AIMC Topic: Exome

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Validation of an AI-enabled exome/transcriptome liquid biopsy platform for early detection, MRD, disease monitoring, and therapy selection for solid tumors.

Scientific reports Jul 1, 2025
Effective clinical management of patients with cancer requires highly accurate diagnosis, precise therapy selection, and highly sensitive monitoring of disease burden. Caris Assure is a multifunctional blood-based assay that couples whole exome and w...
Male Middle Aged Neoplasms Machine Learning Female Adult Liquid Biopsy Exome Sequencing Neoplasm, Residual Exome Gene Expression Profiling Transcriptome Humans Early Detection of Cancer Artificial Intelligence
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A deep learning model for prediction of autism status using whole-exome sequencing data.

PLoS computational biology Nov 8, 2024
Autism is a developmental disability. Research demonstrated that children with autism benefit from early diagnosis and early intervention. Genetic factors are considered major contributors to the development of autism. Machine learning (ML), includin...
Computational Biology Autistic Disorder Exome Sequencing Multifactorial Inheritance Deep Learning Humans Exome Child Neural Networks, Computer Genetic Predisposition to Disease
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Exome sequence analysis identifies rare coding variants associated with a machine learning-based marker for coronary artery disease.

Nature genetics Jun 11, 2024
Coronary artery disease (CAD) exists on a spectrum of disease represented by a combination of risk factors and pathogenic processes. An in silico score for CAD built using machine learning and clinical data in electronic health records captures disea...
Humans Machine Learning Polymorphism, Single Nucleotide Coronary Artery Disease Genetic Variation Exome Sequencing Female Genome-Wide Association Study Genetic Predisposition to Disease Exome
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An AI-based approach driven by genotypes and phenotypes to uplift the diagnostic yield of genetic diseases.

Human genetics Mar 23, 2024
Identifying disease-causing variants in Rare Disease patients' genome is a challenging problem. To accomplish this task, we describe a machine learning framework, that we called "Suggested Diagnosis", whose aim is to prioritize genetic variants in an...
Exome Genetic Testing Genome, Human Genetic Diseases, Inborn Humans Artificial Intelligence Machine Learning Genetic Predisposition to Disease Genotype Phenotype Genomics
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Polishing copy number variant calls on exome sequencing data via deep learning.

Genome research Jun 13, 2022
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...
High-Throughput Nucleotide Sequencing Exome Sequencing Exome Reproducibility of Results Deep Learning DNA Copy Number Variations Algorithms
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PhenoApt leverages clinical expertise to prioritize candidate genes via machine learning.

American journal of human genetics Jan 20, 2022
In recent years, exome sequencing (ES) has shown great utility in the diagnoses of Mendelian disorders. However, after rigorous filtering, a typical ES analysis still involves the interpretation of hundreds of variants, which greatly hinders the rapi...
Humans Phenotype Cohort Studies Machine Learning Software Hearing Loss, Sensorineural Genetic Testing Intellectual Disability Scoliosis Genotype Genetic Diseases, Inborn Exome Nystagmus, Congenital Exome Sequencing Computational Biology Microcephaly Databases, Genetic
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DNN-Boost: Somatic mutation identification of tumor-only whole-exome sequencing data using deep neural network and XGBoost.

Journal of bioinformatics and computational biology Dec 13, 2021
Detection of somatic mutation in whole-exome sequencing data can help elucidate the mechanism of tumor progression. Most computational approaches require exome sequencing for both tumor and normal samples. However, it is more common to sequence exome...
Exome Sequencing Exome Neoplasms Mutation High-Throughput Nucleotide Sequencing Humans Neural Networks, Computer
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Artificial intelligence (AI)-assisted exome reanalysis greatly aids in the identification of new positive cases and reduces analysis time in a clinical diagnostic laboratory.

Genetics in medicine : official journal of the American College of Medical Genetics Nov 30, 2021
PURPOSE: Artificial intelligence (AI) and variant prioritization tools for genomic variant analysis are being rapidly developed for use in clinical diagnostic testing. However, their clinical utility and reliability are currently limited. Therefore, ...
Exome Artificial Intelligence Reproducibility of Results Humans Genetic Testing Exome Sequencing
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Cancer classification and pathway discovery using non-negative matrix factorization.

Journal of biomedical informatics Jul 2, 2019
OBJECTIVES: Extracting genetic information from a full range of sequencing data is important for understanding disease. We propose a novel method to effectively explore the landscape of genetic mutations and aggregate them to predict cancer type.
Databases, Genetic Sequence Analysis, DNA Humans Regression Analysis Mutation Algorithms Neoplasms Cell Line, Tumor Exome Reproducibility of Results Diagnosis, Computer-Assisted Gene Expression Regulation, Neoplastic Pilot Projects Support Vector Machine
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A machine-learning approach for accurate detection of copy number variants from exome sequencing.

Genome research Jun 6, 2019
Copy number variants (CNVs) are a major cause of several genetic disorders, making their detection an essential component of genetic analysis pipelines. Current methods for detecting CNVs from exome-sequencing data are limited by high false-positive ...
DNA Copy Number Variations Humans Exome Sequencing Machine Learning Exome Algorithms
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