Results 71 to 80 of about 691,007 (262)
Hierarchical Joint Graph Learning and Multivariate Time Series Forecasting
IEEE Access, 2023 Multivariate time series is prevalent in many scientific and industrial domains. Modeling multivariate signals is challenging due to their long-range temporal dependencies and intricate interactions–both direct and indirect.
Juhyeon Kim +5 more
doaj +1 more source
Interpreting the effects of DNA polymerase variants at the structural level
Molecular Oncology, EarlyView.Using MAVISp and molecular dynamics simulations, we analyzed over 60 000 missense variants in POLE and POLD1 from ClinVar, COSMIC, cBioPortal, and saturation mutagenesis. Identified mechanistic indicators, including stability, binding, and long‐range, enable structural interpretation, providing ACMG‐like evidence for possible reclassification of VUS ...
Matteo Arnaudi +7 more
wiley +1 more source
Structure Learning-Based Interaction Feature Construction for Bank Failure Prediction
IEEE AccessMachine-learning models for bank failure prediction commonly rely on bank-level financial ratios. Although such models often achieve strong predictive performance, they offer limited insight into the joint signaling of distress by multiple indicators ...
Chiwoo Lee, Sujin Pyo, Minsu Cho
doaj +1 more source
Molecular Oncology, EarlyView.Single‐cell multi‐omics reveals epigenetic heterogeneity across therapy‐adaptive tumor states, including quiescent/dormant, drug‐tolerant persister, and EMT‐like phenotypes. By linking regulatory features with state‐associated biomarkers, these approaches inform biomarker‐guided therapeutic strategies for evolving tumors.
Hee Jung Kim +3 more
wiley +1 more source
From tumor‐centric to ecosystem‐based hypotheses in brain tumor research and care
Molecular Oncology, EarlyView.Primary brain tumors, whether in adults or children, present a major challenge because of their dramatic prognosis and the ongoing lack of efficient therapeutic approaches. In recent years, a shift has occurred from tumor‐centric concepts to a more holistic view of these tumors as dynamic ecosystems.
Julie Gavard +8 more
wiley +1 more source
Biological PsychologyThis paper concerns structure learning or discovery of discrete generative models. It focuses on Bayesian model selection and the assimilation of training data or content, with a special emphasis on the order in which data are ingested. A key move - in the ensuing schemes - is to place priors on the selection of models, based upon expected free energy.
Karl J. Friston +12 more
openaire +3 more sources
Supervised Learning of Graph Structure [PDF]
, 2011 Graph-based representations have been used with considerable success in computer vision in the abstraction and recognition of object shape and scene structure. Despite this, the methodology available for learning structural representations from sets of training examples is relatively limited.
Andrea Torsello, Luca Rossi 0004
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Molecular Oncology, EarlyView.Single‐cell DNA methylation (scDNAme) profiling maps epimutational clonal evolution, revealing mechanisms of malignancy and therapeutic resistance across diverse cancer types. By providing a high‐resolution landscape of intratumoral heterogeneity, these technologies empower precise patient stratification, guide the development of enhanced ...
Ik Soo Kim
wiley +1 more source
Learning the Structural Vocabulary of a Network [PDF]
Neural Computation, 2017 Networks have become instrumental in deciphering how information is processed and transferred within systems in almost every scientific field today. Nearly all network analyses, however, have relied on humans to devise structural features of networks believed to be most discriminative for an application.
openaire +3 more sources
From pixels to planning: scale-free active inference
Frontiers in Network PhysiologyThis paper describes a discrete state-space model and accompanying methods for generative modeling. This model generalizes partially observed Markov decision processes to include paths as latent variables, rendering it suitable for active inference and ...
Karl Friston +13 more
doaj +1 more source