Course on Life Science and Health Care

In this module we will be discussing the CRISP-ML (Q), it is a research methodology used in the life sciences to integrate statistical and machine learning methods with clinical reasoning in order to improve the accuracy of medical diagnoses and treatments. The goal of CRISP-MLQ is to develop quantitative models that can assist healthcare professionals in making more informed and efficient decisions, while also taking into account the uncertainties and biases inherent in medical data.

This module covers the essentials of Exploratory Data Analysis (EDA) and Data Preprocessing in the context of Life Sciences and Healthcare data. The goal is to familiarize participants with techniques for cleaning, transforming, and preparing data for analysis in order to obtain meaningful insights and inform decision making.

This module explores the application of K-means algorithm, a popular unsupervised learning technique, in the Life Sciences and Healthcare domain. Participants will learn how to use K-means for data clustering, gain hands-on experience in implementing the algorithm, and understand its potential for solving real-world problems in the field.

This module focuses on the K-Nearest Neighbors (KNN) algorithm, a popular supervised learning method. The module covers how KNN works, its implementation in the Life Sciences and Healthcare domain, and its applications for solving various problems in the field, such as diagnosis and prognosis. Participants will gain hands-on experience in using KNN algorithms for data analysis and prediction

In this module, the working principle of Decision Tree algorithms for supervised learning in Life Sciences and Healthcare is explored, with a focus on TNBC relapse prediction. Participants will gain a thorough understanding of how Decision Trees operate and their practical applications, through hands-on work with a real-world TNBC relapse prediction use case

This module focuses on the working principle and application of Linear Regression, a supervised learning technique, in Life Sciences and Healthcare. A simple use case of predicting Adipose tissue using waist circumference is used to demonstrate the implementation and potential of Linear Regression in the field. Participants will gain hands-on experience in using Linear Regression for data analysis and prediction.

This module discusses the usage of Multilayer Perceptrons (MLP) in Life Sciences and healthcare, through the exploration of a practical use case. Participants will learn about MLP, its working principle, and how it can be applied to solve real-world problems in the field, and gain hands-on experience in using MLP for data analysis and prediction.

This module examines the application of Natural Language Processing (NLP) in Life Sciences and Healthcare, using the use case of sentiment analysis on drugs. Participants will learn about NLP, its potential and challenges in the field, and gain hands-on experience in using NLP techniques for sentiment analysis on drug-related data.