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…
Joint replacement is the most common inpatient surgical treatment in the US. We investigate the clinical pathway optimization for knee replacement, which is a sequential decision process from onset to recovery. Based on episodic claims from previous cases, we view the pathway optimization as an intelligence crowdsourci…
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.
Clinical researchers use disease progression models to understand patient status and characterize progression patterns from longitudinal health records. One approach for disease progression modeling is to describe patient status using a small number of states that represent distinctive distributions over a set of obser…
Predictive models identify patients at risk of severe COVID-19.
problem Identifying patients at risk of severe COVID-19 to ease healthcare strain.
method Machine learning on routinely collected clinical data.
result Models predict positive SARS-CoV-2 tests, hospitalizations, and critical care with high accuracy.
CoI framework models clinical feature interactions, revealing temporal dependencies and enhancing transparency.
problem Capturing latent, time-varying dependencies among clinical features in time-series data.
method Chain-of-Influence (CoI) framework constructs an explicit, time-unfolded graph of feature interactions.
result Achieves state-of-the-art predictive performance (AUROC of 0.960 on CKD progression and 0.950 on ICU mortality).
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…
Over half a million individuals are diagnosed with head and neck cancer each year worldwide. Radiotherapy is an important curative treatment for this disease, but it requires manual time consuming delineation of radio-sensitive organs at risk (OARs). This planning process can delay treatment, while also introducing int…
AI detects heart disease from ECGs with improved interpretability and performance.
problem Undiagnosed structural heart disease due to high cost and accessibility of echocardiography.
method Generalized additive model integrating clinically meaningful ECG predictors.
result Improved AUROC, AUPRC, and F1 score compared to deep-learning baselines.
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.
PWSHAP provides targeted explanations for complex models.
problem Inability of black-box models to explain targeted effects in sensitive domains.
method Augments model with DAG, uses Shapley values for causal pathway identification.
result Establishes error bounds and demonstrates resolution, interpretability, and locality.
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.
Novel method identifies proteomic risk markers for Alzheimer disease.
problem Lack of comprehensive proteomic risk markers for Alzheimer disease diagnosis.
method Deep belief network-based feature selection method using proteomic and clinical data.
result Identified an optimal subset of proteins achieving 90% accuracy in Alzheimer disease diagnosis.
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.
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 …
Aims to integrate AI and modelling for patient health forecasting.
problem Personalized, precise treatment plans for patients.
method Graph neural network (GNNs) and generative adversarial network (GANs) for probabilistic simulations.
result Demonstrated integration of molecular data for predicting physiological state evolution.
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…
Molecular profiling data (e.g., gene expression) has been used for clinical risk prediction and biomarker discovery. However, it is necessary to integrate other prior knowledge like biological pathways or gene interaction networks to improve the predictive ability and biological interpretability of biomarkers. Here, we…
Unified method for discovering biclusters and triclusters in longitudinal data.
problem High-dimensional, sparsely sampled, irregularly observed longitudinal data.
method Tri-SfSVD, a unified sparse functional Singular Value Decomposition framework.
result Identified localized structures at the subject, subject-feature, and subject-feature-time levels.
engGNN combines external and generated graphs to improve disease classification and biomarker discovery.
problem Challenges in integrating omics data due to high dimensionality and small sample sizes.
method Dual-graph framework that integrates external biological networks with data-driven generated graphs.
result engGNN outperforms state-of-the-art methods in disease classification and biomarker discovery.
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…
Complex systems may contain heterogeneous types of variables that interact in a multi-level and multi-scale manner. In this context, high-level layers may considered as groups of variables interacting in lower-level layers. This is particularly true in biology, where, for example, genes are grouped in pathways and two …
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.
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.
We introduce tools to capture the dynamics of three different pathways, in which the synchronization of human decision-making could lead to turbulent periods and contagion phenomena in financial markets. The first pathway is caused when stock market indices, seen as a set of coupled integrate-and-fire oscillators, sync…
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.
A fast method estimates group-adaptive elastic net penalties using co-data.
problem Computational inefficiency in estimating group-adaptive elastic net penalties.
method Derive low-dimensional representation of Taylor approximation for marginal likelihood and its derivative for group-adaptive ridge penalties; approximate elastic net marginal likelihood by ridge; transform ridge penalties to elastic net penalties.
result Significantly decreases computation time and outperforms other methods.
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.
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.
In this work, we developed a network inference method from incomplete data ("PathInf") , as massive and non-uniformly distributed missing values is a common challenge in practical problems. PathInf is a two-stages inference model. In the first stage, it applies a data summarization model based on maximum likelihood to …
Develops platforms to analyze social media data for human behavior and emotions.
problem Understanding human behavior and emotions from social media data.
method Self-structuring incremental machine learning, event detection, natural language processing.
result Captured salient topics and events from social media data, validated against news.
Deep learning model explains breast cancer subtypes using logistic regression.
problem Clarifying the mechanisms of breast cancer subtypes for better treatment.
method Developed a PWL model that generates custom-made logistic regression for each patient.
result The PWL model reveals genes relevant to cell cycle-related pathways.
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.
Network medicine predicts repurposable drugs for COVID-19.
problem Identifying effective drugs for SARS-CoV-2 infections quickly.
method Artificial intelligence, network diffusion, and network proximity algorithms.
result A multimodal approach combining predictions from multiple algorithms outperforms individual methods.
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.
SFCNeXt estimates brain age from small MRI datasets.
problem Efficiently estimating brain age from limited MRI data.
method Simple fully convolutional network (SFCNeXt) with SPEC and HRL.
result SFCNeXt outperforms complex models in small sample size scenarios.
Updating predictive models introduces bias, leading to miscalibration.
problem Bias introduced when updating predictive models after interventions.
method Proposed a causal framework and partially observed Markov decision process.
result Successive predictive scores may converge to undesirable outcomes.
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.
Computer-assisted synthesis planning aims to help chemists find better reaction pathways faster. Finding viable and short pathways from sugar molecules to value-added chemicals can be modeled as a retrosynthesis planning problem with a catalyst allowed. This is a crucial step in efficient biomass conversion. The tradit…
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 …