Proposes Pathway Lasso for sparse mediation pathway estimation with high dimensional mediators.
problem Estimating sparse mediation pathways with high dimensional mediators in scientific studies.
method Sparse mediation model using Pathway Lasso penalty for high dimensional mediators, with a fast ADMM-type algorithm.
result Higher pathway selection accuracy and lower estimation bias than competing methods.
Paper uses machine learning to identify key pathways for c-di-GMP in bacterial genomes.
problem Understanding pathways essential for c-di-GMP in bacterial cellulose production.
method Applied Lasso and Random Forests for feature selection and modeling gene count data.
result Bacterial chemotaxis is identified as the most essential pathway for c-di-GMP encoding domains.
A new method speeds up overlapping group lasso computations.
problem Time-consuming optimization of overlapping group lasso on large-scale problems.
method Non-overlapping statistical approximation to overlapping group lasso.
result The proposed penalty is statistically equivalent to overlapping group lasso.
Method infers multi-layer networks from gene expression data.
problem Inference of multi-level networks from gene expression data.
method Extension of latent graphical lasso method leveraging group structure.
result Efficacy in retrieving multi-layer network structure from synthetic data.
Exclusive Group Lasso improves feature selection in correlated biological data.
problem Correlated features hinder Lasso performance in biological classification problems.
method Proposes and solves the exclusive group Lasso, combining stability selection and random group allocation.
result Exclusive Group Lasso outperforms Lasso in comprehensive selection of informative features.
Develops a model for RNA-seq data clustering.
problem Challenges in clustering RNA-seq count data with variable selection.
method Sparse negative binomial mixture model with lasso or fused lasso regularization.
result Superior performance in clustering accuracy, feature selection, and biological interpretation.
A new method for sparse regression using principal components.
problem Wide data with many features and few observations.
method Combines lasso (ℓ1) sparsity with quadratic penalty towards principal components. result Powerful feature selection and group-wise shrinkage.
A new model identifies genetic risk factors using gene-level priors.
problem Identifying genetic risk factors from nucleotide-level genetic variants.
method Sparse Group Lasso with Group-level Graph structure (SGLGG) model.
result SGLGG effectively identifies phenotype-associated risk SNPs.
PKB method uses pathway information for cancer sample classification.
problem Cancer genomic data's high dimensionality and limited sample sizes.
method Pathway-based Kernel Boosting (PKB) method integrating gene pathway information for sample classification.
result PKB method outperforms other methods and identifies relevant pathways.
PKB framework boosts genomic data analysis by integrating pathway knowledge.
problem Boosting discovery power and connecting new findings with biological mechanisms in genomic data.
method Pathway-based Kernel Boosting (PKB) framework integrating clinical and pathway information for prediction of various outcomes.
result PKB substantially outperforms other methods in predicting drug response and cancer survival.
Bayesian approach infers signaling pathways from data.
problem Accurately understand cellular regulation processes.
method Dynamic Bayesian Network structure estimation using Markov Chain Monte Carlo.
result Efficient sampling of sparse graphs improves inference.
Bayesian model identifies cancer pathways using genomic data.
problem Identifying altered pathways associated with specific cancer types.
method Bayesian semi-nonnegative tri-matrix factorization incorporating biological prior knowledge.
result Pathways identified can be used as prognostic biomarkers.
PUMA interprets metabolomics data to predict pathway activity and assign chemical identities.
problem Interpreting metabolomics data to determine biochemical pathway activities.
method Generative probabilistic modeling using stochastic sampling.
result PUMA predicts pathway activity and assigns chemical identities to metabolites.
Machine learning identifies key metabolic control circuits in bacterial pathways.
problem Identifying regulated metabolic pathways in bacteria.
method Machine learning approach analyzing multi-omics data.
result Identification of E. coli Glycolysis regulatory circuits.
Crowdsourced reinforcement learning optimizes knee replacement pathway, reducing costs.
problem Optimizing the sequential decision process for knee replacement surgery.
method Reinforcement learning, value iteration, state compression, kernel representation, cross validation.
result Optimized policy reduces overall cost by 7% and excessive cost premium by 33%.
We categorise coherent band (aka nullification) pathways between knots and 2-component links. Additionally, we characterise the minimal coherent band pathways (with intermediates) between any two knots or 2-component links with small crossing number. We demonstrate these band surgeries for knots and links with small cr…
CRNN discovers chemical reaction pathways from data.
problem Challenging to infer reaction pathways for complex systems.
method Neural network approach that satisfies fundamental physics laws.
result CRNN autonomously discovers reaction pathways from species concentration data.
Bayesian hypergraph inference models disease pathways from EHR data.
problem Modeling rare diseases influenced by shared risk factors.
method Bayesian hypergraph inference framework reframing multi-disease modeling.
result Interpretable disease pathways and well-calibrated uncertainty quantification.
Motivated by an important insight from neural science, we propose a new framework for understanding the success of the recently proposed "maxout" networks. The framework is based on encoding information on sparse pathways and recognizing the correct pathway at inference time. Elaborating further on this insight, we pro…
This research improves multitask learning by creating task-specific pathways.
problem Creating task-specific representations for efficient learning.
method Developed generalization bounds for learning multiple tasks over multiple pathways.
result Proves the superiority of multipath representation over traditional shallow supernets.
Variable (feature, gene, model, which we use interchangeably) selections for regression with high-dimensional BIGDATA have found many applications in bioinformatics, computational biology, image processing, and engineering. One appealing approach is the L0 regularized regression which penalizes the number of nonzero fe…
Novel method predicts drug responses using pathway information.
problem Predicting drug responses from molecular features is challenging.
method Kernelized Bayesian Matrix Factorization (KBMF) with component-wise multiple kernel learning and pathway integration.
result Quantitatively outperforms state-of-the-art methods on multiple data sets.
Developed PathInf for network inference from incomplete data.
problem Massive and non-uniformly distributed missing values in data.
method Two-stage inference model: data summarization and graph inference.
result Consistently superior performance compared to state-of-the-art methods.
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.
The paper develops methods to optimize personalized policies in complex causal pathways.
problem Optimizing policies in healthcare settings with entangled causal effects.
method Combining mediation analysis and dynamic treatment regime ideas for longitudinal data.
result Derived methods for learning high-quality policies from observational data.
New method detects and compares folding pathways of knotted proteins.
problem Understanding the function of knots in protein folding.
method Topological analysis of protein knotoid distributions and entanglement.
result Reveals unique folding pathway for shallow knotted Carbonic Anhydrases.
Fair decision-making method avoids ignoring fair pathways.
problem Learning fair decision systems in complex scenarios with sensitive attributes.
method Causal approach to disregard unfair pathways, corrects affected observations, uses for decision-making.
result Avoids ignoring fair information, widely applicable to complex scenarios.
Deep reinforcement learning method finds rare events in complex systems.
problem Computing transition pathways in high-dimensional systems.
method Formulated as a cost minimization problem, solved using DDPG with physical properties.
result Efficiently samples and computes globally optimal transition pathways.
New method makes neural networks transparent, revealing learning modes.
problem Lack of interpretability in neural networks.
method Weight pathway analysis (WPA) to decompose neural networks into subnetworks.
result Neural networks store and utilize information holographically, with linear and nonlinear learning modes.
PIMKL uses pathway knowledge to improve sample classification and provide interpretable molecular signatures.
problem Lack of reliable molecular biomarkers and poor generalization in machine learning for health care.
method Pathway Induced Multiple Kernel Learning (PIMKL) using a mixture of pathway-induced kernels optimized via Multiple Kernel Learning.
result PIMKL provides interpretable molecular signatures and stable predictions.
Deep RL finds efficient pathways for sugar to chemicals.
problem Finding efficient pathways from sugar to value-added chemicals.
method Markov decision process with deep reinforcement learning.
result Promising preliminary results in efficient biomass conversion.
Three ways synchronization in financial markets can cause contagion, using models of decision-making and oscillators.
problem Contagion in financial markets caused by synchronization of decision-making.
method Agent-based modeling, integrate-and-fire oscillators, and communication models.
result Synchronization in financial markets can lead to turbulent periods and contagion.
Simulates patient pathways to detect delayed rare disease diagnoses.
problem Delayed rare disease diagnoses in France, causing health system and patient harm.
method Probabilistic modelling of patient pathways to create an alert system.
result Alert system detects and refers wandering patients to CRMRs.
New method analyzes complex multivariate pathways in high-dimensional data.
problem High-dimensional mediation analysis of multivariate exposures, mediators, and outcomes.
method Simultaneous variable selection, indirect effect matrix estimation, and prediction of multivariate outcomes.
result Identifies biologically interpretable genetic-neural-cognitive pathways.
Bayesian method detects mesoscale structures in pathway data networks.
problem Mesoscale structures in pathway data networks are hard to detect due to dependencies between interactions.
method Bayesian approach modeling optimal partitioning and higher-order dynamics.
result Method can recover both proximity-based and role-based groupings of nodes.
ESN model helps understand climate event impacts.
problem Understanding complex climate event impacts.
method Feature importance methods for ESNs on spatio-temporal climate data.
result Characterized relationships between Mount Pinatubo eruption variables.
AsylADMM improves gossip-based learning for non-smooth objectives.
problem Efficient and robust decentralized learning on edge devices.
method Asynchronous gossip algorithm for non-smooth optimization.
result AsylADMM converges faster on non-smooth problems.
The paper proposes a method to predict the performance of data-driven algorithms using surrogate models.
problem Improving the performance prediction of data-driven knowledge discovery algorithms.
method Surrogate-assisted performance prediction using evolutionary modeling of clinical pathways.
result The proposed approach provides interpretable prediction of algorithm performance and quality.
New bounds for Lasso and Group Lasso in high dimensions derived.
problem Estimation error bounds for Lasso and Group Lasso in high-dimensional settings.
method Recent advances in high-dimensional statistics to derive new L2 estimation upper bounds.
result Bounds match optimal minimax rate for Lasso and improve over existing results for Group Lasso.
PLS-Lasso integrates dimension reduction into regression for financial index tracking.
problem Dimension reduction and regression are traditionally treated separately in multivariate data analysis.
method PLS-Lasso integrates dimension reduction directly into the regression process, presenting two formulations: PLS-Lasso-v1 and PLS-Lasso-v2.
result PLS-Lasso-v1 and PLS-Lasso-v2 outperform Lasso in financial index tracking.
Paper proposes methods to handle missing data in online RL, improving efficiency and uncertainty capture.
problem Missing data in online RL poses challenges due to the need to impute and act at each time step.
method Proposes fully online imputation ensembles and multiple imputation pathways to balance uncertainty and efficiency.
result Preliminary evidence suggests multiple imputation pathways can be a useful framework for simple and efficient online missing data RL methods.
The paper examines Adaptive Lasso and Transfer Lasso, highlighting their differences and proposing a new method.
problem Comparing and contrasting Adaptive Lasso and Transfer Lasso.
method Theoretical analysis of asymptotic properties and introduction of a new method.
result The Transfer Lasso method reduces non-asymptotic estimation errors compared to Adaptive Lasso.
RPPs improve deep learning models with soft equivariance constraints.
problem Balancing expressiveness and inductive biases in deep learning.
method Introducing Residual Pathway Priors (RPPs) to convert hard constraints into soft priors.
result RPPs enable models to learn structured solutions while retaining flexibility.
SWA selects important features from large data sets, controlling false discovery rate.
problem Feature selection in large regression data, especially scaling to big data and matching target FDR.
method Subsampling Winner algorithm using subsampling and scoring features.
result SWA controls actual FDR better than benchmark procedures and randomForest.
New proof shows weighted fused lasso has O(n^2) segments.
problem Proving the complexity of the weighted fused lasso.
method New proof showing the number of segments is O(n^2).
result The solution path of the weighted fused lasso has O(n^2) segments.
Study optimizes climate adaptation strategies for NYC.
problem Catastrophic damages from extreme weather in NYC.
method Real options analysis and extreme value theory.
result Optimal adaptation pathways identified for NYC.
Bayesian Lasso Sparse model provides sparse estimates in linear and nonlinear regression.
problem Sparse learning in regression models.
method Develops a new sparse learning model using type-II maximum likelihood procedure.
result The BLS model provides sparse estimates and is more precise, especially with noisy data.
ISOKANN learns collective variables and effective dynamics for metastable transitions.
problem Understanding metastable transitions in complex molecular systems.
method Integrates Koopman operators with neural networks to extract CVs and effective dynamics.
result Reconstructs coarse-grained kinetics and reproduces transition times across barriers.