Analysis of Biomedical Data and Application of Machine Learning using R

Machine learning (ML) is a branch of artificial intelligence (AI) that concentrates on creating algorithms and statistical models that allow computer systems to learn from data and make predictions or decisions. Essentially, it is a discipline where computers are trained to carry out tasks without the need for explicit programming for each individual task. Recent developments in Artificial Intelligence (AI) and Machine Learning (ML) technology have led to significant progress in predicting and identifying health emergencies, disease demographics, and immune responses, among other areas. However, there is still skepticism about the practical use and interpretation of outcomes from ML-based methods in healthcare environments, even as the adoption of these methods rapidly increases. In this workshop, we present a concise summary of machine learning techniques and algorithms, which encompass supervised, unsupervised, and reinforcement learning, complete with examples. Additionally, we explore the use of machine learning in the biomedical sector to gain insights into disease prevalence and diagnosis.

The aim is to assist bio-medical scientists and medical professionals by identifying and summarizing meaningful patterns from large datasets.

• To examine extensive datasets of medical information in order to uncover patterns and forecast disease progression, diagnosis, treatment responses, and patient outcomes.
• To expedite medical discoveries and enhance healthcare by delivering greater insights into intricate biological systems and diseases via data analysis.

This workshop intends to provide an arena for students, faculty and researchers in discussing the following topics as well as to provide hands on training.

Day 1: Introductionto ML/AI and its Foundational Principles

• Overview of Artificial Intelligence, Machine Learning, and Deep Learning.
• Supervised Leaning, Unsupervised Learning, Reinforcement learning LDA (Linear Discriminant Analysis), KNN (K-Nearest Neighbours), SVM (Support Vector Machine), Random Forest, AdaBoost (Adaptive Boosting)

Day 2: R studio and Bioconductor packages

• R and Bioconductor packages installation
• Application of Machine Learning for Biomedical Research

Day 3: Biomedical Data analysis and Machine Learning

• Machine learning prediction model via data extraction, data analysis, data visualization and data modelling
• Performance testing of the model