Results 211 to 220 of about 30,503 (277)

Integrating Spatial Proteogenomics in Cancer Research

Advanced Science, EarlyView.
Xx xx. ABSTRACT Background: Spatial proteogenomics marks a paradigm shift in oncology by integrating molecular analysis with spatial information from both spatial proteomics and other data modalities (e.g., spatial transcriptomics), thereby unveiling tumor heterogeneity and dynamic changes in the microenvironment.
Yida Wang, Yang Wu, Feng Zhang, Parthiban Periasamy, Haiyue You, Denise Goh, Rachel Elizabeth Ann Fincham, Xin Ning, Danping Wu, Lu Liu, Ying Jiang, Zhiwen Qian, Joe Yeong, Yan Zhang   +13 more
wiley   +1 more source

Self-supervised graph contrastive learning with diffusion augmentation for functional MRI analysis and brain disorder detection. [PDF]

Med Image Anal
Wang X, Fang Y, Wang Q, Yap PT, Zhu H, Liu M.   +5 more
europepmc   +1 more source

Machine Learning‐Guided Engineering of Protein Phase Separation Properties in Immune Regulation

Advanced Science, EarlyView.
PScalpel, a machine learning model integrating protein structure extraction, graph contrastive learning, and a genetic algorithm, guides the engineering of protein phase separation ability. It adopts transfer learning methods to provide predictive recommendations for protein phase separation ability changes through single amino acid mutations in a ...
Chenqiu Zhang, Jia Wang, Zhe Wang, Liyan Zhu, Sihui Cai, Luzhi Zhan, Haorui Liang, Yaoxing Wu, Jianqiang Li, Jun Cui   +9 more
wiley   +1 more source

Deep graph contrastive learning model for drug-drug interaction prediction. [PDF]

PLoS One
Jiang Z, Gong Z, Dai X, Zhang H, Ding P, Shen C.   +5 more
europepmc   +1 more source

SpatialESD: Spatial Ensemble Domain Detection in Spatial Transcriptomics

Advanced Science, EarlyView.
ABSTRACT Spatial transcriptomics (ST) measures gene expression while preserving spatial context within tissues. One of the key tasks in ST analysis is spatial domain detection, which remains challenging due to the complex structure of ST data and the varying performance of individual clustering methods. To address this, we propose SpatialESD, a Spatial
Hongyan Cao, Gaiqin Liu, Jingyi Xia, Runle Chen, Tong Wang, Xiaoling Yang, Ruiling Fang, Yanhong Luo, Ping Zeng, Hongmei Yu, Yanbo Zhang, Yuehua Cui   +11 more
wiley   +1 more source

Deconvolution of spatial transcriptomics data via graph contrastive learning and partial least square regression. [PDF]

Brief Bioinform
Mo Y, Liu J, Zhang L.
europepmc   +1 more source

Persistently Increased Expression of PKMzeta and Unbiased Gene Expression Profiles Identify Hippocampal Molecular Traces of a Long‐Term Active Place Avoidance Memory and “Shadow” Proteins

Advanced Science, EarlyView.
Protein complexes like KIBRA‐PKMζ are crucial for maintaining memories, forming month‐long protein traces in memory‐tagged neurons, but conventional RNA‐seq analysis fails to detect their transcript changes, leaving memory molecules undetected in the shadows of abundantly‐expressed genes.
Jiyeon Han, Alejandro Grau‐Perales, Rayna M. Harris, Edith Lesburguères, Hsin‐Yi Kao, Asit Pal, Juan Marcos Alarcon, Todd C. Sacktor, Stefano Martiniani, Hans A. Hofmann, André A. Fenton   +10 more
wiley   +1 more source

ACLNDA: an asymmetric graph contrastive learning framework for predicting noncoding RNA-disease associations in heterogeneous graphs. [PDF]

Brief Bioinform
Fu L, Yao Z, Zhou Y, Peng Q, Lyu H.
europepmc   +1 more source

Pancancer Fine‐Mapping of Mutational Intolerance Identifies CHEK1 as an Immunosuppressive Driver in Lung Adenocarcinoma

Advanced Science, EarlyView.
This study identifies mutation‐intolerant genes (MIGs), which are mutationally constrained in tumors despite normal‐tissue variability. Using miDriver, the authors pinpoint MIGs essential for tumor‐intrinsic fitness and immune evasion. Focusing on CHEK1, they show it drives tumor fitness and sculpts an immunosuppressive niche via the MIF–CD74 axis ...
Tao Wang, Hongyu Zhao, Xiaojie Sun, Yuqi Ding, Zhipeng Zhu, Xiaotong Yu, Rongyi Zhu, Dan Wang, Kailong Li, Yang Liu, Li‐Bin Wang, Xiaolu Zhao, Baojun Suo, Hongsen Bi, Peipei Zhang, Tong Liu, Fengbiao Mao   +16 more
wiley   +1 more source

STAIG: Spatial transcriptomics analysis via image-aided graph contrastive learning for domain exploration and alignment-free integration. [PDF]

Nat Commun
Yang Y, Cui Y, Zeng X, Zhang Y, Loza M, Park SJ, Nakai K.   +6 more
europepmc   +1 more source