AIMC Topic: Alleles
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Prediction of peptide binding to MHC using machine learning with sequence and structure-based feature sets.
Biochimica et biophysica acta. General subjects
Jan 16, 2020
Selecting peptides that bind strongly to the major histocompatibility complex (MHC) for inclusion in a vaccine has therapeutic potential for infections and tumors. Machine learning models trained on sequence data exist for peptide:MHC (p:MHC) binding...
ImaGene: a convolutional neural network to quantify natural selection from genomic data.
BMC bioinformatics
Nov 22, 2019
BACKGROUND: The genetic bases of many complex phenotypes are still largely unknown, mostly due to the polygenic nature of the traits and the small effect of each associated mutation. An alternative approach to classic association studies to determini...
Forensic STR allele extraction using a machine learning paradigm.
Forensic science international. Genetics
Oct 28, 2019
We present a machine learning approach to short tandem repeat (STR) sequence detection and extraction from massively parallel sequencing data called Fragsifier. Using this approach, STRs are detected on each read by first locating the longest repeat ...
DeepTetrad: high-throughput image analysis of meiotic tetrads by deep learning in Arabidopsis thaliana.
The Plant journal : for cell and molecular biology
Oct 22, 2019
Meiotic crossovers facilitate chromosome segregation and create new combinations of alleles in gametes. Crossover frequency varies along chromosomes and crossover interference limits the coincidence of closely spaced crossovers. Crossovers can be mea...
Automated estimation of the number of contributors in autosomal short tandem repeat profiles using a machine learning approach.
Forensic science international. Genetics
Aug 23, 2019
The number of contributors (NOC) to (complex) autosomal STR profiles cannot be determined with absolute certainty due to complicating factors such as allele sharing and allelic drop-out. The precision of NOC estimations can be improved by increasing ...
Developmental validation of PACE™: Automated artifact identification and contributor estimation for use with GlobalFiler™ and PowerPlex® fusion 6c generated data.
Forensic science international. Genetics
Aug 8, 2019
DNA mixture interpretation remains one of the major challenges in forensic DNA analysis. DNA mixture samples are inherently complex due to several factors including the variations in the quantity of DNA, the presence of non-allelic artifactual peaks ...
Comparative performances of machine learning methods for classifying Crohn Disease patients using genome-wide genotyping data.
Scientific reports
Jul 17, 2019
Crohn Disease (CD) is a complex genetic disorder for which more than 140 genes have been identified using genome wide association studies (GWAS). However, the genetic architecture of the trait remains largely unknown. The recent development of machin...
Humans
Neural Networks, Computer
Female
Male
Deep Learning
Decision Trees
Area Under Curve
Crohn Disease
Nonlinear Dynamics
Alleles
Polymorphism, Single Nucleotide
Genotyping Techniques
Genotype
INDEL Mutation
Genome-Wide Association Study
Logistic Models
ROC Curve
Models, Genetic
Genetic Predisposition to Disease
Estimation of allele-specific fitness effects across human protein-coding sequences and implications for disease.
Genome research
Jun 27, 2019
A central challenge in human genomics is to understand the cellular, evolutionary, and clinical significance of genetic variants. Here, we introduce a unified population-genetic and machine-learning model, called inear llele-pecific election nferenc ...
Detection of the BRAF V600E Mutation in Colorectal Cancer by NIR Spectroscopy in Conjunction with Counter Propagation Artificial Neural Network.
Molecules (Basel, Switzerland)
Jun 15, 2019
This paper proposes a sensitive, sample preparation-free, rapid, and low-cost method for the detection of the B-rapidly accelerated fibrosarcoma (BRAF) gene mutation involving a substitution of valine to glutamic acid at codon 600 (V600E) in colorect...
MHCSeqNet: a deep neural network model for universal MHC binding prediction.
BMC bioinformatics
May 28, 2019
BACKGROUND: Immunotherapy is an emerging approach in cancer treatment that activates the host immune system to destroy cancer cells expressing unique peptide signatures (neoepitopes). Administrations of cancer-specific neoepitopes in the form of synt...
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