AIMC Topic: Genome, Human
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Ranking of non-coding pathogenic variants and putative essential regions of the human genome.
Nature communications
Nov 20, 2019
A gene is considered essential if loss of function results in loss of viability, fitness or in disease. This concept is well established for coding genes; however, non-coding regions are thought less likely to be determinants of critical functions. H...
Dysmorphology in a Genomic Era.
Clinics in perinatology
Oct 31, 2019
Dysmorphology is the practice of defining the morphologic phenotype of syndromic disorders. Genomic sequencing has advanced our understanding of human variation and molecular dysmorphology has evolved in response to the science of relating embryologi...
Machine learning enables detection of early-stage colorectal cancer by whole-genome sequencing of plasma cell-free DNA.
BMC cancer
Aug 23, 2019
BACKGROUND: Blood-based methods using cell-free DNA (cfDNA) are under development as an alternative to existing screening tests. However, early-stage detection of cancer using tumor-derived cfDNA has proven challenging because of the small proportion...
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 DNA base modifications by deep recurrent neural network on Oxford Nanopore sequencing data.
Nature communications
Jun 4, 2019
DNA base modifications, such as C5-methylcytosine (5mC) and N6-methyldeoxyadenosine (6mA), are important types of epigenetic regulations. Short-read bisulfite sequencing and long-read PacBio sequencing have inherent limitations to detect DNA modifica...
Pre-Treatment Biomarkers of Anti-Tumour Necrosis Factor Therapy Response in Crohn's Disease-A Systematic Review and Gene Ontology Analysis.
Cells
May 28, 2019
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...
Whole-genome deep-learning analysis identifies contribution of noncoding mutations to autism risk.
Nature genetics
May 27, 2019
We address the challenge of detecting the contribution of noncoding mutations to disease with a deep-learning-based framework that predicts the specific regulatory effects and the deleterious impact of genetic variants. Applying this framework to 1,7...
Gene Expression Regulation
Genome, Human
Transcription, Genetic
RNA, Untranslated
Genes, Reporter
RNA Processing, Post-Transcriptional
Computational Biology
Gene Expression
Deep Learning
Mutation
Humans
Genomics
Alleles
Algorithms
Genetic Predisposition to Disease
Phenotype
Genetic Association Studies
Autism Spectrum Disorder
Recognition of 3'-end L1, Alu, processed pseudogenes, and mRNA stem-loops in the human genome using sequence-based and structure-based machine-learning models.
Scientific reports
May 10, 2019
The role of 3'-end stem-loops in retrotransposition was experimentally demonstrated for transposons of various species, where LINE-SINE retrotransposons share the same 3'-end sequences, containing a stem-loop. We have discovered that 62-68% of proces...
Association of genomic subtypes of lower-grade gliomas with shape features automatically extracted by a deep learning algorithm.
Computers in biology and medicine
May 3, 2019
Recent analysis identified distinct genomic subtypes of lower-grade glioma tumors which are associated with shape features. In this study, we propose a fully automatic way to quantify tumor imaging characteristics using deep learning-based segmentati...
Machine learning technology in the application of genome analysis: A systematic review.
Gene
Apr 23, 2019
Machine learning (ML) is a powerful technique to tackle many problems in data mining and predictive analytics. We believe that ML will be of considerable potentials in the field of bioinformatics since the high-throughput technology is producing ever...
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