Results 201 to 210 of about 536,893 (261)

ML-BUSMetab: Machine Learning-Based Metabolomic Profiling for Predicting Aspirin Response in Colorectal Cancer Chemoprevention: A Multi-Model Explainable Artificial Intelligence Approach with External Validation. [PDF]

J Clin Med
Pınar A, Arslan AK, Çolak C.
europepmc   +1 more source

Single‐Cell Reveal GALNT7‐Dependent Ferroptosis Suppression as a Mechanism of Immunotherapy Resistance in Non‐Small Cell Lung Cancer

Advanced Science, EarlyView.
Integrated clinical and mechanistic analyses identify GALNT7 as a ferroptosis‐suppressive regulator associated with immunotherapy resistance in non‐small cell lung cancer. GALNT7 depletion promotes lipid peroxidation, mitochondrial dysfunction, and ferroptosis, enhances CD8+ T‐cell activation and IFN‐γ production, and sensitizes tumors to PD‐1 blockade,
Jiadi Gan, Qian Zheng, Deng Liu, Ruichao Nie, Menglin Yao, Xiaolong Tang, Renjie Xu, Jinghong Xian, Wenjun Meng, Wenxin Luo, Dan Liu, Guiyi Ji   +11 more
wiley   +1 more source

Correction to "Pre-Treatment Serum Prognostic Scores and Survival in Curatively Treated Laryngeal Cancer". [PDF]

Laryngoscope Investig Otolaryngol
europepmc   +1 more source

Machine learning risk stratification to identify people living with HIV at high risk of delayed ART and advanced immunosuppression: a precision public health approach. [PDF]

Front Public Health
Yin J, Li T, Jin J, Chen J, Ba H, Zhang Y, Li J, Liu H, Ma K.   +8 more
europepmc   +1 more source

Single‐Cell RNA Editing Identifies T Cell ADAR1 as a Key Regulator of Immune Exhaustion and Anti‐PD‐1 Resistance in Colorectal Cancer

Advanced Science, EarlyView.
Single‐cell RNA editing analysis identifies ADAR1 as a regulator of dysfunctional T cell states in colorectal cancer. Elevated ADAR1 activity promotes T cell exhaustion and impairs antitumor immunity partly through TGF‐β‐SMAD signaling, contributing to anti‐PD‐1 resistance and highlighting T cell ADAR1 as a potential therapeutic target and biomarker ...
Da Kang, Song‐Zuo Xie, Yong‐Zhou Luo, Xin‐Pei Deng, Xi‐Rong Tan, Ze‐Geng Chen, Ling‐Xing Zeng, Gong Chen, Pei‐Rong Ding, Zhi‐Zhong Pan, Rong‐Xin Zhang   +10 more
wiley   +1 more source

Liquid biopsy-based epigenetic signatures for early detection of prostate cancer: a systematic review. [PDF]

Front Oncol
Cleanclay WD, Kintung IF, Kernyuy FB, Yensii NG, Fagbohun BO, Amadi EC, Kaze NN.   +6 more
europepmc   +1 more source

CauFinder: Steering Cell‐State and Phenotype Transitions by Causal Disentanglement Learning

Advanced Science, EarlyView.
CauFinder combines causal disentanglement modeling and network control to prioritize causal drivers of cell‐state transitions from observational transcriptomic data. The framework separates transition‐relevant signals from spurious associations, nominates intervention targets across biological and disease contexts, and identifies DAAM1 as an actionable
Chengming Zhang, Zexi Chen, Yuanxiang Miao, Zuolin Shen, Deyu Cai, Shijie Tang, Yun Xue, Weifeng Guo, Hongbin Ji, Jian Liu, Kazuyuki Aihara, Luonan Chen   +11 more
wiley   +1 more source

Risk prediction models for venous thromboembolism among patients with multiple myeloma: a systematic review and meta-analysis. [PDF]

PeerJ
Yang J, Pu Y, Li Y, Li X, Jiang X, Zou L.   +5 more
europepmc   +1 more source

AUC's brandlaboratorium

, 1977
Hviid, Niels Jørgen, Olesen, Frits Bolonius   +1 more
openaire   +1 more source

Condition‐Associated Pattern Extraction and Recovery From Multi‐Condition Single‐Cell RNA‐seq Data With CAPER

Advanced Science, EarlyView.
Decoupling biological signals from unwanted variation in multi‑condition single‑cell RNA sequencing data remains challenging. CAPER disentangles condition‑associated biological effects from sample heterogeneity through matrix factorization, producing interpretable latent factors and a batch‑corrected expression matrix.
Ye Li, Jin Ning, An Wang, Minxi Shi, Yuanze Chen, Guoliang Liu, Shiquan Sun   +6 more
wiley   +1 more source