Bayesian method discovers local causal relationships among genes from gene expression data.
problem Discovering gene regulatory relationships from gene expression data.
method Bayesian approach scoring covariance structures for triplets of normally distributed variables, incorporating background knowledge as priors.
result Stable and conservative posterior probability estimates of local causal structures.
Bayesian method infers gene regulatory network structure from data.
problem Discovering local causal relationships in gene regulatory networks.
method Bayesian approach scoring covariance patterns with background priors.
result Stable and conservative posterior estimates of regulatory relationships.
NO-BEARS algorithm speeds up gene network inference from transcriptomic data.
problem Constructing accurate gene regulatory networks from transcriptomic data.
method NO-BEARS algorithm, based on NOTEARS, with new constraint and polynomial regression loss.
result Significantly reduced computational time and improved accuracy in inferring gene regulatory networks.
DASH simplifies neural networks for gene regulatory dynamics using domain knowledge.
problem Pruning neural networks for gene regulatory dynamics lacks biologically meaningful structure learning.
method DASH uses domain-specific structural information to guide network pruning, leading to sparser, better interpretable models.
result DASH outperforms general pruning methods in gene regulatory network inference, yielding deeper insights.
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.
Bayesian model learns cell types and gene networks from two data views.
problem Estimating cell types and their regulatory networks from single-cell gene expression and epigenetic data.
method Symphony Bayesian hierarchical multi-view mixture model with Variational EM inference.
result Symphony outperforms other methods in learning cell types and regulatory networks.
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.
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.
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.
Bayesian method reconstructs gene regulatory network structure from oscillating signals.
problem Reconstructing gene regulatory network structure from oscillating signals.
method Bayesian Hierarchical model using Discrete Fourier Transform.
result Method leads to substantial improvements over competing approaches when oscillatory assumption is met.
Efficiently infers gene regulatory networks from spatial data.
problem Inferring spatially-varying gene regulatory networks.
method Proposed an efficient optimization problem for SV-GMRFs.
result Solves large-scale SV-GMRF problems in minutes.
Generative model for inferring graph from time series data.
problem Generating graphs conditioned on multivariate time series data.
method Time Series Conditioned Graph Generation-Generative Adversarial Networks (TSGG-GAN).
result Demonstrates effectiveness and generalizability of TSGG-GAN on synthetic and real-world datasets.
Algorithm recovers large causal tree from small samples.
problem Determining causal structure in large gene networks.
method Algorithm that recovers tree with high accuracy under mild conditions.
result High accuracy in recovering causal tree from small samples.
Bayesian networks for gene regulatory pathways using hybrid quantum-classical ML.
problem Elucidate gene regulatory pathways from proteomics data.
method Hybrid quantum-classical machine learning framework to build Bayesian networks.
result Scalable framework for learning gene regulatory networks from proteomics data.
Graphical models help in learning the structure of dependencies among variables.
problem Estimating the underlying graph of a graphical model from data.
method Review of methods including graphical lasso, PC algorithm, and score-based search.
result Advances in structure learning for both undirected and directed graphical models.
A new method infers causal gene regulatory networks from parallel CRISPR interventions and transcriptomic data.
problem Learning causal gene regulatory networks from observational data is complicated by lack of identifiability and a combinatorial solution space.
method A continuous optimization framework that leverages observational and interventional data to infer a single causal structure, assuming a linear Structural Equation Model (SEM).
result A provably consistent estimator of the true DAG under mild assumptions.
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.…
New model predicts traits from gene expression, accounting for heterogeneity and gene networks.
problem Predicting phenotypes from gene expression data, considering heterogeneity and gene networks.
method Developed a novel model that considers heterogeneity and gene regulatory networks.
result Model performs well on prediction and provides clusters and gene regulatory networks.
Discovering genomic structure through learned neural architectures.
problem Decoding the complex, unknown structure of human genomes using deep learning.
method Developed a novel search algorithm to learn optimal architectures for genomic data.
result Architectures learned from RNA expression data predict gene regulatory structure and identify key sequence motifs.
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.
New method infers causal factors from large-scale data without full graph reconstruction.
problem Inferring causal variables from large-scale systems without full causal graph reconstruction.
method Supervised learning on simulated data using a neural network and subsampled-ensemble inference.
result Efficiently identifies causal relationships in large-scale gene regulatory networks.
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.
Reconstructing transcriptional regulatory networks is an important task in functional genomics. Data obtained from experiments that perturb genes by knockouts or RNA interference contain useful information for addressing this reconstruction problem. However, such data can be limited in size and/or are expensive to acqu…
ZICO learns DAGs from zero-inflated count data efficiently.
problem Learning network structures from zero-inflated count data.
method ZICO uses node-wise likelihoods with canonical links and a differentiable surrogate constraint for acyclicity.
result ZICO achieves superior performance and faster runtimes on simulated data.
A new method for joint eQTL mapping and gene network estimation.
problem Discovering SNP-gene relationships and gene-gene relationships in gene expression regulation.
method L1-2 regularized multi-task graphical lasso (L1-2 GLasso).
result Competitive performance on capturing true sparse structures of eQTL mapping and gene network.
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 learns complex cell networks from millions of cells.
problem Existing methods fail to scale to large datasets.
method Multi-axis Gaussian graphical models.
result Method scales to millions of cells in minutes.
New models analyze stability of gene regulation networks with coregulation.
problem Stability and structure of gene regulation networks with shared regulatory motifs.
method Developed formalism for modeling coregulation rules in RBN, analyzed stability through mean-field approach.
result Coregulation can increase network stability, especially in autoregulated multi-gene modules and hierarchical gene complexes.
Inference of gene regulatory network from expression data is a challenging task. Many methods have been developed to this purpose but a comprehensive evaluation that covers unsupervised, semi-supervised and supervised methods, and provides guidelines for their practical application, is lacking. We performed an extensiv…
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.
Components of biological systems interact with each other in order to carry out vital cell functions. Such information can be used to improve estimation and inference, and to obtain better insights into the underlying cellular mechanisms. Discovering regulatory interactions among genes is therefore an important problem…
Due to the dynamic nature of biological systems, biological networks underlying temporal process such as the development of {\it Drosophila melanogaster} can exhibit significant topological changes to facilitate dynamic regulatory functions. Thus it is essential to develop methodologies that capture the temporal evolut…
Within the description of stochastic differential equations it is argued that the existence of Boltzmann-Gibbs type distribution in economy is independent of the time reversal symmetry in econodynamics. Both power law and exponential distributions can be accommodated by it. The demonstration is based on a mathematical …
VEGN uses graph neural networks to predict disease-causing mutations from genetic variants.
problem Identifying disease-causing mutations from millions of genetic variants.
method VEGN employs a graph neural network on a heterogeneous graph of genes and variants, learning gene-gene interactions.
result VEGN outperforms existing state-of-the-art models in variant effect prediction.
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.
A standard technique for understanding underlying dependency structures among a set of variables posits a shared conditional probability distribution for the variables measured on individuals within a group. This approach is often referred to as module networks, where individuals are represented by nodes in a network, …
GO-CBED optimizes experiments for specific causal queries, improving efficiency.
problem Efficiently infer causal relationships with limited resources.
method Goal-oriented Bayesian framework that maximizes expected information gain on user-specified causal quantities.
result GO-CBED outperforms existing methods in various causal tasks, especially with limited budgets.
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…
Identifies shifts in causal mechanisms between related datasets using ANMs.
problem Estimating the full causal structure from data is challenging; focus on identifying shifts in causal mechanisms.
method Assumes nonlinear additive noise models, uses Jacobian of score function for mixture distribution to identify shifts.
result Shows applicability of the approach on synthetic and real-world data.
ABCDEFG learns causal graphs from interventional data efficiently.
problem Learning causal graphs from interventional data is challenging.
method Amortized Bayesian Causal Discovery of Extended Factor Graphs (ABCDEFG)
result Estimates a posterior distribution that identifies the true causal graph up to an equivalence class.
Many real world network problems often concern multivariate nodal attributes such as image, textual, and multi-view feature vectors on nodes, rather than simple univariate nodal attributes. The existing graph estimation methods built on Gaussian graphical models and covariance selection algorithms can not handle such d…
Biophysical models explain deep learning in gene regulation.
problem Difficulty in interpreting deep learning models in gene regulation.
method Expressed biophysical models as neural networks with explicit interpretations.
result Biophysical networks can be inferred from MPRAs.
Novel kernel-based SEMs improve edge detection in directed networks.
problem Detecting causal interactions in complex directed networks.
method Advocates nonlinear SEMs using kernels for nonlinear dependencies, proposing a convex regularized estimator with efficient optimization methods.
result Novel kernel-based approach outperforms linear SEMs in edge detection, revealing new regulatory edges.
Deep learning detects bifurcations in dynamical systems.
problem Predicting catastrophic changes in dynamical systems across sciences.
method Data-driven, physically-informed deep-learning framework for classifying dynamical regimes and characterizing bifurcation boundaries.
result Extracts topologically invariant features to detect bifurcation boundaries in unseen systems.
New method for causal discovery using peeling algorithms for various data types.
problem Challenges in causal discovery due to unmeasured confounders.
method Two peeling algorithms (bottom-up and top-down) for causal discovery with generalized structural equation models.
result Valid discovery of causal relationships and parent-child effects in diverse data types.
A very important topic in systems biology is developing statistical methods that automatically find causal relations in gene regulatory networks with no prior knowledge of causal connectivity. Many methods have been developed for time series data. However, discovery methods based on steady-state data are often necessar…
Observations consisting of measurements on relationships for pairs of objects arise in many settings, such as protein interaction and gene regulatory networks, collections of author-recipient email, and social networks. Analyzing such data with probabilisic models can be delicate because the simple exchangeability assu…