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bioconda-recipes

Conda recipes for the bioconda channel.

book_notebooks_spanish

Notebooks associated to the book (Spanish translation)

ciberer_poster_2024

citeseq_autoencoder

Integrative analysis of single-cell multi-omics data using deep learning

dmaps

Placeholder for common development on hipathia and disease maps.

drexml

(DRExM³L) Drug REpurposing using eXplainable Machine Learning and Mechanistic Models of signal transduction

drexml-retinitis

dualaae-ehr

Dual Adversarial Autoencoder for Generating Set-valued Sequences

genomics_shortcuts

geometric_ml

This repository contains code for applying Riemannian geometry in machine learning.

gin

Graph Intervention Networks (GIN) (NeurIPS 2021)

gp_extras

Additional kernels that can be used with scikit-learn's Gaussian Process module

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.

keras_informed_layer

luad_lusc_tcga_comparison

Scripts and Data associated with our publication on genomic signatures to discriminate LUAD and LUSC lung cancer types using TCGA data

malmstin

Unveiling the Druggable Landscape: A Multimodal Approach (MALMSTIN)

maqc_2022

MAQC 2022 slides

matlab_aux_functions

Repository of MATLAB helper functions

mime

MiME Repository

msiprestige15

neuralcr

numpy-ml

Machine learning, in numpy

ovarian_cancer_project

pandas-head-to-tail

pybel

A pure Python package for parsing, validating, compiling, and converting biological knowledge graphs encoded in BEL

rapids-single-cell-examples

Examples of single-cell genomic analysis accelerated with RAPIDS

rnn-vae

Hybrid prediction system. Use VAE to get latent states of a time-dependent system. Use RNN (Reservoir Computer) to evolve latents. VAE translates to predictions

robustness_informed

scibet

A portable and fast single cell type identifier

scmatch

scMatch: a single-cell gene expression profile annotation tool using reference datasets