Author

Sucheta Ghosh

Heidelberg Institute for Theoretical StudiesORCID 0000-0002-2896-4552Unclaimed 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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Approach:statistical model· 2 Approach:benchmark study· 1 Research Focus:benchmarking· 1 Research Focus:cell type annotation· 1 Research Focus:clustering· 1 Research Focus:database / resource· 1 Approach:deep learning· 1 Research Focus:dimensionality reduction· 1

Frequent Co-Authors

Bettina Heinlein· 1 shared Divyang Deep TiwariiD· 1 shared Ghadeer MobasheriD· 1 shared Henning HermjakobiD· 1 shared Karthik RamaniD· 1 shared Kieran DidiiD· 1 shared Lukrécia MertováiD· 1 shared Nils HoffmanniD· 1 shared

Activity by Year

2021-2023 · peak 1/yr

Co-Author Collaboration Network

10 top collaborators · 21 mutual co-author links

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1 shared paperBioinformatics

Bettina Heinlein

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2 papers

Recent·Most cited

BioModelsML: Building a FAIR and reproducible collection of machine learning models in life sciences and medicine for easy reuse

Divyang Deep Tiwari, Nils Hoffmann, Kieran Didi, Sumukh Deshpande, Sucheta Ghosh, Tung V. N. Nguyen, Karthik Raman, Henning Hermjakob, Rahuman S. Malik‐Sheriff

This paper introduces a protocol to create a FAIR collection of machine learning models in life sciences and medicine, aiming to improve reproducibility and reuse through community curation.

bioRxiv · May 22, 2023 · PreprintBioinformatics

Combining dictionary- and rule-based approximate entity linking with tuned BioBERT

Ghadeer Mobasher, Lukrécia Mertová, Sucheta Ghosh, Olga Krebs, Bettina Heinlein, Wolfgang Müller

The paper describes a two-stage approach combining fine-tuned BioBERT for chemical entity recognition with semantic approximate search in MeSH and PubChem for entity linking, addressing challenges in chemical text-mining.

bioRxiv · Nov 9, 2021 · PreprintBioinformatics