Author

Abhishake Lahiri

Indian Institute of Chemical BiologyORCID 0009-0002-7546-9447Unclaimed ORCID profile · follow to get notified of new preprints

Biology Disciplines

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Bioinformatics· 2

Signature Assays, Organisms & Methods

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Assay:metagenomics· 2 Research Focus:benchmarking· 1 Research Focus:database / resource· 1 Approach:machine learning (classical)· 1 Approach:statistical model· 1

Frequent Co-Authors

Sandip PauliD· 2 shared Anupam GautamiD· 1 shared Daniel H. HusoniD· 1 shared Debaleena Bhowmik· 1 shared Jesmita Dhar· 1 shared Kausik BasakiD· 1 shared Rachana BanerjeeiD· 1 shared Rohit Roy Chowdhury· 1 shared

Activity by Year

2023-2023 · peak 2/yr

Co-Author Collaboration Network

10 top collaborators · 25 mutual co-author links

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2 shared papersBioinformatics

Sandip PaulORCID

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2 papers

Recent·Most cited

MACI: A machine learning-based approach to identify drug classes of antibiotic resistance genes from metagenomic data

Rohit Roy Chowdhury, Jesmita Dhar, Stephy Mol Robinson, Abhishake Lahiri, Kausik Basak, Sandip Paul, Rachana Banerjee

MACI identifies drug classes of antibiotic resistance genes from metagenomic data using machine learning, achieving high precision and recall for multidrug resistant classes.

Computers in Biology and Medicine · Oct 24, 2023Peer-reviewedBioinformatics

Microbiome Metabolome Integration Platform (MMIP): a web-based platform for microbiome and metabolome data integration and feature identification.

Anupam Gautam, Debaleena Bhowmik, Sayantani Basu, Wenhuan Zeng, Abhishake Lahiri, Daniel H. Huson, Sandip Paul

MMIP integrates microbiome and metabolome data to identify features and predict metabolite signatures from amplicon sequencing data without requiring initial metabolomic analysis.

Briefings in Bioinformatics · Sep 22, 2023Peer-reviewedBioinformatics