Results 81 to 90 of about 11,193 (260)

Translating whole‐genome doubling into precision medicine in cancer

open access: yesMolecular Oncology, EarlyView.
Whole‐genome doubling creates a WGD‐positive tumor state characterized by persistent chromosomal instability, karyotypic diversification, and cellular stress. These same biological pressures drive aggressive tumor evolution while exposing therapeutic vulnerabilities, providing a rationale for WGD‐informed precision medicine. Whole‐genome doubling (WGD)
Sejung Lee, Junghyeok Lim, Jinhyuk Bhin
wiley   +1 more source

Metric Gaussian Variational Inference

open access: yesCoRR, 2019
Code is part of NIFTy5 release at https://gitlab.mpcdf.mpg.de/ift ...
Jakob Knollmüller, Torsten A. Enßlin
openaire   +2 more sources

Single‐cell DNA methylation profiling: Technologies, computation, and applications in precision oncology

open access: yesMolecular 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

Decision, Inference, and Information: Formal Equivalences Under Active Inference

open access: yesEntropy
A central challenge in artificial intelligence and cognitive science is identifying a unifying principle that governs inference, learning, and action. Active inference proposes such a principle: the minimization of variational free energy.
Patrick Sweeney   +2 more
doaj   +1 more source

Variational Bayesian Sparse Signal Recovery With LSM Prior

open access: yesIEEE Access, 2017
This paper presents a new sparse signal recovery algorithm using variational Bayesian inference based on the Laplace approximation. The sparse signal is modeled as the Laplacian scale mixture (LSM) prior.
Shuanghui Zhang   +3 more
doaj   +1 more source

Pharmacological chromatin remodeling enhances response to estrogen therapy in ER+ breast cancer

open access: yesMolecular Oncology, EarlyView.
Estrogen therapy elicits clinical benefit in ~ 30% of patients with endocrine‐resistant estrogen receptor (ER)‐positive breast cancer. Based on findings that ER transcriptional activation underlies response to estrogen therapy, we tested the effects of epigenetic dysregulation via pharmacological inhibition of histone deacetylases (HDACi).
Anneka L. Johnson Thomas   +16 more
wiley   +1 more source

VIGoR: Variational Bayesian Inference for Genome-Wide Regression

open access: yesJournal of Open Research Software, 2016
Genome-wide regression using a number of genome-wide markers as predictors is now widely used for genome-wide association mapping and genomic prediction.
Akio Onogi, Hiroyoshi Iwata
doaj   +1 more source

Semi-Implicit Variational Inference

open access: yesCoRR, 2018
Semi-implicit variational inference (SIVI) is introduced to expand the commonly used analytic variational distribution family, by mixing the variational parameter with a flexible distribution. This mixing distribution can assume any density function, explicit or not, as long as independent random samples can be generated via reparameterization.
Mingzhang Yin, Mingyuan Zhou
openaire   +3 more sources

Spatial biology in cancer epigenetics

open access: yesMolecular Oncology, EarlyView.
Spatial epigenomics combines molecular profiling with tissue architecture to reveal how gene regulation is organized within intact tissues. In cancer, these technologies uncover the mechanisms driving tumor heterogeneity and microenvironmental interactions, opening new opportunities for biomarker discovery and precision medicine.
Eva Crespo‐García, Manel Esteller
wiley   +1 more source

Toward Variational Structural Learning of Bayesian Networks

open access: yesIEEE Access
This study presents a novel variational framework for structural learning in Bayesian networks (BNs), addressing the key limitation of existing Bayesian methods: their lack of scalability to large graphs with many variables.
Andres R. Masegosa, Manuel Gomez-Olmedo
doaj   +1 more source

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