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.
A method to explain disease transformation using biomarker covariance matrices.
problem Understanding disease transformation from a healthy baseline.
method Modeling healthy and disease states of biomarker covariance matrices to characterize perturbations.
result Disease perturbs the biomarker covariance structure, allowing for mechanistic explanations and individual patient prognosis.
Study identifies biomarkers for lung cancer in female non-smokers.
problem Identifying prognostic biomarkers for stage III NSCLC in non-smoking females.
method Gene expression profiling and XGBoost machine learning algorithm.
result Top biomarkers validated in literature, with AUC score of 0.835.
The development of molecular signatures for the prediction of time-to-event outcomes is a methodologically challenging task in bioinformatics and biostatistics. Although there are numerous approaches for the derivation of marker combinations and their evaluation, the underlying methodology often suffers from the proble…
Study uses LLMs to create personalized treatment plans for rare gynecological tumors.
problem Suboptimal management and poor prognosis due to low incidence and heterogeneity of rare gynecological tumors.
method Developed a digital twin system using LLMs to integrate clinical and biomarker data.
result LLM-enabled digital twins efficiently model individual patient trajectories and identify potential treatment options.
New method integrates network knowledge for better clinical risk prediction and biomarker discovery.
problem Improving predictive ability and interpretability of biomarkers using molecular profiling data.
method Introduces a network-regularized sparse Logistic Regression framework with a new penalty term.
result Demonstrates improved performance in simulated and real data compared to existing methods.
PS-VAE extracts multi-parameter MRI biomarkers with uncertainty quantification.
problem Uncertainty in inverse problems limits clinical acceptance of quantitative MRI methods.
method Physics-Structured Variational Autoencoder (PS-VAE) integrating physics simulator and self-supervised learning.
result PS-VAE provides full covariance of inter-parameter correlations and accelerates multi-parametric MRI quantification.
Bayesian Cox model identifies biomarkers from multi-omics data.
problem Produce interpretable survival prognosis from multi-omics data.
method Penalized semiparametric Bayesian Cox model with graph-structured selection priors.
result Model identifies new biomarkers and improves survival prediction.
Machine learning improves glioma diagnosis and prognosis.
problem Improving glioma diagnosis and prognosis using imaging biomarkers.
method Search PubMed and MEDLINE for articles applying machine learning to high-grade glioma biomarkers.
result Machine learning enables accurate classification of glioma biomarkers.
Background: Predictive, stable and interpretable gene signatures are generally seen as an important step towards a better personalized medicine. During the last decade various methods have been proposed for that purpose. However, one important obstacle for making gene signatures a standard tool in clinics is the typica…
Deep neural networks infer multiple cancer properties from transcriptome data.
problem Limited use of biomarkers in molecular cancer pathology due to computational challenges.
method Multi-task and transfer learning architecture encoding whole transcriptome into a latent vector.
result Significantly better at predicting tissue-of-origin, disease state, and cancer type.
The study ranks biomarkers using mutual information, tackling clinical trial challenges.
problem Disentangling predictive and prognostic biomarkers in clinical trials.
method Formalizing biomarker ranking as optimizing mutual information, estimating conditional mutual information terms with efficient approximations and empirical Bayes, introducing a visualisation tool.
result Efficient methods to rank predictive and prognostic biomarkers, visualisation tool for biomarker discovery.
Paper proposes inference method for high-dimensional censored quantile regression.
problem Identifying heterogeneous effects of high-dimensional genetic biomarkers on survival outcomes.
method Combines low-dimensional model estimates based on multi-sample splittings and variable selection.
result Proposed estimator is consistent and asymptotically follows a Gaussian process.
New method detects biomarker-treatment interactions in clinical trials.
problem Detecting interactions between high-dimensional biomarkers and treatments in randomized trials.
method Two-stage penalized regression screening using ridge regression for multivariate screening.
result Ridge regression screening provides greater power than traditional methods in correlated data.
Optimizes biomarker selection for cost-effective treatment rules.
problem Incorporating multiple biomarkers in treatment selection rules can be costly and reduce model performance.
method Developed procedures for estimating linear and nonlinear combinations of biomarkers using 0-norm penalized weighted classification.
result Demonstrated the importance of feature selection and marker cost in treatment selection rules.
Motivation: Biomarker discovery from high-dimensional data is a crucial problem with enormous applications in biology and medicine. It is also extremely challenging from a statistical viewpoint, but surprisingly few studies have investigated the relative strengths and weaknesses of the plethora of existing feature sele…
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.
Neural network models improve ROC curve evaluation of biomarkers, focusing on age's role in physical activity-mortality association.
problem Improving biomarker evaluation using machine learning for complex relationships.
method Proposes neural network-based covariate-adjusted ROC modeling.
result Age has distinct effects on mortality outcomes when physical activity is measured as total activity time.
New biomarker predicts MRgFUS treatment outcome without contrast agents.
problem Inaccurate assessment of treated tissue viability after MRgFUS.
method Deep learning on noncontrast multiparametric MRI images, voxel-wise registration.
result Predicted follow-up NPV with DICE coefficient 0.71, outperforming current standard.
RIF prioritizes predictive biomarkers for precision medicine.
problem Lack of tools to select and prioritize predictive biomarkers.
method Random Interaction Forest (RIF) method.
result RIF outperformed conventional methods in various simulation scenarios and clinical trials.
Super-resolution improves MRI resolution and accuracy for biomarker assessment.
problem Inadequate SNR for accurate quantification in high-resolution MRI.
method Utilized deep learning super-resolution to maintain SNR for T2 relaxation time biomarkers while generating high-resolution images.
result Super-resolution successfully maintains high-resolution and accurate biomarkers for MRI.
Graph Neural Network identifies ASD biomarkers from fMRI data.
problem Finding biomarkers for Autism Spectrum Disorder (ASD).
method Graph Neural Network (GNN) for analyzing task-fMRI brain networks, 2-stage pipeline to interpret feature importance.
result GNN achieves high accuracy in identifying ASD biomarkers and reveals their association with social behaviors.
DKT transfers biomarker information between neurodegenerative diseases.
problem Estimating biomarker trajectories in rare neurodegenerative diseases with limited data.
method DKT is a joint-disease generative model that transfers biomarker progressions from common neurodegenerative diseases to rare ones.
result DKT estimates plausible multimodal biomarker trajectories in rare diseases like PCA using only unimodal data.
New method identifies predictive biomarkers for subgroup analysis.
problem Identifying predictive biomarkers from large covariates.
method Generalized penalized regression with overlapped group penalties.
result Asymptotically consistent method for sparse, interpretable models.
This paper compares feature selection methods for biomarker discovery in toxicant-treated fish.
problem Choosing the most suitable method for biomarker discovery in toxicant exposure studies.
method Three feature selection methods: SAM, mRMR, and GeoDE are compared.
result Different methods perform better in different cases, requiring dataset-specific decisions.
This research uses cooperative game theory to interpret deep learning models for ASD biomarker discovery.
problem Understanding image features used by deep learning models for ASD biomarker discovery.
method Shapley value explanation (SVE) from cooperative game theory applied to deep learning models with graph structure optimization.
result SVE provides more accurate biomarker importance than traditional methods.
Method predicts biomarker trajectories with uncertainty bands for Alzheimer's disease.
problem Uncertainty in biomarker predictions poses risks in clinical deployment.
method Conformal prediction for randomly-timed biomarker trajectories.
result Conformal bands achieve desired coverage and are tighter than baseline.
Proposes ConRad model for lung cancer classification using radiomics and interpretable machine learning.
problem Lack of interpretability in deep neural networks for cancer diagnosis.
method Integration of radiomics and DNN-predicted biomarkers in interpretable classifiers (ConRad).
result ConRad models outperform CNNs in five-fold cross-validation.
EBM uses high-dimensional imaging biomarkers to improve dementia progression estimation.
problem Current EBMs only use scalar biomarkers, limiting accuracy from cross-sectional data.
method Proposes nDEBM, a novel method using semi-supervised SVM on voxel-wise imaging biomarkers.
result nDEBM outperforms state-of-the-art EBM methods using regional volume biomarkers.
Novel framework predicts brain biomarker trajectories with superior performance.
problem Challenges in estimating longitudinal brain biomarker trajectories due to variability, inconsistencies, and irregular measurements.
method Personalized deep kernel regression with Adaptive Shrinkage Estimation.
result Superior predictive performance compared to state-of-the-art models.
New methods use network data to find genetic indicators for diseases.
problem Finding genetic indicators for diseases from large, complex data.
method Integrating network data to select features from whole-genome data.
result Methods can identify genetic indicators that work together.
PR-GNN identifies salient brain regions for ASD biomarkers.
problem Identifying brain regions associated with neurological disorders.
method Pooling Regularized Graph Neural Network (PR-GNN) with novel salient region selection.
result PR-GNN outperforms baseline methods in ASD classification accuracy.
New method uses SHAP for biomarker identification in CATE models.
problem Identifying predictive biomarkers from observational data.
method Surrogate estimation approach using SHAP values for CATE meta-learners.
result SHAP accurately identifies biomarkers in high-dimensional data.
New biomarkers for autism detected from R-fMRI data.
problem Challenges in extracting functional biomarkers from multi-site R-fMRI data for complex neuropsychiatric disorders.
method Developed pipelines to extract participant-specific connectomes from functionally-defined brain areas, compared across participants, and predicted neuropsychiatric status.
result 67% prediction accuracy on ABIDE dataset, significantly better than previous results.
A method for biomarker selection using aggregated data under data protection constraints.
problem Manual data exchange and limited data calls hinder joint analyses of clinical biomarkers.
method Distributed multivariate regression modeling for automatic variable selection.
result The heuristic variant reduces data calls from over 10 to 3, making manual data releases feasible.
The paper develops Gaussian Process models for Alzheimer's disease biomarkers.
problem Designing accurate biomarkers for Alzheimer's disease diagnosis.
method Gaussian Processes, Multiple Kernel Learning, Automatic Relevance Determination.
result Gaussian Process models outperform Support Vector Machines in classification performance.
AFTNet uses a network-constrained Weibull model for biomarker discovery.
problem Discovering biomarkers from survival data with correlated predictors.
method Survival analysis method based on Weibull AFT model, incorporating network constraints and penalized likelihood for variable selection.
result Theoretical consistency and efficient algorithm for AFTNet estimator validated on synthetic and real data.
The study discovers digital biomarkers for Parkinson's Disease using optimized transitions and emissions in HSMM.
problem Identifying digital biomarkers for Parkinson's Disease.
method Proposed a Hidden Semi-Markov Model (HSMM) to model Parkinson's Disease patients' step and stride periodic cycles.
result The HSMM allows for more informative characterization of Parkinson's Disease patients/controls by considering the duration spent in each state.
Transformers improve Alzheimer's disease progression prediction by accounting for irregular biomarker histories.
problem Difficult prediction of medium-horizon Alzheimer's disease progression due to tied clinical scores and irregular biomarker observations.
method Developed a residual gap-aware transformer that combines statistical reference with transformer-based residual learning.
result The proposed model reduces mean error and improves prediction-observation correlation compared to baseline models.
Wide and deep neural network predicts Alzheimer's progression from shape and clinical data.
problem Predicting Alzheimer's disease progression from shape and clinical data.
method Fused anatomical shape and tabular clinical data in a neural network, employing survival analysis loss.
result The model outperforms shape and clinical models individually.
Proposes LSTM algorithm for robust Alzheimer's disease progression modeling with missing data.
problem Challenges in modeling disease progression using incomplete longitudinal data.
method Utilizes Long Short-Term Memory (LSTM) networks for Alzheimer's disease progression modeling with a generalized training rule for handling missing data.
result Achieves significantly lower mean absolute error (MAE) than alternatives with p < 0.05.
A new framework models multi-state events and biomarkers.
problem Limited representation of complex multi-state trajectories.
method General multi-state joint modeling framework.
result Accurate parameter recovery and personalized predictions.
This study interprets machine learning models to identify biomarkers for severe COVID-19 infection.
problem The black-box nature of machine learning models makes it difficult for medical researchers to understand and trust their predictions.
method The study uses permutation feature importance, Partial Dependence Plot, Individual Conditional Expectation, Accumulated Local Effects, Local Interpretable Model-agnostic Explanations, and Shapley Additive Explanation to interpret four machine learning models.
result The study identifies NTproBNP, CRP, LDH, LYM, leukocytes, eosinophils, and platelets as biomarkers associated with severe COVID-19 infection.
Paper uses RL to optimize daily step distribution for better health biomarkers.
problem Lack of personalized PA distribution recommendations for health biomarkers.
method Developed an offline reinforcement learning algorithm to learn optimal PA distributions.
result Learned optimal policy suggests more consistent daily steps and tailored recommendations.
NetBiTE predicts drug sensitivity and identifies biomarkers in cancer.
problem Predicting drug sensitivity and identifying biomarkers in cancer.
method NetBiTE combines prior knowledge and gene expression data using a biased tree ensemble approach.
result NetBiTE outperforms RF in predicting IC50 drug sensitivity for drugs targeting membrane receptor pathways.
ROOFS helps researchers select robust biomarker features from complex data.
problem Challenges in feature selection for biomarker discovery and clinical models.
method ROOFS is a Python package that benchmarks multiple feature selection methods on user data.
result ROOFS identifies a filter method as optimal for identifying predictors of lung cancer resistance.
SBI improves uncertainty analysis of cardiovascular biomarkers.
problem Mapping waveforms back to plausible physiological parameters.
method Simulation-based inference (SBI) for statistical inference.
result Posterior distributions provide a multi-dimensional representation of uncertainty.
Ensemble model predicts AD progression from CN status with high accuracy.
problem Early prediction of clinical progression from cognitively normal to mild cognitive impairment or Alzheimer's disease.
method Ensemble survival analysis combining penalized Cox regression, advanced survival models, and aggregation techniques.
result Ensemble model achieved peak C-index of 0.907 and integrated time-dependent AUC of 0.904, outperforming baseline models.