Results 121 to 130 of about 53,359 (288)

A Circuit of Mechanically Regulated Transcription Factors Balances Regenerative and Fibrotic Memory of Mesenchymal Stromal Cells

Advanced Science, EarlyView.
Producing MSCs on rigid culture substrates induces a scar‐making phenotype, jeapordizing therapeutic success. ‘Tissue‐soft’ surfaces prevent MSC fibrogenesis and preserve regenerative traits. An epigenetic network, driven by HOXA11 and SALL1, maintains ‘soft memory’ by keeping chromatin open in relaxed MSCs, promoting anti‐fibrotic programs.
Fereshteh Sadat Younesi, Andrew E. Miller, Li Diao, Xinying Guo, Natalie Andonian, Elham Karimizadeh, Thomas H. Barker, Boris Hinz   +7 more
wiley   +1 more source

An Adaptive Unsupervised Feature Selection Algorithm Based on MDS for Tumor Gene Data Classification. [PDF]

Sensors (Basel), 2021
Jin B, Fu C, Jin Y, Yang W, Li S, Zhang G, Wang Z.   +6 more
europepmc   +1 more source

Unsupervised Feature Selection Using Extreme Learning Machine

International audienceIn machine learning, feature selection is an important step in building an inference model with good generalization capacity when the number of variables is large.
Grall-Maës, Edith, Beauseroy, Pierre, Kanouté, Mamadou   +2 more
core   +1 more source

Combining Fret and Super‐Resolution Microscopy Reveals Kinase Activation and Mitochondrial Activity at the Nanoscale

Advanced Science, EarlyView.
BioSenSRRF is an open‐source workflow that combines conventional FRET biosensors with SRRF reconstruction to generate sub‐diffraction FRET index maps on standard microscopes. The pipeline integrates automated image registration, SRRF reconstruction, quantitative FRET index calculation, and random line‐based hotspot analysis to uncover AURKA‐dependent ...
Nicolas Y. Jolivet, Pierre‐Jean Desmaison, Xavier Pinson, Arthur Masson, Olivier Delalande, Giulia Bertolin   +5 more
wiley   +1 more source

Consensus Guided Multi-View Unsupervised Feature Selection with Hybrid Regularization

Applied Sciences
Multi-source heterogeneous data has been widely adopted in developing artificial intelligence systems in recent years. In real-world scenarios, raw multi-source data are generally unlabeled and inherently contain multi-view noise and feature redundancy ...
Yifan Shi, Haixin Zeng, Xinrong Gong, Lei Cai, Wenjie Xiang, Qi Lin, Huijie Zheng, Jianqing Zhu   +7 more
doaj   +1 more source

The Unsupervised Feature Selection Algorithms Based on Standard Deviation and Cosine Similarity for Genomic Data Analysis. [PDF]

Front Genet, 2021
Xie J, Wang M, Xu S, Huang Z, Grant PW.
europepmc   +1 more source

Unsupervised group feature selection for media classification

, 2017
The selection of an appropriate feature set is crucial for the efficient analysis of any media collection. In general, feature selection strongly depends on the data and commonly requires expert knowledge and previous experiments in related application ...
Breiteneder, Christian; orcid:, Maia Zaharieva, Hudec, Marcus, Christian Breiteneder, Marcus Hudec, Zaharieva, Maia   +5 more
core   +1 more source

GHRHR Deficiency Enhances Retinal Ganglion Cell Survival and Visual Functions in Experimental Glaucoma by Inhibiting Ferroptosis

Advanced Science, EarlyView.
Glaucoma, a major cause of blindness, involves retinal ganglion cell (RGC) degeneration. This study shows growth hormone‐releasing hormone receptor (GHRHR) deficiency preserves RGC survival and restores vision, unlike activation which only aids survival.
Yan Tong, Ming Ho Yam, Jiaxin Zhang, Lin Du, Linbin Zhou, Yolanda Wong Ying Yip, Bo Man Ho, Hemlata Bisnauthsing, Jiahui Li, Ivan Kong, Shushu Xu, Changzhen Fu, Karl K.H. So, Joaquim S.L. Vong, Ling‐Ping Cen, Ming‐Ming Yang, Khazeema Yousaf, Mai Har Sham, Sun On Chan, Poemen P. Chan, Chi Pui Pang, Clement C. Tham, Jing Na He, Jian Li, Wai Kit Chu   +24 more
wiley   +1 more source

Unsupervised feature selection using nonnegative spectral analysis

, 2012
In this paper, a new unsupervised learning algorithm, namely Nonnegative Discriminative Feature Selection (NDFS), is proposed. To exploit the discriminative information in unsupervised scenarios, we perform spectral clustering to learn the cluster labels
Lu, Hanqing, Li, Zechao, Liu, Jing, Zhou, Xiaofang, Yang, Yi   +4 more
core  

Large‐Scale Genomics Reveals Three‐Source Ancestry and Layered Adaptation to High Altitude in Tibetan Chickens

Advanced Science, EarlyView.
Whole‐genome analysis of 1,054 chickens reveals three ancestral sources (NWC, SYA, and SHF) with distinct temporal entry patterns into the Tibetan Plateau. Route‐specific selection scans, calibrated against a demographic null, suggest complementary functional enrichments—vascular homeostasis (NWC), calcium signaling and cardiac adaptation (SYA), and ...
Zongyi Zhao, Ruixue Nie, Hailu Fan, Tenzin Ngodroup, Li Zhu, Hongbin Pan, Bo Zhang, Hao Zhang   +7 more
wiley   +1 more source