AIMC Topic: Polymorphism, Single Nucleotide

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Computational and artificial neural network based study of functional SNPs of human LEPR protein associated with reproductive function.

Journal of cellular biochemistry
Genetic polymorphisms are mostly associated with inherited diseases, detecting and analyzing the biological significance of functional single-nucleotide polymorphisms (SNPs) using wet laboratory experiments is an arduous task hence the computational ...

Analysis of the Deleterious Single Nucleotide Polymorphisms Impact on Estrogen Receptor Alpha-p53 Interaction: A Machine Learning Approach.

International journal of molecular sciences
Breast cancer is a leading cancer type and one of the major health issues faced by women around the world. Some of its major risk factors include body mass index, hormone replacement therapy, family history and germline mutations. Of these risk facto...

fastJT: An R package for robust and efficient feature selection for machine learning and genome-wide association studies.

BMC bioinformatics
BACKGROUND: Parametric feature selection methods for machine learning and association studies based on genetic data are not robust with respect to outliers or influential observations. While rank-based, distribution-free statistics offer a robust alt...

Pre-Treatment Biomarkers of Anti-Tumour Necrosis Factor Therapy Response in Crohn's Disease-A Systematic Review and Gene Ontology Analysis.

Cells
The most prominent treatment for the serious cases of Crohn's disease (CD) are biological tumour necrosis factor (TNF) inhibitors. Unfortunately, therapy nonresponse is still a serious issue in ~1/3 of CD patients. Accurate prediction of responsivene...

A directed learning strategy integrating multiple omic data improves genomic prediction.

Plant biotechnology journal
Genomic prediction (GP) aims to construct a statistical model for predicting phenotypes using genome-wide markers and is a promising strategy for accelerating molecular plant breeding. However, current progress of phenotype prediction using genomic d...

DeepHistone: a deep learning approach to predicting histone modifications.

BMC genomics
MOTIVATION: Quantitative detection of histone modifications has emerged in the recent years as a major means for understanding such biological processes as chromosome packaging, transcriptional activation, and DNA damage. However, high-throughput exp...

Application of Single-Nucleotide Polymorphisms in the Diagnosis of Autism Spectrum Disorders: A Preliminary Study with Artificial Neural Networks.

Journal of molecular neuroscience : MN
Autism spectrum disorder (ASD) includes different neurodevelopmental disorders characterized by deficits in social communication, and restricted, repetitive patterns of behavior, interests or activities. Based on the importance of early diagnosis for...

A multi-task convolutional deep neural network for variant calling in single molecule sequencing.

Nature communications
The accurate identification of DNA sequence variants is an important, but challenging task in genomics. It is particularly difficult for single molecule sequencing, which has a per-nucleotide error rate of ~5-15%. Meeting this demand, we developed Cl...

ClinTAD: a tool for copy number variant interpretation in the context of topologically associated domains.

Journal of human genetics
Standard clinical interpretation of DNA copy number variants (CNVs) identified by cytogenomic microarray involves examining protein-coding genes within the region and comparison to other CNVs. Emerging basic research suggests that CNVs can also exert...