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Shaika Chowdhury on Muck Rack

Shaika Chowdhury

As seen in: BioRxiv, medRxiv, Texas Medical Center
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Articles

Stratifying Heart Failure Patients With Graph Neural Network and Transformer Using Electronic Health Records to Optimize Drug Response Prediction

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By Shaika Chowdhury
| Texas Medical Center
Objectives: Heart failure (HF) impacts millions of patients worldwide, yet the variability in treatment responses remains a major challenge for healthcare professionals. The current treatment strategies, largely derived from population based evidence, often fail to consider the unique characteristics of individual patients, resulting in suboptimal outcomes.
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SensitiveCancerGPT: Leveraging Generative Large Language Model on Structured Omics Data to Optimize Drug Sensitivity Prediction

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By Shaika Chowdhury, Sivaraman Rajaganapathy, Lichao Sun, Liewei Wang
| BioRxiv
Abstract The fast accumulation of vast pharmacogenomics data of cancer cell lines provide unprecedented opportunities for drug sensitivity prediction (DSP), a crucial prerequisite for the advancement of precision oncology. Recently, Generative Large Language Models (LLM) have demonstrated performance and generalization prowess across diverse tasks in the field of natural language processing (NLP).
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SensitiveCancerGPT: Leveraging Generative Large Language Model on Structured Omics Data to Optimize Drug Sensitivity Prediction

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By Shaika Chowdhury, Sivaraman Rajaganapathy, Lichao Sun, Liewei Wang
| BioRxiv
Abstract The fast accumulation of vast pharmacogenomics data of cancer cell lines provide unprecedented opportunities for drug sensitivity prediction (DSP), a crucial prerequisite for the advancement of precision oncology. Recently, Generative Large Language Models (LLM) have demonstrated performance and generalization prowess across diverse tasks in the field of natural language processing (NLP).
Open in Who Shared Issue with article?
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