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DNA Copy Number Variations

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Multiclass Cancer Prediction Based on Copy Number Variation Using Deep Learning.

Computational intelligence and neuroscience
DNA copy number variation (CNV) is the type of DNA variation which is associated with various human diseases. CNV ranges in size from 1 kilobase to several megabases on a chromosome. Most of the computational research for cancer classification is tra...

A scalable artificial intelligence platform that automatically finds copy number variations (CNVs) in journal articles and transforms them into a database: CNV extraction, transformation, and loading AI (CNV-ETLAI).

Computers in biology and medicine
BACKGROUND: Although copy number variations (CNVs) are infrequent, each anomaly is unique, and multiple CNVs can appear simultaneously. Growing evidence suggests that CNVs contribute to a wide range of diseases. When CNVs are detected, assessment of ...

Polishing copy number variant calls on exome sequencing data via deep learning.

Genome research
Accurate and efficient detection of copy number variants (CNVs) is of critical importance owing to their significant association with complex genetic diseases. Although algorithms that use whole-genome sequencing (WGS) data provide stable results wit...

Deep learning and multi-omics approach to predict drug responses in cancer.

BMC bioinformatics
BACKGROUND: Cancers are genetically heterogeneous, so anticancer drugs show varying degrees of effectiveness on patients due to their differing genetic profiles. Knowing patient's responses to numerous cancer drugs are needed for personalized treatme...

The NHGRI-EBI GWAS Catalog: knowledgebase and deposition resource.

Nucleic acids research
The NHGRI-EBI GWAS Catalog (www.ebi.ac.uk/gwas) is a FAIR knowledgebase providing detailed, structured, standardised and interoperable genome-wide association study (GWAS) data to >200 000 users per year from academic research, healthcare and industr...

Biologically Interpretable Deep Learning To Predict Response to Immunotherapy In Advanced Melanoma Using Mutations and Copy Number Variations.

Journal of immunotherapy (Hagerstown, Md. : 1997)
Only 30-40% of advanced melanoma patients respond effectively to immunotherapy in clinical practice, so it is necessary to accurately identify the response of patients to immunotherapy pre-clinically. Here, we develop KP-NET, a deep learning model th...

Deep learning-based multiomics integration model for predicting axillary lymph node metastasis in breast cancer.

Future oncology (London, England)
To develop a deep learning-based multiomics integration model. Five types of omics data (mRNA, DNA methylation, miRNA, copy number variation and protein expression) were used to build a deep learning-based multiomics integration model a deep neura...

DeepGenePrior: A deep learning model for prioritizing genes affected by copy number variants.

PLoS computational biology
The genetic etiology of brain disorders is highly heterogeneous, characterized by abnormalities in the development of the central nervous system that lead to diminished physical or intellectual capabilities. The process of determining which gene driv...

Identify gestational diabetes mellitus by deep learning model from cell-free DNA at the early gestation stage.

Briefings in bioinformatics
Gestational diabetes mellitus (GDM) is a common complication of pregnancy, which has significant adverse effects on both the mother and fetus. The incidence of GDM is increasing globally, and early diagnosis is critical for timely treatment and reduc...