Authors: Nisha Pillai, Athish Ram Das, Moses Ayoola, Ganga Gireesan, Bindu Nanduri, Mahalingam Ramkumar

Published on: March 27, 2024

Impact Score: 7.8

Arxiv code: Arxiv:2403.18203

Summary

• What is new: An open-source, user-friendly interface for AI models in the life sciences that doesn’t require programming skills for complex biological data analysis.
• Why this is important: The difficulty life science scientists face in applying innovative AI due to the need for computing language understanding.
• What the research proposes: A web-based end-to-end pipeline allowing for preprocessing, training, evaluating, and visualizing ML models without coding expertise.
• Results: The library successfully aids in recognizing, classifying, clustering, and predicting various multi-modal datasets for applications in drug discovery, pathogen classification, and medical diagnostics.

Technical Details

Technological frameworks used: Web-based pipeline integrating both traditional machine learning and deep neural networks

Models used: Machine learning and deep neural networks

Data used: Multi-modal, multi-sensor datasets including images, languages, and one-dimensional numerical data

Potential Impact

Bioinformatics, pharmaceutical companies, healthcare diagnostics

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