We study information theoretic methods for ranking biomarkers. In clinical trials there are two, closely related, types of biomarkers: predictive and prognostic, and disentangling them is a key challenge. Our first step is to phrase biomarker ranking in terms of optimizing an information theoretic quantity. This formal…
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48 results for “Biomarkers”
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.
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.
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.
Optimal biomarker combinations for treatment-selection can be derived by minimizing total burden to the population caused by the targeted disease and its treatment. However, when multiple biomarkers are present, including all in the model can be expensive and hurt model performance. To remedy this, we consider feature …
Noninvasive MR-guided focused ultrasound (MRgFUS) treatments are promising alternatives to the surgical removal of malignant tumors. A significant challenge is assessing the viability of treated tissue during and immediately after MRgFUS procedures. Current clinical assessment uses the nonperfused volume (NPV) biomarke…
Uncertainty-Calibrated Prediction of Randomly-Timed Biomarker Trajectories with Conformal Bandsstat.ML
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.
Finding the biomarkers associated with ASD is helpful for understanding the underlying roots of the disorder and can lead to earlier diagnosis and more targeted treatment. A promising approach to identify biomarkers is using Graph Neural Networks (GNNs), which can be used to analyze graph structured data, i.e. brain ne…
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.
We introduce Disease Knowledge Transfer (DKT), a novel technique for transferring biomarker information between related neurodegenerative diseases. DKT infers robust multimodal biomarker trajectories in rare neurodegenerative diseases even when only limited, unimodal data is available, by transferring information from …
Adaptive Shrinkage Estimation For Personalized Deep Kernel Regression In Modeling Brain Trajectoriescs.LG
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.
Precision medicine is becoming a focus in medical research recently, as its implementation brings values to all stakeholders in the healthcare system. Various statistical methodologies have been developed tackling problems in different aspects of this field, e.g., assessing treatment heterogeneity, identifying patient …
Obtaining magnetic resonance images (MRI) with high resolution and generating quantitative image-based biomarkers for assessing tissue biochemistry is crucial in clinical and research applications. How- ever, acquiring quantitative biomarkers requires high signal-to-noise ratio (SNR), which is at odds with high-resolut…
Discovering imaging biomarkers for autism spectrum disorder (ASD) is critical to help explain ASD and predict or monitor treatment outcomes. Toward this end, deep learning classifiers have recently been used for identifying ASD from functional magnetic resonance imaging (fMRI) with higher accuracy than traditional lear…
Event-based models (EBM) are a class of disease progression models that can be used to estimate temporal ordering of neuropathological changes from cross-sectional data. Current EBMs only handle scalar biomarkers, such as regional volumes, as inputs. However, regional aggregates are a crude summary of the underlying hi…
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.
Univariate and multivariate feature selection methods can be used for biomarker discovery in analysis of toxicant exposure. Among the univariate methods, differential expression analysis (DEA) is often applied for its simplicity and interpretability. A characteristic of methods for DEA is that they treat genes individu…
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.
A Wide and Deep Neural Network for Survival Analysis from Anatomical Shape and Tabular Clinical Datacs.LG
We introduce a wide and deep neural network for prediction of progression from patients with mild cognitive impairment to Alzheimer's disease. Information from anatomical shape and tabular clinical data (demographics, biomarkers) are fused in a single neural network. The network is invariant to shape transformations an…
The identification of predictive biomarkers from a large scale of covariates for subgroup analysis has attracted fundamental attention in medical research. In this article, we propose a generalized penalized regression method with a novel penalty function, for enforcing the hierarchy structure between the prognostic an…
Identifying measurable genetic indicators (or biomarkers) of a specific condition of a biological system is a key element of precision medicine. Indeed it allows to tailor diagnostic, prognostic and treatment choice to individual characteristics of a patient. In machine learning terms, biomarker discovery can be framed…
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.
Large Language Models-Enabled Digital Twins for Precision Medicine in Rare Gynecological Tumorscs.CL
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.
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.
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.
Resting-state functional Magnetic Resonance Imaging (R-fMRI) holds the promise to reveal functional biomarkers of neuropsychiatric disorders. However, extracting such biomarkers is challenging for complex multi-faceted neuropatholo-gies, such as autism spectrum disorders. Large multi-site datasets increase sample sizes…
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.
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.
Alzheimer's disease is a major cause of dementia. Its diagnosis requires accurate biomarkers that are sensitive to disease stages. In this respect, we regard probabilistic classification as a method of designing a probabilistic biomarker for disease staging. Probabilistic biomarkers naturally support the interpretation…
Disease progression modeling (DPM) using longitudinal data is a challenging machine learning task. Existing DPM algorithms neglect temporal dependencies among measurements, make parametric assumptions about biomarker trajectories, do not model multiple biomarkers jointly, and need an alignment of subjects' trajectories…
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.
Dual-edge spatial Jacobian image graph for interpretable diabetic retinopathy grading
problem Automated diabetic retinopathy grading from color fundus photographs
method Dual-edge spatial-Jacobian image graph
result 0.8076 accuracy, 0.8312 quadratic weighted kappa, 0.5915 macro-F1, 0.9330 adjacent-grade accuracy
Flexible variable selection handles missing data for better biomarker panels.
problem Identifying relevant features from incomplete data sets.
method Nonparametric variable selection combined with multiple imputation.
result Improved biomarker panels with higher classification and variable selection performance.
Maintaining good cardiac function for as long as possible is a major concern for healthcare systems worldwide and there is much interest in learning more about the impact of different risk factors on cardiac health. The aim of this study is to analyze the impact of systolic blood pressure (SBP) on cardiac function whil…
Aim: To review how machine learning (ML) is applied to imaging biomarkers in neuro-oncology, in particular for diagnosis, prognosis, and treatment response monitoring. Materials and Methods: The PubMed and MEDLINE databases were searched for articles published before September 2018 using relevant search terms. The sear…
Robust cancer screening model using pre-trained ensembles for biomarkers.
problem Detecting early-stage cancer, especially in hard-to-diagnose cases like pancreatic cancer.
method Meta-trained Hyperfast model for robust classification, combined with ensembling of XGBoost and LightGBM.
result Achieved highest AUC of 0.9929 and robust performance on imbalanced datasets.
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.
The discovery of clinical biomarkers requires large patient cohorts and is aided by a pooled data approach across institutions. In many countries, data protection constraints, especially in the clinical environment, forbid the exchange of individual-level data between different research institutes, impeding the conduct…
Due to the rapid innovation of technology and the desire to find and employ biomarkers for neurodegenerative disease, high-dimensional data classification problems are routinely encountered in neuroimaging studies. To avoid over-fitting and to explore relationships between disease and potential biomarkers, feature lear…
Finite mixture model is an important branch of clustering methods and can be applied on data sets with mixed types of variables. However, challenges exist in its applications. First, it typically relies on the EM algorithm which could be sensitive to the choice of initial values. Second, biomarkers subject to limits of…
MSB framework improves survival prediction in immunotherapy patients with missing data.
problem High dimensionality and blockwise missingness in multimodal clinical data.
method MSB is a late-fusion framework that independently models modality-specific features before aggregating predictions via cross-validated stacking.
result MSB outperformed baseline algorithms in predicting progression-free survival in lung cancer patients.
RobKMR improves robustness in multi-omics data analysis for osteoporosis biomarker discovery.
problem Sensitivity to adversarial outliers and lack of comprehensive multi-omics data integration.
method RobKMR, a non-linear M-estimator-based approach using robust kernel centered Gram matrix and robust score test.
result Selected biomarkers (DKK1, MTND5, FASTKD2) significantly bond with four drugs for osteoporosis.
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…
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…
Recently, the intervention calculus when the DAG is absent (IDA) method was developed to estimate lower bounds of causal effects from observational high-dimensional data. Originally it was introduced to assess the effect of baseline biomarkers which do not vary over time. However, in many clinical settings, measurement…