Talks

Word Embeddings In Mental Health (R in Medicine, 2024)

This suicide prevention workshop, developed with Veterans Affairs, aims to assist clinicians by analyzing health data to improve follow-up with at-risk patients, enable access to relevant notes, and share insights while preserving confidentiality. It covers the project overview, the Diagnostic Manual’s role, a proposed diagnosis, an analysis of suicide publications, and instructions for reproducing the work - all to leverage data and expertise for suicide prevention, especially for military and veterans.

Introducing The Organization Of A Project Git Repository

Example on project CSRP P50 (Center for Suicide Research and Prevention https://csrp.mgh.harvard.edu/)

Overall structure and collaborative development

Single Nucleotide Polymorphisms Linkage Disequilibrium Visualizations

Single nucleotide polymorphisms are the most common genetic variations in humans and genome-wide microarrays measure up to several millions. Physically close polymorphisms often exhibit correlation structures and are said to be in linkage disequilibrium when they are more correlated than randomly expected. The snplinkage package provides linkage disequilibrium visualizations by displaying correlation matrices annotated with chromosomic positions and gene names. Two types of displays are provided to focus on small or large regions, and both can be extended to combine associations results or investigate feature selection methods. This article introduces the package and illustrates the variety of correlation structures found genome-wide by focusing on 3 regions associated with autoimmune diseases: the chromosome 1 region 1p13.2 (111-115 Mbp), the chromosome 8 region 8p23.1 (7-12 Mbp) and the chromosome 6 region 6p21.3 (29-35 Mbp).

OPTICS K-Xi Density-Based Clustering

Density-based clustering methods are well adapted to the clustering of high-dimensional data and enable the discovery of core groups of various shapes despite large amounts of noise. This package provides a novel density-based cluster extraction method, OPTICS k-Xi, and a framework to compare k-Xi models using distance-based metrics to investigate datasets with unknown number of clusters. The vignette first introduces density-based algorithms with simulated datasets, then presents and evaluates the k-Xi cluster extraction method. Finally, the models comparison framework is described and experimented on 2 genetic datasets to identify groups and their discriminating features. The k-Xi algorithm is a novel OPTICS cluster extraction method that specifies directly the number of clusters and does not require fine-tuning of the steepness parameter as the OPTICS Xi method. Combined with a framework that compares models with varying parameters, the OPTICS k-Xi method can identify groups in noisy datasets with unknown number of clusters. Results on summarized genetic data of 1,200 patients are in Charlon T. (2019) https://archive-ouverte.unige.ch/unige:161795 doi:10.13097/archive-ouverte/unige:161795.