EndToEndML: An Open-Source End-to-End Pipeline for Machine Learning Applications
Project Overview
The document discusses the innovative use of generative AI in education through the introduction of EndToEndML, an open-source, user-friendly web-based platform that democratizes access to machine learning tools for researchers, particularly in the life sciences. By eliminating the need for programming skills, this platform empowers a wider array of users to engage with complex datasets effectively. It features an intuitive graphical user interface that streamlines essential processes like data preprocessing, model training, evaluation, and visualization, making it accessible for those who may not have extensive technical backgrounds. The potential applications highlighted include drug discovery, pathogen classification, and medical diagnostics, showcasing how generative AI can enhance research capabilities in these areas. However, the document also acknowledges challenges, particularly the steep learning curve associated with existing AI libraries, which may hinder some users from fully leveraging the platform's capabilities. Overall, the findings underscore the transformative impact of generative AI in educational contexts, facilitating a more inclusive approach to scientific research and analysis.
Key Applications
Generative AI for Biomedical Data Analysis and Chatbot Development
Context: Life sciences researchers and students with limited programming knowledge seeking to analyze biological datasets, develop medical chatbots, and interpret pathology images through multimodal learning.
Implementation: A user-friendly web application that allows users to upload datasets (medical transcriptions or pathology images), configure settings, and utilize various AI models such as BERT for language processing and machine learning pipelines for image analysis. The system can generate language models, trained models, and evaluations, which are delivered to users via email.
Outcomes: Facilitates machine learning applications in biological research, enhances accessibility to AI tools, accelerates hypothesis generation from complex data, enables the development of chatbots for medical purposes, improves understanding of visual data interpretation in a medical context, and fosters collaboration.
Challenges: Initial apprehension from users unfamiliar with machine learning; complexity of integrating diverse machine learning techniques and visual data processing; ensuring user-friendliness amidst advanced functionalities; and potentially lengthy training times depending on dataset size and server performance.
Implementation Barriers
Technical Barrier
The complexity of existing machine learning libraries requires users to have specialized programming knowledge. Users may feel overwhelmed by the variety of machine learning tools and algorithms available.
Proposed Solutions: Developing a simplified interface that abstracts coding complexities and provides automated workflows for data analysis, along with creating a user-friendly interface that guides users through the selection of appropriate algorithms and data processing techniques.
Learning Curve
Life science students may find it difficult to learn machine learning concepts alongside their biological studies.
Proposed Solutions: Providing intuitive graphical interfaces that reduce the cognitive load associated with learning machine learning.
Project Team
Nisha Pillai
Researcher
Athish Ram Das
Researcher
Moses Ayoola
Researcher
Ganga Gireesan
Researcher
Bindu Nanduri
Researcher
Mahalingam Ramkumar
Researcher
Contact Information
For information about the paper, please contact the authors.
Authors: Nisha Pillai, Athish Ram Das, Moses Ayoola, Ganga Gireesan, Bindu Nanduri, Mahalingam Ramkumar
Source Publication: View Original PaperLink opens in a new window
Project Contact: Dr. Jianhua Yang
LLM Model Version: gpt-4o-mini-2024-07-18
Analysis Provider: Openai