AIMC Topic: Genome
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Brain-specific functional relationship networks inform autism spectrum disorder gene prediction.
Translational psychiatry
Mar 6, 2018
Autism spectrum disorder (ASD) is a neuropsychiatric disorder with strong evidence of genetic contribution, and increased research efforts have resulted in an ever-growing list of ASD candidate genes. However, only a fraction of the hundreds of nomin...
Decontaminating eukaryotic genome assemblies with machine learning.
BMC bioinformatics
Dec 1, 2017
BACKGROUND: High-throughput sequencing has made it theoretically possible to obtain high-quality de novo assembled genome sequences but in practice DNA extracts are often contaminated with sequences from other organisms. Currently, there are few exis...
CNNdel: Calling Structural Variations on Low Coverage Data Based on Convolutional Neural Networks.
BioMed research international
May 28, 2017
Many structural variations (SVs) detection methods have been proposed due to the popularization of next-generation sequencing (NGS). These SV calling methods use different SV-property-dependent features; however, they all suffer from poor accuracy wh...
Multi-Instance Metric Transfer Learning for Genome-Wide Protein Function Prediction.
Scientific reports
Feb 6, 2017
Multi-Instance (MI) learning has been proven to be effective for the genome-wide protein function prediction problems where each training example is associated with multiple instances. Many studies in this literature attempted to find an appropriate ...
acdc - Automated Contamination Detection and Confidence estimation for single-cell genome data.
BMC bioinformatics
Dec 20, 2016
BACKGROUND: A major obstacle in single-cell sequencing is sample contamination with foreign DNA. To guarantee clean genome assemblies and to prevent the introduction of contamination into public databases, considerable quality control efforts are put...
Mirnacle: machine learning with SMOTE and random forest for improving selectivity in pre-miRNA ab initio prediction.
BMC bioinformatics
Dec 15, 2016
BACKGROUND: MicroRNAs (miRNAs) are key gene expression regulators in plants and animals. Therefore, miRNAs are involved in several biological processes, making the study of these molecules one of the most relevant topics of molecular biology nowadays...
Resistance gene identification from Larimichthys crocea with machine learning techniques.
Scientific reports
Dec 6, 2016
The research on resistance genes (R-gene) plays a vital role in bioinformatics as it has the capability of coping with adverse changes in the external environment, which can form the corresponding resistance protein by transcription and translation. ...
Introducing a Stable Bootstrap Validation Framework for Reliable Genomic Signature Extraction.
IEEE/ACM transactions on computational biology and bioinformatics
Nov 29, 2016
The application of machine learning methods for the identification of candidate genes responsible for phenotypes of interest, such as cancer, is a major challenge in the field of bioinformatics. These lists of genes are often called genomic signature...
Semi-supervised learning for genomic prediction of novel traits with small reference populations: an application to residual feed intake in dairy cattle.
Genetics, selection, evolution : GSE
Nov 7, 2016
BACKGROUND: Genomic prediction for novel traits, which can be costly and labor-intensive to measure, is often hampered by low accuracy due to the limited size of the reference population. As an option to improve prediction accuracy, we introduced a s...
Extensive complementarity between gene function prediction methods.
Bioinformatics (Oxford, England)
Aug 13, 2016
MOTIVATION: The number of sequenced genomes rises steadily but we still lack the knowledge about the biological roles of many genes. Automated function prediction (AFP) is thus a necessity. We hypothesized that AFP approaches that draw on distinct ge...
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