AIMC Topic: Transcription Factors

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Learning and interpreting the gene regulatory grammar in a deep learning framework.

PLoS computational biology Nov 2, 2020
Deep neural networks (DNNs) have achieved state-of-the-art performance in identifying gene regulatory sequences, but they have provided limited insight into the biology of regulatory elements due to the difficulty of interpreting the complex features...
Animals Deep Learning Gene Regulatory Networks Mice Neural Networks, Computer Computer Simulation Terminology as Topic Binding Sites Enhancer Elements, Genetic Genes, Regulator Computational Biology Models, Genetic Transcription Factors Databases, Genetic
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A deep learning approach to programmable RNA switches.

Nature communications Oct 7, 2020
Engineered RNA elements are programmable tools capable of detecting small molecules, proteins, and nucleic acids. Predicting the behavior of these synthetic biology components remains a challenge, a situation that could be addressed through enhanced ...
Genetic Engineering Riboswitch Humans Deep Learning Datasets as Topic Transcription Factors Synthetic Biology Kinetics Genome, Viral Thermodynamics
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Deep learning of immune cell differentiation.

Proceedings of the National Academy of Sciences of the United States of America Sep 25, 2020
Although we know many sequence-specific transcription factors (TFs), how the DNA sequence of cis-regulatory elements is decoded and orchestrated on the genome scale to determine immune cell differentiation is beyond our grasp. Leveraging a granular a...
Animals Deep Learning Mice Transcription Factors Humans Chromatin Databases, Genetic Gene Expression Regulation Hematopoiesis Regulatory Sequences, Nucleic Acid
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Transcription factor expression as a predictor of colon cancer prognosis: a machine learning practice.

BMC medical genomics Sep 21, 2020
BACKGROUND: Colon cancer is one of the leading causes of cancer deaths in the USA and around the world. Molecular level characters, such as gene expression levels and mutations, may provide profound information for precision treatment apart from path...
Humans Machine Learning Prognosis Transcription Factors Colonic Neoplasms Proportional Hazards Models Kaplan-Meier Estimate Female Biomarkers, Tumor Male Gene Expression Regulation, Neoplastic
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Enhancing the interpretability of transcription factor binding site prediction using attention mechanism.

Scientific reports Aug 7, 2020
Transcription factors (TFs) regulate the gene expression of their target genes by binding to the regulatory sequences of target genes (e.g., promoters and enhancers). To fully understand gene regulatory mechanisms, it is crucial to decipher the relat...
Gene Expression Regulation Base Sequence Binding Sites Neural Networks, Computer Forecasting Deep Learning Protein Binding Computational Biology Models, Theoretical Transcription Factors
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Machine learning uncovers cell identity regulator by histone code.

Nature communications Jun 1, 2020
Conversion between cell types, e.g., by induced expression of master transcription factors, holds great promise for cellular therapy. Our ability to manipulate cell identity is constrained by incomplete information on cell identity genes (CIGs) and t...
Chromatin Immunoprecipitation Sequencing Gene Expression Regulation Cells Pluripotent Stem Cells Histone Code Transcription Factors Databases, Genetic Humans Human Umbilical Vein Endothelial Cells Regenerative Medicine Machine Learning Algorithms Phenotype RNA-Seq Gene Regulatory Networks Epigenesis, Genetic
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Analysis of gene expression profiles of lung cancer subtypes with machine learning algorithms.

Biochimica et biophysica acta. Molecular basis of disease Apr 28, 2020
Lung cancer is one of the most common cancer types worldwide and causes more than one million deaths annually. Lung adenocarcinoma (AC) and lung squamous cell cancer (SCC) are two major lung cancer subtypes and have different characteristics in sever...
Gene Expression Profiling Transcriptome Lung Neoplasms Computational Biology Gene Expression Regulation, Neoplastic Transcription Factors Cystatin A Membrane Transport Proteins Diagnosis, Differential Monte Carlo Method Humans Cadherins Serpins Machine Learning Adenocarcinoma of Lung Carcinoma, Squamous Cell Datasets as Topic Tumor Suppressor Proteins Adenosine Triphosphatases Terminology as Topic
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Identification of Methylated Gene Biomarkers in Patients with Alzheimer's Disease Based on Machine Learning.

BioMed research international Mar 26, 2020
BACKGROUND: Alzheimer's disease (AD) is a neurodegenerative disorder and characterized by the cognitive impairments. It is essential to identify potential gene biomarkers for AD pathology.
Patients Carrier Proteins Humans Peptide Hydrolases Cyclin-Dependent Kinase Inhibitor p57 DNA-Binding Proteins Machine Learning Mitochondrial Proteins Protein Tyrosine Phosphatases Genetic Markers Microfilament Proteins Gene Expression Regulation, Neoplastic Alzheimer Disease Transcription Factors DNA Methylation
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Mb-level CpG and TFBS islands visualized by AI and their roles in the nuclear organization of the human genome.

Genes & genetic systems Mar 12, 2020
Unsupervised machine learning that can discover novel knowledge from big sequence data without prior knowledge or particular models is highly desirable for current genome study. We previously established a batch-learning self-organizing map (BLSOM) f...
Heterochromatin Genome, Human Binding Sites Centromere Chromosomes, Human Protein Binding Transcription Factors Humans Oligonucleotides Artificial Intelligence CpG Islands
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A generative model for constructing nucleic acid sequences binding to a protein.

BMC genomics Dec 27, 2019
BACKGROUND: Interactions between protein and nucleic acid molecules are essential to a variety of cellular processes. A large amount of interaction data generated by high-throughput technologies have triggered the development of several computational...
DNA Base Sequence Nucleic Acid Conformation Humans Aptamers, Nucleotide Neural Networks, Computer Protein Binding Algorithms Transcription Factors Proteins
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