Results 31 to 40 of about 27,865 (256)

Emerging experimental and computational methods for studying redox‐regulated structural transitions

open access: yesFEBS Letters, EarlyView.
Redox reactions can reshape proteins and alter how they behave in cells, with important consequences for health and disease. This review explores emerging experimental and computational approaches for discovering these redox‐sensitive protein switches, revealing their structural effects, and predicting their behavior, opening new opportunities to ...
Tasneem Rass   +2 more
wiley   +1 more source

Metastatic niche shaped by host factors influences disseminated cancer cell fate

open access: yesFEBS Letters, EarlyView.
Metastasis is shaped not only by cancer cells but also by the environments they encounter. This review explores how factors such as aging, diet, the microbiome, lifestyle, and environmental exposures remodel organ‐specific niches in the lung, liver, bone, and brain, influencing where metastatic cells survive, remain dormant, or grow, and ultimately ...
Gwennan Delyth Ward   +2 more
wiley   +1 more source

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

Explainable Artificial Intelligence in Echocardiography [PDF]

open access: yesAdvanced Ultrasound in Diagnosis and Therapy
Recent advancements in artificial intelligence (AI) have generated novel opportunities and challenges in ultrasound imaging. Deep learning algorithms exhibit significant potential in analyzing echocardiographic images, encompassing tasks such as view ...
Hu Xuelin, Zhu Ye, Zhang Zisang, Quan Yuanting, Chen Wenwen, Chen Leichong, Xu Guangyu, Qin Luning, Xie Mingxing, Zhang Li
doaj   +1 more source

CEACAM1 participation in breast cancer progression

open access: yesMolecular Oncology, EarlyView.
In invasive breast cancer (BC), CEACAM1 shifts from an apical to a uniform membranous/cytoplasmic pattern, or is lost, as tumors dedifferentiate, inversely tracking the Ki‐67 proliferative index. In MCF‐7 cells, only CEACAM1‐4L suppresses proliferation, repressing cell cycle and growth factor genes.
Mykola Lyndin   +3 more
wiley   +1 more source

A Survey on Explainable Artificial Intelligence for Cybersecurity

open access: yesIEEE Transactions on Network and Service Management, 2023
The black-box nature of artificial intelligence (AI) models has been the source of many concerns in their use for critical applications. Explainable Artificial Intelligence (XAI) is a rapidly growing research field that aims to create machine learning models that can provide clear and interpretable explanations for their decisions and actions.
Gaith Rjoub   +7 more
openaire   +2 more sources

A light‐triggered Time‐Resolved X‐ray Solution Scattering (TR‐XSS) workflow with application to protein conformational dynamics

open access: yesFEBS Open Bio, EarlyView.
Time‐resolved X‐ray solution scattering captures how proteins change shape in real time under near‐native conditions. This article presents a practical workflow for light‐triggered TR‐XSS experiments, from data collection to structural refinement. Using a calcium‐transporting membrane protein as an example, the approach can be broadly applied to study ...
Fatemeh Sabzian‐Molaei   +3 more
wiley   +1 more source

Explainable and responsible artificial intelligence [PDF]

open access: yesElectronic Markets, 2022
Christian Meske   +3 more
openaire   +3 more sources

Explainable Artificial Intelligence for Social Sciences and Humanities: A Systematic Bibliometric Analysis

open access: yesEngineering Proceedings
The increasing adoption of artificial intelligence in the social sciences and humanities has intensified concerns regarding transparency, interpretability, and epistemic accountability, thereby contributing to the growing prominence of explainable ...
Nikos Koutsoupias, Marios Nosios
doaj   +1 more source

Integrating machine learning algorithms and explainable artificial intelligence approach for predicting patient unpunctuality in psychiatric clinics

open access: yesHealthcare Analytics, 2023
This study addresses patient unpunctuality, a major concern affecting patient waiting time, resource utilization, and quality of care. We develop and compare four machine learning models, including multinomial logistic regression, decision tree, random ...
Alireza Kasaie, Suchithra Rajendran
doaj   +1 more source

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