AutoML enhances clinical metabolic profiling by adjusting for confounders.
problem Identifying and adjusting for clinical confounders in AutoML for metabolic profiling.
method Tandem rank-accuracy measure for feature selection, residual training adjustment for confounders.
result Increased homocysteine concentration associated with long-term metformin exposure.
Clarifies the confidence interval approach for bioequivalence testing.
problem Ensuring the reliability of bioequivalence testing methods.
method Clarifies the conditions under which a 100(1-2α)% confidence interval yields a size-α test.
result A 100(1-2α)% confidence interval approach for bioequivalence testing yields a size-α test only when the two one-sided tests are 'equal-tailed'.
A model predicts which patients can safely use a warfarin dosing algorithm.
problem Determining the optimal initial dose for warfarin patients.
method Support Vector Machines with a polynomial kernel function.
result The model increases dosing accuracy by 15% in RMSE and 17% in MAE.
AI enhances microbiology and microbiome research through machine learning.
problem Understanding microbial life and its impact on health and the environment.
method AI-driven approaches including machine learning and deep learning.
result Transformative role in enhancing microbial life understanding.
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.
Novel process model for metabolomics data analysis.
problem Analyzing complex metabolomics data.
method Data-driven and hypothesis-driven data mining approaches using various techniques.
result Demonstrated applicability and strengths of MeKDDaM model.
Machine learning detects subtle glucose changes for early diabetes diagnosis.
problem Challenging early-stage diabetes diagnosis due to subtle glucose changes.
method Applied machine learning to synthetic glucose profiles generated by a biophysical model.
result High accuracy (above 85%) in detecting insulin resistance using various neural networks.
Bayesian analysis uncovers flux couplings in metabolic networks.
problem Uncertainty and unrealistic assumptions in traditional flux analysis methods.
method Introduces Bayesian metabolic flux analysis to model reactions probabilistically and infer flux distributions.
result Reveals informative flux couplings and more unobserved fluxes in metabolic networks.
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.
Develops PageRank for directed hypergraphs using metabolic network.
problem Lack of directed hypergraph datasets for PageRank algorithm.
method Developed PageRank algorithm for directed hypergraphs and applied it to metabolic network.
result Successfully applied novel PageRank algorithm to metabolic network.
In a classic paper Zeeman introduced the k-twist spin of a knot K and showed that the exterior of a twist spin fibers over S^1. In particular this result shows that the knot K # -K is doubly slice. In this paper we give a quick proof of Zeeman's result. The k-twist spin of K also gives rise to two metabolizers for K # …
New framework predicts 5-year glucose values with missing data.
problem Significant missing data in longitudinal glucose studies.
method Reproducing Kernel Hilbert Spaces (RKHS) with missing responses analysis.
result Identifies new factors affecting long-term glucose evolution.
Causal ML predicts treatment outcomes, aiding personalized medicine.
problem Predicting individualized treatment effects for personalized medicine.
method Flexible, data-driven methods using causal inference with clinical trial and real-world data.
result Causal ML allows for estimating individualized treatment effects.
Paper develops a method to predict cancer patient survival using molecular profiles.
problem Accurately predicting cancer patient survival with complex survival-molecular profile relationships.
method Kernel Cox partially linear regression with a novel regularized garrotized kernel machine (RegGKM) method.
result The proposed method outperforms other methods in predicting survival accuracy.
Method predicts NAFLD risk with high accuracy and distribution-free coverage guarantees.
problem Insufficient population-level screening tools for NAFLD.
method Gradient-boosted decision trees with conformal prediction.
result Method achieves AUROC of 0.912 internally and 0.891 externally, superior to other models.
It is known that the linking form on the 2-cover of slice knots has a metabolizer. We show that several weaker conditions, or some other conditions related to sliceness, do not imply the existence of a metabolizer. We then show how the Rudolph-Bennequin inequality can be used indirectly to prove that some knots are not…
A derivative of an algebraically slice knot K is an oriented link disjointly embedded in a Seifert surface of K such that its homology class forms a basis for a metabolizer H of K. We show that for a genus three algebraically slice knot K, the set $\{ \barμ_{\{γ_1,γ_2,γ_3\}}(123) - \barμ_{\{γ'_1,γ'_2,γ'_3\}}(…
Study identifies subphenotypes of pediatric sepsis to improve ML predictive performance.
problem Enhance machine learning predictive performance in pediatric sepsis.
method Latent profile analysis of clinical data to identify subphenotypes, followed by ML experiments.
result Improved predictive performance of ML models targeting specific subphenotypes of pediatric sepsis.
Study reveals biases in facial landmark detection methods for dementia patients.
problem Challenges in facial landmark detection for older adults with dementia.
method Evaluation of seven facial landmark detection methods on frontal, profile, and various face regions.
result Significant performance differences between dementia patients and non-patients, and biases across face regions.
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.
Automatically extracts phenotypes from cancer clinical notes for genetic studies.
problem Lack of structured patient representations in EHRs.
method Clustering of medical terms and sentences in clinical notes.
result 341 significant associations between clinical features and somatic mutations.
Poisson variational autoencoders introduce a metabolic cost term that penalizes high baseline activity.
problem Energy constraints in computation.
method Poisson variational autoencoders with a Kullback-Leibler divergence term proportional to firing rates.
result Poisson variational autoencoders introduce a metabolic cost term that penalizes high baseline activity.
A new algorithm discovers causal factors between T2DM and bone mineral density.
problem Discovering causal factors between T2DM and bone mineral density from clinical data.
method Prior-Knowledge-driven local Causal structure Learning (PKCL) algorithm.
result PKCL achieves more reliable results without long-standing medical experiments.
Deep learning improves MRI analysis of MSK disorders.
problem Accurate and rapid analysis of musculoskeletal disorders from MRI scans.
method Convolutional neural networks (CNN) for automatic classification of knee abnormalities.
result Multi-view deep learning showed promising performance in classifying MSK abnormalities.
Deep featurization improves ADMET prediction accuracy.
problem Predicting ADMET properties to reduce clinical trial failures.
method Learning features from explicit molecular graphs using graph convolutions.
result Achieved unprecedented accuracy in ADMET property prediction.
For n >1, if the Seifert form of a knotted 2n-1 sphere K in S^{2n+1} has a metabolizer, then the knot is slice. Casson and Gordon proved that this is false in dimension three (n = 1). However, in the three dimensional case it is true that if the metabolizer has a basis represented by a strongly slice link then K is sli…
Framework integrates brain connectivity data for clinical predictions.
problem Predicting clinical outcomes from brain connectivity data.
method Structurally-regularized Dynamic Dictionary Learning (sr-DDL) and LSTM-ANN block.
result Framework outperforms state-of-the-art approaches in clinical outcome prediction.
Deep learning improves tumor type classification accuracy.
problem Classifying cancer types based on DNA mutations is challenging.
method Deep transfer learning and fine-tuning of gene expression data.
result Significantly improved tumor type classification accuracy (78.3%) using DNA point mutations.
Liver transplant patients have a 25% chance of developing diabetes within 5 years.
problem Predicting new onset diabetes after liver transplant to improve patient care.
method Comparison of time-to-event prediction models and classifiers, including regularized Cox proportional-hazards model.
result Regularized Cox proportional-hazards model achieved high Concordance Index of 0.863.
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.
Unsupervised method selects genes for tumor subtype discovery.
problem High-dimensional tumor gene expression data with noisy variables and heterogeneity.
method Autoencoders for latent space learning, Multiple Kernel Learning for feature selection, clustering.
result Lower redundancy and better clustering performance compared to benchmarks.
Deep Bayesian neural networks improve somatic variant calling accuracy.
problem Improving accuracy in pinpointing somatic variants from next-gen sequencing data.
method Deep Bayesian Recurrent Neural Networks (RNNs) for somatic variant calling.
result Deep Bayesian RNNs provide more reliable confidence intervals for variant calls.
Retina-VAE models macular disease spectrum using clinical data.
problem Representing the spectrum of macular diseases clinically.
method Variational autoencoder (VAE) model trained on patient profiles.
result Latent vectors cluster into 14 subtypes, suggesting treatment responses.
Lattices embeddability determined by correction terms.
problem Embeddability of nonunimodular definite lattices.
method Using Elkies' theorem and lattice correction terms.
result Embeddability of lattices is determined by correction terms.
C-kNN-LSH identifies similar patient histories for causal inference in longitudinal data.
problem Estimating causal effects from longitudinal trajectories with high-dimensional confounding.
method C-kNN-LSH uses locality-sensitive hashing to find clinical twins and estimate treatment effects.
result C-kNN-LSH outperforms existing methods in capturing recovery heterogeneity and estimating policy values.
T-LSTM autoencoder improves chronic kidney disease patient representation.
problem Improving latent representations from irregularly sampled clinical data.
method Time-Aware Long Short-Term Memory Autoencoder.
result Significant improvements in learnt representations on synthetic and real datasets.
Machine learning detects NASH patients from medical claims data.
problem Detecting undiagnosed NASH patients for screening and management.
method Gradient-boosted decision trees trained on administrative medical claims data.
result Model precision for NASH detection is significantly higher than NASH incidence.
Study compares atom representations in graph neural networks for molecular properties.
problem Incorrect attribution of results in molecular property prediction due to varying atom features.
method Evaluated multiple atom representations on free energy, solubility, and metabolic stability predictions.
result Different atom representations can lead to varying predictive performance in graph neural networks.
Broad learning integrates diverse healthcare data for diagnostics and precision medicine.
problem Integrating various types of healthcare data for better diagnostics and personalized medicine.
method Fusing multi-view data including scalar, tensor, graph, and sequence data for knowledge discovery and machine learning tasks.
result Accurate user profiles and brain connectivity patterns can be created for improved diagnostics and personalized medicine.
Cancer survival prediction is an active area of research that can help prevent unnecessary therapies and improve patient's quality of life. Gene expression profiling is being widely used in cancer studies to discover informative biomarkers that aid predict different clinical endpoint prediction. We use multiple modalit…
The paper corrects a proof and extends a theorem about linking pairings in 4-manifolds.
problem Linking pairings in 4-manifolds and their properties.
method Analyzes the embedding of multiple copies of a 4-manifold in a compact 4-manifold and examines the resulting linking pairings.
result The linking pairing on the boundary of a 4-manifold is split metabolic, generalizing Hantzsche's theorem.
Method predicts ODX scores for breast cancer patients based on clinical data.
problem Predicting ODX scores for breast cancer patients to aid decision-making.
method Distributional random forest approach using 9 clinico-pathological characteristics.
result Correctly predicted 92% of low risk and 40.2% of high risk patients.
This work uses Sylvester normalizing flows for more accurate metabolite quantification in MRS.
problem Challenges in accurate metabolite quantification in MRS due to spectral overlap, low SNR, and artifacts.
method Bayesian inference framework with physics-informed Sylvester normalizing flows.
result Accurate metabolite quantification, well-calibrated uncertainties, and insights into parameter correlations and multi-modal distributions.
OmiVAE combines variational autoencoders and a classification network to classify multi-omics data.
problem Classifying samples from high-dimensional multi-omics data.
method OmiVAE integrates variational autoencoders and a classification network to extract features and classify samples.
result OmiVAE achieved an average classification accuracy of 97.49% across 33 tumour types and normal samples.
Deep learning models accurately recognize and estimate physical activity types and energy expenditure from wrist accelerometer data.
problem Rigorous evaluation of wrist-worn accelerometers for assessing physical activity across the lifespan.
method Built deep learning networks to extract spatial and temporal representations from time-series data, recognizing physical activity types and estimating energy expenditure.
result Deep learning models achieved high performance: F1 scores of 0.82, 0.81, and 95 for sedentary, locomotor, and lifestyle activities, respectively; root mean square error of 1.1 for EE estimation.
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…
Computes the group of link homotopy classes of 2-spheres in 4-space.
problem Computing the group of link homotopy classes of link maps of 2-spheres into 4-space.
method Geometric constructions and algebraic duals of immersed Whitney disks.
result The group is free abelian, generated by specific constructions and detected by invariants.
ODBAE detects complex phenotypes in biological data.
problem Challenges in identifying complex phenotypes from high-dimensional biological data.
method ODBAE (Outlier Detection using Balanced Autoencoders) identifies influential and high leverage points in latent relationships among multiple physiological parameters.
result ODBAE reveals novel metabolism-related genes and uncovers coordinated abnormalities across metabolic indicators.