Pinned Repositories
drexml
(DRExM³L) Drug REpurposing using eXplainable Machine Learning and Mechanistic Models of signal transduction
loucerac's Repositories
loucerac/drexml
(DRExM³L) Drug REpurposing using eXplainable Machine Learning and Mechanistic Models of signal transduction
loucerac/bioconda-recipes
Conda recipes for the bioconda channel.
loucerac/book_notebooks_spanish
Notebooks associated to the book (Spanish translation)
loucerac/ciberer_poster_2024
loucerac/drexml-retinitis
loucerac/DualAAE-EHR
Dual Adversarial Autoencoder for Generating Set-valued Sequences
loucerac/genomics_shortcuts
loucerac/geometric_ml
This repository contains code for applying Riemannian geometry in machine learning.
loucerac/GIN
Graph Intervention Networks (GIN) (NeurIPS 2021)
loucerac/High-Dimensional-Model-Explanations-An-Axiomatic-Approach
Complex black-box machine learning models are regularly used in critical decision-making domains. This has given rise to several calls for algorithmic explainability. Many explanation algorithms proposed in literature assign importance to each feature individually. However, such explanations fail to capture the joint effects of sets of features. Indeed, few works so far formally analyze high dimensional model explanations. In this paper, we propose a novel high dimension model explanation method that captures the joint effect of feature subsets. We propose a new axiomatization for a generalization of the Banzhaf index; our method can also be thought of as an approximation of a black-box model by a higher-order polynomial. In other words, this work justifies the use of the generalized Banzhaf index as a model explanation by showing that it uniquely satisfies a set of natural desiderata and that it is the optimal local approximation of a black-box model. Our empirical evaluation of our measure highlights how it manages to capture desirable behavior, whereas other measures that do not satisfy our axioms behave in an unpredictable manner.
loucerac/keras_informed_layer
loucerac/malmstin
Unveiling the Druggable Landscape: A Multimodal Approach (MALMSTIN)
loucerac/maqc_2022
MAQC 2022 slides
loucerac/mime
MiME Repository
loucerac/msiprestige15
loucerac/numpy-ml
Machine learning, in numpy
loucerac/Ovarian_Cancer_Project
loucerac/PSO-FTS
MATLAB Implementation of "Fuzzy time series forecasting based on proportions of intervals and particle swarm optimization techniques"
loucerac/pybel
A pure Python package for parsing, validating, compiling, and converting biological knowledge graphs encoded in BEL
loucerac/pykan
Kolmogorov Arnold Networks
loucerac/rapids-single-cell-examples
Examples of single-cell genomic analysis accelerated with RAPIDS
loucerac/robustness_informed
loucerac/scibet
A portable and fast single cell type identifier
loucerac/scMatch
scMatch: a single-cell gene expression profile annotation tool using reference datasets
loucerac/SCSA
SCSA: cell type annotation for single-cell RNA-seq data
loucerac/shap
A game theoretic approach to explain the output of any machine learning model.
loucerac/sptest
loucerac/staged-recipes
A place to submit conda recipes before they become fully fledged conda-forge feedstocks
loucerac/TeachingDataScience
Course notes for Data Science related topics, prepared in LaTeX
loucerac/TumorType-WGS
Classifying tumor types based on Whole Genome Sequencing (WGS) data