Results 271 to 280 of about 3,291,817 (342)

Unveiling Multi‐Scale Architectural Features in Single‐Cell Hi‐C Data Using scCAFE

Advanced Science, EarlyView.
scCAFE is a deep learning framework designed to identify multi‐scale 3D genome architectural features from single‐cell Hi‐C data without dense imputation. It predicts chromatin loops, TAD‐like domains, and A/B compartments, enabling efficient characterization of organization at the single‐cell level. scCAFE also identifies marker loop anchors, offering
Fuzhou Wang, Jiecong Lin, Hamid Alinejad‐Rokny, Wenjing Ma, Lingkuan Meng, Lei Huang, Jixiang Yu, Nanjun Chen, Yuchen Wang, Zhongyu Yao, Weidun Xie, Ka‐Chun Wong, Xiangtao Li   +12 more
wiley   +1 more source

Collaborative Duality of CircGLIS3(2) RNA and Protein in human Wound Repair

Advanced Science, EarlyView.
CircGLIS3(2), a circRNA transiently upregulated in dermal fibroblasts during acute wound healing, is induced by IL‐1α and TGF‐β. Its RNA activates fibroblasts into matrix‐secreting cells, while its encoded protein enhances proliferation. The RNA stabilizes PCOLCE, and the protein interacts with BTF3, collectively accelerating wound repair. Abstract The
Guanglin Niu, Maria A. Toma, Jennifer Geara, Xiaowei Bian, Yongjian Chen, Lihua Luo, Qizhang Wang, Yunting Xiao, Manika Vij, Minna Piipponen, Zhuang Liu, Sho Oasa, Letian Zhang, Dörte Schlesinger, Ákos Végvári, Dongqing Li, Aoxue Wang, Vladana Vukojević, Simon J Elsässer, Pehr Sommar, Ning Xu Landén   +20 more
wiley   +1 more source

A CRISPR/Cas9-based enhancement of high-throughput single-cell transcriptomics. [PDF]

Nat Commun
Pandey AC, Bezney J, DeAscanis D, Kirsch EB, Ahmed F, Crinklaw A, Choudhary KS, Mandala T, Deason J, Hamidi JS, Siddique A, Ranganathan S, Brown K, Armstrong J, Head S, Ordoukhanian P, Steinmetz LM, Topol EJ.   +17 more
europepmc   +1 more source

DeepCCDS: Interpretable Deep Learning Framework for Predicting Cancer Cell Drug Sensitivity through Characterizing Cancer Driver Signals

Advanced Science, EarlyView.
DeepCCDS leverages prior knowledge and self‐supervised learning to model cancer driver signals for drug sensitivity prediction. It captures complex regulatory patterns enabling more biologically informed representations. The framework outperforms existing methods across datasets, offering improved accuracy and interpretability.
Jiashuo Wu, Jiyin Lai, Xilong Zhao, Ziyi Wang, Yongbao Zhang, Liqiang Wang, Yinchun Su, Yalan He, Siyuan Li, Ying Jiang, Junwei Han   +10 more
wiley   +1 more source

PubChemLite Plus Collision Cross Section (CCS) Values for Enhanced Interpretation of Nontarget Environmental Data. [PDF]

Environ Sci Technol Lett
Elapavalore A, Ross DH, Grouès V, Aurich D, Krinsky AM, Kim S, Thiessen PA, Zhang J, Dodds JN, Baker ES, Bolton EE, Xu L, Schymanski EL.   +12 more
europepmc   +1 more source

Basics of Evidence-based Practice; Finding Citation Metrics; Tuesday Tech Lunch Series – Introduction to Creating e-Books, Vol 11. (3) [PDF]

, 2013
Texas Medical Center Library
core   +1 more source

Small RNA Toxin‐Assisted Evolution of GC‐Preferred ErCas12a for Enhanced Genome Targeting Range

Advanced Science, EarlyView.
ErCas12a is engineered to target GC‐rich PAMs using a small RNA toxin‐aided positive screening system. The resulting variant, enErCas12a, exhibits an expanded PAM profile and facilitates efficient gene editing in both bacterial and mammalian cells, while preserving high targeting specificity for both canonical and non‐canonical PAM targets.
Zehua Chen, Junyuan Xue, Ziying Wang, Jinyuan Sun, Yinglu Cui, Tong Zhu, Huaiyi Yang, Ming Li, Bian Wu   +8 more
wiley   +1 more source

It won't end with COVID: Countering the next phase of American antivaccine activism 2025-29. [PDF]

PLOS Glob Public Health
Hotez P.
europepmc   +1 more source

HAM-TMC Library Fee Increase, Vol.2 (4) July - August 2004 [PDF]

, 2004
Houston Academy of Medicine-Texas Medical Center Library
core   +1 more source

In vivo functional screens reveal <i>KEAP1</i> loss as a driver of chemoresistance in small cell lung cancer. [PDF]

Sci Adv
Brumage L, Best S, Hippe DS, Grunblatt E, Chanana P, Wu F, Lee MC, Ying Z, Ibrahim A, Chung JH, Vigil A, Fatherree J, Beronja S, Paddison P, Sullivan L, Nabet B, MacPherson D.   +16 more
europepmc   +1 more source