JqVicky
- PhD @ MIT. LIDS, EECS Dept. - Undergrad @ PKU. Stats, Math Dept. - Interests: causality x sequential decision making in bio.
Cambridge
JqVicky's Stars
apple/ml-heart-rate-models
wallet-maker/cytopus
Single cell omics biology annotations
cellarium-ai/cellarium-ml
Distributed single-cell data analysis.
uhlerlab/actlearn_optint
Active learning for optimal intervention design in causal models
robertness/causalvae
rguo12/awesome-causality-algorithms
An index of algorithms for learning causality with data
RelationRx/pyrelational
pyrelational is a python active learning library for rapidly implementing active learning pipelines from data management, model development (and Bayesian approximation), to creating novel active learning strategies.
jax-md/jax-md
Differentiable, Hardware Accelerated, Molecular Dynamics
microsoft/BioGPT
pyro-ppl/pyro
Deep universal probabilistic programming with Python and PyTorch
scverse/scvi-tools
Deep probabilistic analysis of single-cell and spatial omics data
calico/scnym
Semi-supervised adversarial neural networks for classification of single cell transcriptomics data
theislab/sc-pert
Models and datasets for perturbational single-cell omics
eberharf/cfl
iaconogi/bigSCale2
Framework for clustering, phenotyping, pseudotiming and inferring gene regulatory networks from single cell data
tschaffter/genenetweaver
GeneNetWeaver (GNW) is an intuitive Java application developed for the generation of in silico benchmarks and the identification of systematic errors of network inference algorithms.
larslorch/dibs
DiBS: Differentiable Bayesian Structure Learning, NeurIPS 2021
juangamella/abcd
Code to reproduce the results comparing ABCD to A-ICP in the paper "Active Invariant Causal Prediction: Experiment Selection through Stability", by Juan L Gamella and Christina Heinze-Deml.
juangamella/aicp
Code to reproduce the experimental results from the paper "Active Invariant Causal Prediction: Experiment Selection Through Stability", by Juan L Gamella and Christina Heinze-Deml.
cantinilab/OT-scOmics
This Python package will allow you to replicate the experiments from our research on applying Optimal Transport as a similarity metric in between single-cell omics data.
google/neural-tangents
Fast and Easy Infinite Neural Networks in Python
genedisco/genedisco
GeneDisco is a benchmark suite for evaluating active learning algorithms for experimental design in drug discovery.
nicola-decao/power_spherical
Pytorch implementation of the Power Spherical distribution
aristoteleo/dynamo-release
Inclusive model of expression dynamics with conventional or metabolic labeling based scRNA-seq / multiomics, vector field reconstruction and differential geometry analyses
facebookresearch/CPA
The Compositional Perturbation Autoencoder (CPA) is a deep generative framework to learn effects of perturbations at the single-cell level. CPA performs OOD predictions of unseen combinations of drugs, learns interpretable embeddings, estimates dose-response curves, and provides uncertainty estimates.
cmu-phil/tetrad
Repository for the Tetrad Project, www.phil.cmu.edu/tetrad.
csquires/causal-rep-learning-reading-group
py-why/causal-learn
Causal Discovery in Python. It also includes (conditional) independence tests and score functions.
scverse/anndata
Annotated data.
scverse/scanpy
Single-cell analysis in Python. Scales to >1M cells.