Results 111 to 120 of about 12,594 (165)

Big Data in Cancer Genomics: Computational Foundations and Emerging Pathways for Precision Oncology

Computational and Systems Oncology, Volume 6, Issue 1, December 2026.
ABSTRACT This review aims to explore the computational foundations of big data in cancer genomics and examine emerging pathways that support precision oncology and personalized cancer care. A narrative review approach was adopted to synthesize evidence from PubMed, Scopus, Web of Science, and IEEE Xplore.
Nur Vanu, Nur Mohammad, Fahad Ahmed, Towsif Alam, Koushik Saha, Durga Shahi, Adib Hossain, Mia Md. Tofayel Gonee Manik, Mst. Rina Parvin   +8 more
wiley   +1 more source

Frequency‐dependent contraction rates for the Bayesian method to the inverse source problem

Transactions of the London Mathematical Society, Volume 13, Issue 1, December 2026.
Abstract This paper addresses an inverse source problem for acoustic waves in a range of frequencies. Our study has two main goals. First, although the problem is severely ill‐posed with a logarithmic stability estimate, we demonstrate, through careful analysis of the forward map's singular values, that increasing the frequency range enhances stability,
Pu‐Zhao Kow, Jenn‐Nan Wang
wiley   +1 more source

TMBclaw: tumor clone-aware graph learning improves immunotherapy response prediction across heterogeneous cohorts. [PDF]

Brief Bioinform
Wang Y, Zhu T, Song X, Chen P, Zhu X, Chang Z, Wang X, Lai X, Wang J.   +8 more
europepmc   +1 more source

Unveiling the Functional Role of KIF21B in Microglia Inflammatory Response

Glia, Volume 74, Issue 9, September 2026.
Microglia express KIF21B in both sexes. KIF21B binds to microglia cytoskeleton; inflammation alters such interaction promoting a phagocytic state in females. In male microglia KIF21B modulates migration. ABSTRACT Microglia are brain immune cells that maintain homeostasis and respond to injury, changing cell morphology to drive inflammation, migration ...
Danny Ganchala, Alvaro Bautista‐Abad, Daniel Pinto‐Benito, Julia Simon‐Areces, Daniela Grassi, Francesco Paolo Ulloa Severino, Maria‐Angeles Arevalo   +6 more
wiley   +1 more source

Intra‐MRI Head Motion Tracking and Correction: A Quantitative In Vivo Evaluation Framework

NMR in Biomedicine, Volume 39, Issue 9, September 2026.
This framework introduces, for the first time, an in vivo quantification of intra‐MRI head tracking accuracy and precision. By comparing a markerless optical system (MOS) and fat‐signal navigators (FatNav) against a rigid registration‐based reference standard, it successfully detects subtle, axis‐specific performance differences and strengths between ...
Zakaria Zariry, Franck Lamberton, Robert Frost, Thomas Gaass, Thomas Troalen, Holly Rayson, Jakob M. Slipsager, Nathalie Richard, Andre van der Kouwe, James Bonaiuto, Bassem Hiba   +10 more
wiley   +1 more source

Experimental datasets on intermittency and observability in networks of electronic oscillators. Analogue and hybrid configuration. [PDF]

Data Brief
Vera-Ávila VP, Rivera-Durón RR, Rodríguez-Ornelas JM, Vázquez-Fuentes E, Leyva I, Sevilla-Escoboza JR.   +5 more
europepmc   +1 more source

Genome–phenome association prediction using weighted deep matrix factorization with a multisource graph attention network

Quantitative Biology, Volume 14, Issue 3, September 2026.
Abstract Genome–phenome association (GPA) prediction can broaden the understanding of biological mechanisms underlying complex phenotypic traits (e.g., diseases and agronomic traits). Traditional deep matrix factorization (DMF)‐based GPA methods can integrate multiple data types and uncover nonlinear associations but often rely on low‐dimensional ...
Ran Duan, Wei Cao, Maozu Guo, Le Tian, Lingling Zhao   +4 more
wiley   +1 more source

A Crystalline In(II) Hydride. [PDF]

J Am Chem Soc
Mouriki O, Tizzard GJ, Coles SJ, Andrada DM, Planas O.   +4 more
europepmc   +1 more source

Single‐cell marker gene clustering: A unified deep learning framework for marker gene‐based clustering of single‐cell RNA‐sequencing data

Quantitative Biology, Volume 14, Issue 3, September 2026.
Abstract Single‐cell RNA sequencing (scRNA‐seq) has transformed the study of cellular heterogeneity by making it possible to classify individual cells and their functional states. However, the analysis remains difficult because high dropout rates lead to sparse and noisy expression data.
Shahriar Rahman Niloy, Toushif Muktashid Hasan, Md. Saiduzzaman Apu, Fahim Hafiz, Salekul Islam, Riasat Azim   +5 more
wiley   +1 more source

Unusual bonding situations in Th(iv) and U(iv)-Al(iii) pnictogen complexes. [PDF]

Chem Sci
Mahawar P, Wang G, Ward RJ, Kelley SP, Maron L, Walensky JR.   +5 more
europepmc   +1 more source