This study presents a novel approach leveraging machine learning techniques to analyze epigenomic and transcription factor binding patterns for the identification of key genes, including non-coding genes, associated with ...

Chemsules introduces a revolutionary approach to carcinogenicity assessment, anchored by the precision-driven Metabokiller machine learning model. Catering to a diverse user base, including researchers, scientists, industry ...

In the realm of critical care medicine, the continuous generation of vital signs time series data from Intensive Care Units (ICUs) serves as a pivotal element for clinicians in evaluating patient prognostics and accessing ...

Advancement in the field of metagenomic research have highlighted the role of microbiomes can play ecologically and clinically contexts. So, our proposed platform will provide metagenomic researchers with a friendly user ...

Recognizing subtle and implied forms of hate speech is convoluted. Fine-tuning pre-trained language models (PLMs) to generate an explanation for an incoming implicit statement has become an active area of research. Moreover, ...

Generating unit tests is a crucial undertaking in software development, demanding substantial time and e↵ort from programmers. The advent of Large Language Models (LLMs) introduces a novel avenue for unit test script ...

Intensive Care Units (ICUs) collect vast amounts of data, including patient vitals, often recorded as time series data. This data can be analyzed to extract meaningful data, which can aid in timely intervention, improving ...

This study seeks to forecast microbial colonization in the human gut and oral microbiomes by examining various factors, including diet, host genetics, and environmental influences. Through the utilization of sophisticated ...

The project introduces the development and evaluation of machine learning models incorporating both unsupervised learning clustering and supervised learning classification and regression techniques. These models aim to ...