Abhilash Mohan,Priyanka Tyagi,Amit Singh,J.S. Akshata,Razia Sultana,Shashidhar Buggi,Nagasuma Chandra +10 more
TL;DR: A meta-analysis of host’s whole blood transcriptomic profiles that were integrated into a genome-scale protein–protein interaction network to generate response networks in active tuberculosis, and reports the emergence of a highly active common core in disease, showing partial reversals upon treatment.
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Structure-Based Phylogenetic Analysis of the Lipocalin Superfamily
TL;DR: The present study with 39 protein domains from the lipocalin superfamily suggests that the clusters of lipocalins obtained by structure-based phylogeny correspond well with the functional diversity.
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EpiTracer - an algorithm for identifying epicenters in condition-specific biological networks.
TL;DR: An algorithm, EpiTracer, is developed, which identifies the key proteins, or epicenters, from which a large number of changes in the protein-protein interaction (PPI) network ripple out, and a new centrality measure, ripple centrality, which measures how effectively a change at a particular node can ripple across the network.
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Identification of a co-target for enhancing efficacy of sorafenib in HCC through a quantitative modeling approach.
TL;DR: A comprehensive model of the glutathione reaction network (GSHnet), consisting of four modules and includes SFB‐induced redox stress, is reported, and ethacrynic acid (EA) is a promising candidate for repurposing as a combination therapy with SFB for HCC treatment.