AIMC Topic: Genome, Human
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Tissue of origin detection for cancer tumor using low-depth cfDNA samples through combination of tumor-specific methylation atlas and genome-wide methylation density in graph convolutional neural networks.
Journal of translational medicine
Jul 3, 2024
BACKGROUND: Cell free DNA (cfDNA)-based assays hold great potential in detecting early cancer signals yet determining the tissue-of-origin (TOO) for cancer signals remains a challenging task. Here, we investigated the contribution of a methylation at...
Recurrent neural network for predicting absence of heterozygosity from low pass WGS with ultra-low depth.
BMC genomics
May 14, 2024
BACKGROUND: The absence of heterozygosity (AOH) is a kind of genomic change characterized by a long contiguous region of homozygous alleles in a chromosome, which may cause human genetic disorders. However, no method of low-pass whole genome sequenci...
On Minimizers and Convolutional Filters: Theoretical Connections and Applications to Genome Analysis.
Journal of computational biology : a journal of computational molecular cell biology
Apr 30, 2024
Machine Learning of Three-Dimensional Protein Structures to Predict the Functional Impacts of Genome Variation.
Journal of chemical information and modeling
Apr 18, 2024
Research in the human genome sciences generates a substantial amount of genetic data for hundreds of thousands of individuals, which concomitantly increases the number of variants of unknown significance (VUS). Bioinformatic analyses can successfully...
Simplified detection of genetic background admixture using artificial intelligence.
Clinical genetics
Apr 1, 2024
Admixture refers to the mixing of genetic ancestry from different populations. Admixture is important for genomic medicine because it can affect how an individual responds to certain medications, how they metabolize drugs, and susceptibility to certa...
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...
Genotype imputation methods for whole and complex genomic regions utilizing deep learning technology.
Journal of human genetics
Jan 15, 2024
The imputation of unmeasured genotypes is essential in human genetic research, particularly in enhancing the power of genome-wide association studies and conducting subsequent fine-mapping. Recently, several deep learning-based genotype imputation me...
MAGPIE: accurate pathogenic prediction for multiple variant types using machine learning approach.
Genome medicine
Jan 8, 2024
Identifying pathogenic variants from the vast majority of nucleotide variation remains a challenge. We present a method named Multimodal Annotation Generated Pathogenic Impact Evaluator (MAGPIE) that predicts the pathogenicity of multi-type variants....
Deep learning of human polyadenylation sites at nucleotide resolution reveals molecular determinants of site usage and relevance in disease.
Nature communications
Nov 15, 2023
The genomic distribution of cleavage and polyadenylation (polyA) sites should be co-evolutionally optimized with the local gene structure. Otherwise, spurious polyadenylation can cause premature transcription termination and generate aberrant protein...
Improving variant calling using population data and deep learning.
BMC bioinformatics
May 12, 2023
Large-scale population variant data is often used to filter and aid interpretation of variant calls in a single sample. These approaches do not incorporate population information directly into the process of variant calling, and are often limited to ...
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