AIMC Topic: Epithelial-Mesenchymal Transition

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Integrative PPI network and random forest analysis identifies KIF4A, TOP2A, and ASPM protein macromolecules as novel protein biomarkers in renal carcinoma pathogenesis.

International journal of biological macromolecules Jun 1, 2025
The pathogenesis of kidney cancer is not fully understood, so there is an urgent need to identify new biomarkers to improve diagnosis and treatment. The study identified KIF4A, TOP2A and ASPM as novel protein biomarkers for renal cancer and explored ...
Poly-ADP-Ribose Binding Proteins DNA Topoisomerases, Type II Kinesins Kidney Neoplasms Gene Expression Regulation, Neoplastic Carcinoma, Renal Cell Random Forest Nerve Tissue Proteins Epithelial-Mesenchymal Transition Gene Expression Profiling Computational Biology Protein Interaction Maps Cell Line, Tumor Prognosis Humans Biomarkers, Tumor
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An Attention-Based Deep Neural Network Model to Detect Cis-Regulatory Elements at the Single-Cell Level From Multi-Omics Data.

Genes to cells : devoted to molecular & cellular mechanisms Mar 1, 2025
Cis-regulatory elements (cREs) play a crucial role in regulating gene expression and determining cell differentiation and state transitions. To capture the heterogeneous transitions of cell states associated with these processes, detecting cRE activi...
Glioma Epithelial-Mesenchymal Transition Multiomics Regulatory Elements, Transcriptional Regulatory Sequences, Nucleic Acid Leukocytes, Mononuclear SOXB1 Transcription Factors Genomics Zinc Finger E-box-Binding Homeobox 1 Neural Networks, Computer Single-Cell Analysis Humans
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Deep learning-based classification of breast cancer cells using transmembrane receptor dynamics.

Bioinformatics (Oxford, England) Dec 22, 2021
MOTIVATION: Motions of transmembrane receptors on cancer cell surfaces can reveal biophysical features of the cancer cells, thus providing a method for characterizing cancer cell phenotypes. While conventional analysis of receptor motions in the cell...
Humans Female Cell Line, Tumor MCF-7 Cells Deep Learning Breast Neoplasms ErbB Receptors Epithelial-Mesenchymal Transition
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Morphology-based prediction of cancer cell migration using an artificial neural network and a random decision forest.

Integrative biology : quantitative biosciences from nano to macro Dec 19, 2018
Metastasis is the cause of death in most patients of breast cancer and other solid malignancies. Identification of cancer cells with highly migratory capability to metastasize relies on markers for epithelial-to-mesenchymal transition (EMT), a proces...
Lab-On-A-Chip Devices Epithelial-Mesenchymal Transition Neoplasm Metastasis Models, Biological Mice Cell Movement Single-Cell Analysis Neural Networks, Computer Humans Decision Support Techniques Algorithms Neoplasms Animals Cell Line, Tumor Reproducibility of Results Female Breast Neoplasms Deep Learning
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