Machine learning

Introduction to Linear Regression

A data scientist's first goal is to find underlying relations within the variables of a dataset. Several statistical and machine learning methods can be used to discover such relations. Once uncovered, this information can be applied to everyday problems. For example, in clinical medicine, a predictive model based on clinical data can help clinicians guide a patient's treatment by offering insights that might not have otherwise been taken into account. Simple linear regression One of the most basic methods available to [...]

Implementing a “Siamese” Neural Network with Mariana 1.0

Mariana was previously introduced in this blog by Geneviève in her May post Machine learning in life science. The Mariana codebase is currently standing on github at the third release candidate before the launch of the stable 1.0 release. This new version incorporates a large refactorization effort as well as many new features (a complete list of the changes found in the 1.0 version can be found in the changelog). I am taking this opportunity to present here a small tutorial on extending the [...]

By | 2016-11-25T09:29:10+00:00 November 7, 2016|Categories: Computer science, Data Analysis, Machine learning, Python|0 Comments

Machine learning in life science

Machine learning's popularity is increasing among bioinformaticians and biologists as it gives interesting results and has become more accessible than ever. A machine learning model can now be easily applied on a given dataset using R or Python packages. For example, the Python package Scikit-learn provides several algorithms (Random Forest, Support Vector Machine - SVM -, regression model and much more) and good documentation. Even deep machine learning (neural networks with multiple layers or convolutional networks for example) is more accessible [...]

By | 2016-11-08T09:30:05+00:00 May 18, 2016|Categories: Machine learning|0 Comments