Results 211 to 220 of about 277,018 (298)

KMT2C Loss Promotes NF2‐Wildtype Meningioma Progression and Ferroptosis Sensitivity via Epigenetic Repression of Hippo Signaling

open access: yesAdvanced Science, EarlyView.
In NF2–wild‐type meningiomas, loss of the epigenetic regulator KMT2C suppresses NF2 transcription and inactivates Hippo signaling, driving tumor progression and increasing ferroptosis sensitivity. Restoration of histone acetylation reverses these effects and inhibits tumor growth, identifying KMT2C as a key regulator linking epigenetic control, NF2 ...
Liuchao Zhang   +13 more
wiley   +1 more source

A Murine Database of Structural Variants Identifies A Candidate Gene for a Spontaneous Murine Lymphoma Model

open access: yesAdvanced Science, EarlyView.
We analyzed long‐read genomic sequencing data obtained from 40 inbred mouse strains to produce a large database of structural variants. This dataset captures the major types of structural variants, which includes deletions, insertions, duplications, and inversions.
Wenlong Ren   +6 more
wiley   +1 more source

Correction for: Accelerated chronic lymphocytic leukemia - characteristics and retrospective analysis of the Polish Adult Leukemia Study Group. [PDF]

open access: yesContemp Oncol (Pozn)
Sośnia O   +22 more
europepmc   +1 more source

Clinically Informed Intelligent Classification of Ovarian Cancer Cells by Label‐Free Holographic Imaging Flow Cytometry

open access: yesAdvanced Intelligent Systems, Volume 7, Issue 3, March 2025.
Quantitative phase maps of single cells recorded in flow cytometry modality feed a hierarchical architecture of machine learning models for the label‐free identification of subtypes of ovarian cancer. The employment of a priori clinical information improves the classification performance, thus emulating the clinical application of liquid biopsy during ...
Daniele Pirone   +11 more
wiley   +1 more source

A Multi-Task Ensemble Strategy for Gene Selection and Cancer Classification. [PDF]

open access: yesBioengineering (Basel)
Lin S, Lin Z, Zhang J, Leung MF.
europepmc   +1 more source

RPSLearner: A Novel Approach Based on Random Projection and Deep Stacking Learning for Categorizing Non‐Small Cell Lung Cancer

open access: yesAdvanced Intelligent Systems, EarlyView.
Identifying non‐small cell lung cancer (NSCLC) subtypes is essential for precision cancer treatment. Conventional methods are laborious, or time‐consuming. To address these concerns, RPSLearner is proposed, which combines random projection and stacking ensemble learning for accurate NSCLC subtyping. RPSLearner outperforms state‐of‐the‐art approaches in
Xinchao Wu, Jieqiong Wang, Shibiao Wan
wiley   +1 more source

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