Santosh Tirunagari on Muck Rack

Santosh Tirunagari

Liverpool, New York
As seen in: Nature, medRxiv
Science Fiction and Data Science. Creatively fusing, exploring, modelling, visualising and even interpreting heterogeneous datasets.
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Articles

Interpretable Machine Learning for Predicting Multiple Sclerosis Conversion from Clinically Isolated Syndrome

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By Santosh Tirunagari, David Windridge, Eden Caroline Daniel, Karan Batth
| medRxiv
The authors have declared no competing interest. No Funding I confirm all relevant ethical guidelines have been followed, and any necessary IRB and/or ethics committee approvals have been obtained. Yes The details of the IRB/oversight body that provided approval or exemption for the research described are given below: Confirmed that application has been given APPROVAL from the Natural Science REC. Middlesex University.
Open in Who Shared View Duplicates (25) Issue with article?

Human-in-the-loop approach to identify functionally important residues of proteins from literature

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By Santosh Tirunagari, Deborah Harrus, David Armstrong, Melanie Vollmar
| biorxiv.org
Abstract We present a novel system that leverages curators in the loop to develop a dataset and model for detecting residue-level functional annotations and other protein structure features from standard publication text. Our approach involves the integration of data from multiple resources, including PDBe, EuropePMC, PubMedCentral, and PubMed, combined with annotation guidelines from UniProt, while employing LitSuggest and Huggingface models as tools in the annotation process.
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Lit-OTAR Framework for Extracting Biological Evidences from Literature

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By Santosh Tirunagari, Shyamasree Saha, Aravind Venkatesan, Daniel Suveges
| biorxiv.org
Abstract The lit-OTAR framework, developed through a collaboration between Europe PMC and Open Targets, leverages deep learning to revolutionise drug discovery by extracting evidence from scientific literature for drug target identification and validation.
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