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.
RAF model explains neural networks' dual rule learning and fact memorization.
problem Understanding how neural networks learn rules and memorize facts simultaneously.
method Introduces the Rules-and-Facts (RAF) model to bridge generalization and memorization.
result Characterizes conditions for simultaneous rule learning and fact memorization in neural networks.
RAFs Ensemble improves neural network uncertainty quantification.
problem Inaccurate predictions in out-of-distribution data.
method Proposes RAFs Ensemble, using random activation functions in neural networks.
result RAFs Ensemble outperforms state-of-the-art methods in uncertainty quantification.
We introduce a tensor-based clustering method to extract sparse, low-dimensional structure from high-dimensional, multi-indexed datasets. This framework is designed to enable detection of clusters of data in the presence of structural requirements which we encode as algebraic constraints in a linear program. Our cluste…
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.
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.
The analysis of cancer genomic data has long suffered "the curse of dimensionality". Sample sizes for most cancer genomic studies are a few hundreds at most while there are tens of thousands of genomic features studied. Various methods have been proposed to leverage prior biological knowledge, such as pathways, to more…
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.
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.
In many scientific studies, it becomes increasingly important to delineate the causal pathways through a large number of mediators, such as genetic and brain mediators. Structural equation modeling (SEM) is a popular technique to estimate the pathway effects, commonly expressed as products of coefficients. However, it …
Identifying altered pathways that are associated with specific cancer types can potentially bring a significant impact on cancer patient treatment. Accurate identification of such key altered pathways information can be used to develop novel therapeutic agents as well as to understand the molecular mechanisms of variou…
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…
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
A key goal of computational personalized medicine is to systematically utilize genomic and other molecular features of samples to predict drug responses for a previously unseen sample. Such predictions are valuable for developing hypotheses for selecting therapies tailored for individual patients. This is especially va…
We consider the problem of learning fair decision systems in complex scenarios in which a sensitive attribute might affect the decision along both fair and unfair pathways. We introduce a causal approach to disregard effects along unfair pathways that simplifies and generalizes previous literature. Our method corrects …
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.
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.
Develops methods for GWAS of high dimensional phenotypes using summary statistics.
problem Lack of methods to model pleiotropy in multi-phenotype GWAS.
method Bayesian inference model using summary statistics, fast computation, and biologically informed priors.
result Demonstrates utility in metabolite GWAS with interpretable pathway-level inference.
spex-LVM infers interpretable latent factors from biomedical data.
problem Inability to learn sparse and interpretable hidden states.
method Factorial latent variable model with sparse priors and domain-relevant annotations.
result Robustly identifies relevant structure in RNA-seq datasets.
SENA-discrepancy-VAE interprets latent causal factors in biological pathways.
problem Interpreting latent causal factors in biological pathways.
method SENA-discrepancy-VAE, a model based on discrepancy-VAE, that produces interpretable latent causal factors.
result Sena-discrepancy-VAE achieves comparable predictive performance with non-interpretable counterparts while providing biologically meaningful causal factors.
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.
A new method uses LLMs to discover causal pathways that affect fairness in machine learning.
problem Discovering fairness-relevant causal pathways in the presence of noise and confounding.
method Hybrid LLM-guided causal discovery framework combining active learning and dynamic scoring.
result LLM-guided methods, including the proposed active, dynamically scored variant, outperform baselines in recovering fairness-relevant structure under noisy conditions.
Complex architectures of biological neural circuits, such as parallel processing pathways, has been behaviorally implicated in many cognitive studies. However, the theoretical consequences of circuit complexity on neural computation have only been explored in limited cases. Here, we introduce a mechanism by which direc…
Reliable identification of molecular biomarkers is essential for accurate patient stratification. While state-of-the-art machine learning approaches for sample classification continue to push boundaries in terms of performance, most of these methods are not able to integrate different data types and lack generalization…
We present the extention and application of a new unsupervised statistical learning technique--the Partition Decoupling Method--to gene expression data. Because it has the ability to reveal non-linear and non-convex geometries present in the data, the PDM is an improvement over typical gene expression analysis algorith…
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.
We present the Network-based Biased Tree Ensembles (NetBiTE) method for drug sensitivity prediction and drug sensitivity biomarker identification in cancer using a combination of prior knowledge and gene expression data. Our devised method consists of a biased tree ensemble that is built according to a probabilistic bi…
New methods estimate interventional effects with multiple mediators using machine learning.
problem Estimating interventional effects with multiple mediators.
method Flexible machine learning techniques for estimation, with weak convergence results for confidence intervals.
result Closed-form confidence intervals and hypothesis tests for interventional mediation effects.