Develops probabilistic models for gene regulatory network inference.
problem Challenges in reconstructing gene regulatory networks from genome-wide data.
method Two complementary frameworks: PMF-GRN and GLM-Prior.
result Probabilistic inference refines regulatory estimates with quantified uncertainty.
Novel framework predicts cell responses to perturbations using GRNs.
problem Predicting cellular responses to perturbations for drug discovery and personalized therapeutics.
method Graph variational Bayesian causal inference framework with refined GRNs and robust estimator.
result Enhanced model performance and robust estimation of perturbation effects.
The paper develops a scalable method to infer GRNs from sparse data.
problem Inferring complex gene regulatory networks from limited and temporally sparse data.
method Bayesian optimization and kernel-based methods to construct a Gaussian Process (GP) model.
result The method efficiently searches for the topology with the highest likelihood value.
RIDS reconstructs sparse gene regulatory networks from few perturbation experiments.
problem Inference of gene regulatory networks from costly perturbation experiments.
method Robust IDentification of Sparse networks (RIDS) method using sparse optimization.
result RIDS can reconstruct GRNs from a small number of experiments, achieving high performance.
Inferring the structure of gene regulatory networks (GRN) from gene expression data has many applications, from the elucidation of complex biological processes to the identification of potential drug targets. It is however a notoriously difficult problem, for which the many existing methods reach limited accuracy. In t…
Semi-supervised methods improve GRN prediction from gene expression data.
problem Challenging task of predicting gene regulatory networks from gene expression data.
method Semi-supervised machine learning methods using SVM and RF.
result Transductive learning approach outperformed inductive learning for both organisms.
InfoSEM infers gene regulatory networks without GT labels, improving performance.
problem Inferring GRNs from gene expression data with high accuracy and avoiding biases.
method InfoSEM uses deep generative models with informative priors (textual gene embeddings).
result InfoSEM outperforms existing models by 38.5% across four datasets.
LAGE is a systematic framework developed in Java. The motivation of LAGE is to provide a scalable and parallel solution to reconstruct Gene Regulatory Networks (GRNs) from continuous gene expression data for very large amount of genes. The basic idea of our framework is motivated by the philosophy of divideand-conquer.…
The paper tackles controlling gene regulatory networks with noisy measurements and uncertain inputs.
problem Controlling gene regulatory networks with indirect measurements and uncertain inputs.
method Modeling GRNs with POBDS, transforming to a Markov Decision Process, using Gaussian processes for cost function, and applying reinforcement learning and sparsification.
result Near-optimal control strategy for infinite-horizon control of GRNs is found.
A method clusters genes in large gene regulatory networks using semi-supervised hierarchical clustering.
problem Challenging task of identifying interaction clusters in large gene regulatory networks due to data noise and inconsistency.
method SHC-DC: a semi-supervised hierarchical clustering method using deconvolved correlation matrix.
result SHC-DC discovers interaction modules enriched in various signal pathways, validating sleep's impact on interleukin levels and related pathways.
Causal methods for GRN inference from single-cell data often fail in real-world benchmarks.
problem Understanding when and why causal methods for GRN inference from single-cell data fail in real-world benchmarks.
method Introduced a controlled diagnostic framework to isolate and measure seven pathologies.
result Causal methods dominate in clean and structurally favorable regimes but fail in specific pathologies.
Graph Neural Networks (GNNs) model graph data for tasks like physics, molecular, and disease prediction.
problem Modeling graph data for various learning tasks.
method Message passing between graph nodes using GNN variants like GCN, GAT, GRN.
result GNNs have achieved breakthrough performance in deep learning tasks.
TNDE quantifies dynamic gene drivers from single-cell snapshots.
problem Reconstructing time-resolved regulatory effects in biological processes.
method Time-varying Network Driver Estimation (TNDE) using shared graph attention encoder and partial optimal transport.
result TNDE identifies stage-specific driver genes in mouse erythropoiesis.
ASCEND discovers causal relationships in multi-omics data by leveraging known hierarchical structure.
problem Causal inference in high-dimensional multi-omics data, especially when ignoring the hierarchical structure.
method Two-tiered divide-and-conquer strategy with ancestral conditioning sets.
result Achieves polynomial-time complexity and accurately recovers ancestral relationships.
New method identifies SDE drift and diffusion from temporal data.
problem Learning SDE parameters from temporal data, especially in noisy or incomplete data.
method Entropy-regularized optimal transport, APPEX algorithm.
result Can almost always recover drift and diffusion from temporal marginals.
Sparse GFA identifies disease factors in FTD subgroups.
problem Heterogeneity in neurological disorders hinders understanding and treatment.
method Sparse Group Factor Analysis (GFA) with regularised horseshoe priors.
result Identified latent disease factors differentially expressed in FTD subgroups.