Model predicts smoking events using a Hawkes process.
problem Predict smoking events to improve cessation interventions.
method Time-varying semi-parametric Hawkes process model.
result TV-SPHP achieves superior prediction performance.
On-device research index
arXiv research
A locally-built, LLM-digested index of recent arXiv papers in quant finance, geometry/topology, and statistical ML — keyword search served straight from SQLite on this machine.
169,051 papers · 148 categories
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48 results for “nicotine metabolism”
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.
Metabolic flux balance analyses are a standard tool in analysing metabolic reaction rates compatible with measurements, steady-state and the metabolic reaction network stoichiometry. Flux analysis methods commonly place unrealistic assumptions on fluxes due to the convenience of formulating the problem as a linear prog…
With the maturation of metabolomics science and proliferation of biobanks, clinical metabolic profiling is an increasingly opportunistic frontier for advancing translational clinical research. Automated Machine Learning (AutoML) approaches provide exciting opportunity to guide feature selection in agnostic metabolic pr…
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 # …
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 is an oriented link disjointly embedded in a Seifert surface of such that its homology class forms a basis for a metabolizer of . We show that for a genus three algebraically slice knot , the set $\{ \barμ_{\{γ_1,γ_2,γ_3\}}(123) - \barμ_{\{γ'_1,γ'_2,γ'_3\}}(…
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.
Pseudo-slice knotsmath.GT
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…
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.
Comparison of Atom Representations in Graph Neural Networks for Molecular Property Predictionphysics.chem-ph
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.
Correction to: Curvature growth of some 4-dimensional gradient Ricci soliton singularity modelsmath.DG
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.
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.
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.
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.
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.
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.
Double Witt groupsmath.GT
The difference between slice and doubly-slice knots is reflected in algebra by the difference between metabolic and hyperbolic Blanchfield linking forms. We exploit this algebraic distinction to refine the classical Witt group of linking forms by defining a `double Witt group' of linking forms. We calculate the double …
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.
Volume of the steady-state space of financial flows in a monetary stock-flow-consistent modelphysics.soc-ph
We show that a steady-state stock-flow consistent macro-economic model can be represented as a Constraint Satisfaction Problem (CSP).The set of solutions is a polytope, which volume depends on the constraintsapplied and reveals the potential fragility of the economic circuit,with no need to study the dynamics. Several …
We develop a theory of chain complex double-cobordism for chain complexes equipped with Poincaré duality. The resulting double-cobordism groups are a refinement of Ranicki's torsion algebraic -groups for localisations of a commutative ring with involution. The refinement is analogous to the difference between metabo…
We consider the problem of joint modelling of metabolic signals and gene expression in systems biology applications. We propose an approach based on input-output factorial hidden Markov models and propose a structured variational inference approach to infer the structure and states of the model. We start from the class…
A Study of Machine Learning Models in Predicting the Intention of Adolescents to Smoke Cigarettescs.LG
Study predicts adolescents' intention to smoke cigarettes using ML models.
problem Early identification of adolescents' smoking intentions.
method Five machine learning algorithms tested for predicting intention to smoke cigarettes.
result Gradient Boosting Classifier showed highest accuracy.
We define a set of "second-order" L^(2)-signature invariants for any algebraically slice knot. These obstruct a knot's being a slice knot and generalize Casson-Gordon invariants, which we consider to be "first-order signatures". As one application we prove: If K is a genus one slice knot then, on any genus one Seifert …
A robust algorithm for non-negative matrix factorization (NMF) is presented in this paper with the purpose of dealing with large-scale data, where the separability assumption is satisfied. In particular, we modify the Linear Programming (LP) algorithm of [9] by introducing a reduced set of constraints for exact NMF. In…
Diffusion Fingerprintsstat.ML
We introduce, test and discuss a method for classifying and clustering data modeled as directed graphs. The idea is to start diffusion processes from any subset of a data collection, generating corresponding distributions for reaching points in the network. These distributions take the form of high-dimensional numerica…
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'.
We compute the group of link homotopy classes of link maps of two 2-spheres into 4-space. It turns out to be free abelian, generated by geometric constructions applied to the Fenn-Rolfsen link map and detected by two self-intersection invariants introduced by Paul Kirk in this setting. As a corollary, we show that any …
For effective treatment of Alzheimer disease (AD), it is important to identify subjects who are most likely to exhibit rapid cognitive decline. Herein, we developed a novel framework based on a deep convolutional neural network which can predict future cognitive decline in mild cognitive impairment (MCI) patients using…
Paper introduces a novel framework for supervised graph prediction using Optimal Transport.
problem Supervised labeled graph prediction.
method Fused Gromov-Wasserstein (FGW) loss and FGW barycenter with neural network weights and learned graphs.
result The method can interpolate in the labeled graph space and achieve good performance on difficult problems.
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.
Study improves LLMs for PPI analysis by addressing uncertainty.
problem Uncertainty in LLM predictions for PPIs.
method Fine-tuned LLaMA-3 and BioMedGPT models, LoRA ensembles, Bayesian LoRA for UQ.
result Competitive PPI identification performance across diverse disease contexts.
PolytopeWalk library efficiently samples high-dimensional polytopes.
problem Sampling from high-dimensional polytopes efficiently.
method End-to-end solution including preprocessing and MCMC algorithms.
result Improved sampling efficiency and scalability to high dimensions.
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.
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.
Framework predicts mortality risk in MAFLD subjects.
problem Lack of mortality prediction methods for MAFLD subjects.
method Artificial Intelligence-based framework MAFUS using ML algorithms.
result Support Vector Machines (SVM) is the best model for mortality prediction.
POEM predicts drug properties without tuning, outperforming other methods.
problem Predicting drug properties from molecular structures efficiently.
method POEM combines multiple molecular representations without hyperparameter tuning.
result POEM outperforms industry-standard methods across 17 tasks.
ART automates synthetic biology design with machine learning.
problem Long development times in synthetic biology due to ad-hoc engineering.
method Machine learning and probabilistic modeling for systematic design.
result ART provides optimized strain recommendations and production levels.
Bayesian Optimization (BO) is a data-efficient method for global black-box optimization of an expensive-to-evaluate fitness function. BO typically assumes that computation cost of BO is cheap, but experiments are time consuming or costly. In practice, this allows us to optimize ten or fewer critical parameters in up to…
Networks have in recent years emerged as an invaluable tool for describing and quantifying complex systems in many branches of science. Recent studies suggest that networks often exhibit hierarchical organization, where vertices divide into groups that further subdivide into groups of groups, and so forth over multiple…
We consider the link prediction problem in a partially observed network, where the objective is to make predictions in the unobserved portion of the network. Many existing methods reduce link prediction to binary classification problem. However, the dominance of absent links in real world networks makes misclassificati…
This research predicts diabetes mellitus using machine learning techniques.
problem Early prediction of diabetes mellitus to control and save human life.
method Exploring various risk factors related to diabetes using four machine learning algorithms (SVM, NB, KNN, C4.5 Decision Tree) on adult population data.
result C4.5 decision tree achieved higher accuracy in predicting diabetic mellitus.
Paper presents estimators for entropy and information in probabilistic models.
problem Estimating entropy and mutual information in high dimensions is challenging.
method EEVI uses importance sampling with proposal distributions like amortized variational inference and sequential Monte Carlo.
result EEVI delivers accurate upper and lower bounds on information quantities.
Neural responses are highly variable, and some portion of this variability arises from fluctuations in modulatory factors that alter their gain, such as adaptation, attention, arousal, expected or actual reward, emotion, and local metabolic resource availability. Regardless of their origin, fluctuations in these signal…
New method uses surrogate gradients to train efficient spiking networks on neuromorphic hardware.
problem Training high-performing spiking networks on analog neuromorphic hardware is challenging due to device mismatch and lack of efficient algorithms.
method Introduces a general in-the-loop learning framework based on surrogate gradients.
result Learning self-corrects for device mismatch, resulting in competitive spiking network performance.
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.
Optimizes impression allocation for e-commerce platforms using reinforcement learning.
problem Short-term and long-term returns are not optimized in current e-commerce platform allocation mechanisms.
method Formal lifecycle model of products, reinforcement learning framework, first principal component based permutation, novel experiences generation method.
result Significant improvement in platform and participant health with optimized impression allocation.