AIMC Topic: Chromosomes

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Genome-Wide Locations of Potential Epimutations Associated with Environmentally Induced Epigenetic Transgenerational Inheritance of Disease Using a Sequential Machine Learning Prediction Approach.

PloS one Nov 16, 2015
Environmentally induced epigenetic transgenerational inheritance of disease and phenotypic variation involves germline transmitted epimutations. The primary epimutations identified involve altered differential DNA methylation regions (DMRs). Differen...
Methoxychlor Sertoli Cells Sequence Analysis, DNA Chromosomes DNA Methylation DDT Epigenesis, Genetic Granulosa Cells Databases, Genetic CpG Islands Cluster Analysis Bayes Theorem Spermatozoa Environmental Exposure Phenotype Reproducibility of Results Mutation Genetic Predisposition to Disease Humans Female Male Computational Biology Machine Learning Genome-Wide Association Study
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Binding Activity Prediction of Cyclin-Dependent Inhibitors.

Journal of chemical information and modeling Jul 10, 2015
The Cyclin-Dependent Kinases (CDKs) are the core components coordinating eukaryotic cell division cycle. Generally the crystal structure of CDKs provides information on possible molecular mechanisms of ligand binding. However, reliable and robust est...
Protein Kinase Inhibitors Chromosomes Algorithms Cyclin-Dependent Kinases Machine Learning Protein Binding Models, Molecular Support Vector Machine Decision Trees Bayes Theorem Protein Conformation
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Unraveling the three-dimensional genome structure using machine learning.

BMB reports May 1, 2025
The study of chromatin interactions has advanced considerably with technologies such as high-throughput chromosome conformation capture (Hi-C) sequencing, providing a genome-wide view of physical interactions within the nucleus. These techniques have...
Genome Chromosomes Humans Machine Learning Chromatin High-Throughput Nucleotide Sequencing
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[Application of convolutional neural networks for the classification of metaphase chromosomes].

Zhonghua yi xue yi chuan xue za zhi = Zhonghua yixue yichuanxue zazhi = Chinese journal of medical genetics Mar 10, 2024
OBJECTIVE: To train a deep convolutional neural networks (CNN) using a labeled data set to classify the metaphase chromosomes and test its accuracy for chromosomal identification.
Karyotyping Metaphase Family Chromosomes Humans Neural Networks, Computer Child
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DeepPHiC: predicting promoter-centered chromatin interactions using a novel deep learning approach.

Bioinformatics (Oxford, England) Jan 1, 2023
MOTIVATION: Promoter-centered chromatin interactions, which include promoter-enhancer (PE) and promoter-promoter (PP) interactions, are important to decipher gene regulation and disease mechanisms. The development of next-generation sequencing techno...
Regulatory Sequences, Nucleic Acid Chromatin Promoter Regions, Genetic Chromosomes Deep Learning
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[Banded chromosome images recognition based on dense convolutional network with segmental recalibration].

Sheng wu yi xue gong cheng xue za zhi = Journal of biomedical engineering = Shengwu yixue gongchengxue zazhi Feb 25, 2021
Human chromosomes karyotyping is an important means to diagnose genetic diseases. Chromosome image type recognition is a key step in the karyotyping process. Accurate and efficient identification is of great significance for automatic chromosome kary...
Neural Networks, Computer Chromosomes Humans
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Feasibility Study on Automatic Interpretation of Radiation Dose Using Deep Learning Technique for Dicentric Chromosome Assay.

Radiation research Feb 1, 2021
The interpretation of radiation dose is an important procedure for both radiological operators and persons who are exposed to background or artificial radiations. Dicentric chromosome assay (DCA) is one of the representative methods of dose estimatio...
Biological Assay Automation Humans Deep Learning Dose-Response Relationship, Radiation Radiation Exposure Chromosomes Radiation Dosage Chromosome Aberrations Chromosomes, Human
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HiCNN: a very deep convolutional neural network to better enhance the resolution of Hi-C data.

Bioinformatics (Oxford, England) Nov 1, 2019
MOTIVATION: High-resolution Hi-C data are indispensable for the studies of three-dimensional (3D) genome organization at kilobase level. However, generating high-resolution Hi-C data (e.g. 5 kb) by conducting Hi-C experiments needs millions of mammal...
Chromosomes Chromatin Mice Genome Software Animals Humans Neural Networks, Computer
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