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Updated: Jun 13, 2026

Generating the Transcriptional Regulation View of Transcriptomic Features for Prediction Task and Dark Biomarker Detection on Small Datasets
Published on: March 1, 2024
Integrating transcriptomics and gene-level interpretable probabilistic Tsetlin Machine reveals elevated pancreatic
Subhradyuti Basu1, Abhipsa Dash1, Deepjyoti Kalita1
1Department of Biotechnology and Medical Engineering, National Institute of Technology, Rourkela, 769008, Odisha, India.
Abstract:
Type 2 diabetes (T2D) is a well-established metabolic risk factor for pancreatic cancer; however, the transcriptomic mechanisms linking these conditions and their utility for patient-level risk stratification remain incompletely understood. In this paper, a novel, interpretable risk estimation platform using interpretable, probabilistic Tsetlin machines is introduced, which can be used to perform cross-disease risk estimation from transcriptomic crosstalk data of diabetic patients towards pancreatic adenocarcinoma. The Tsetlin machine provides interpretability by highlighting crucial genes through clause support counts. This will enable increased screening for early detection of cancers and also enable personalised therapeutics for patients. To the best of our knowledge, this is the first interpretable AI-based platform that performs cross-disease risk estimation based on transcriptomic crosstalk between diabetes and pancreatic adenocarcinoma.
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