AIMC Topic: Sequence Analysis, DNA

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Gene Prediction in Metagenomic Fragments with Deep Learning.

BioMed research international Nov 8, 2017
Next generation sequencing technologies used in metagenomics yield numerous sequencing fragments which come from thousands of different species. Accurately identifying genes from metagenomics fragments is one of the most fundamental issues in metagen...
Databases, Genetic Sequence Analysis, DNA Models, Statistical Metagenomics Machine Learning Genes, Bacterial Bacteria
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Maximum entropy methods for extracting the learned features of deep neural networks.

PLoS computational biology Oct 30, 2017
New architectures of multilayer artificial neural networks and new methods for training them are rapidly revolutionizing the application of machine learning in diverse fields, including business, social science, physical sciences, and biology. Interp...
Computer Simulation Neural Networks, Computer Pattern Recognition, Automated Sequence Analysis, DNA Machine Learning Models, Statistical Entropy Algorithms
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McEnhancer: predicting gene expression via semi-supervised assignment of enhancers to target genes.

Genome biology Oct 26, 2017
Transcriptional enhancers regulate spatio-temporal gene expression. While genomic assays can identify putative enhancers en masse, assigning target genes is a complex challenge. We devised a machine learning approach, McEnhancer, which links target g...
Embryonic Development Promoter Regions, Genetic Drosophila melanogaster Animals Transcription Factors Sequence Analysis, DNA Machine Learning Gene Expression Regulation, Developmental Histone Code Deoxyribonuclease I Nucleotide Motifs Enhancer Elements, Genetic Genes, Reporter
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Selection plays the hand it was dealt: evidence that human adaptation commonly targets standing genetic variation.

Genome biology Aug 1, 2017
Using a powerful machine learning approach, a recent study of human genomes has revealed widespread footprints of recent positive selection on standing genetic variation.
Humans Machine Learning Models, Genetic Selection, Genetic Linkage Disequilibrium Biological Evolution Sequence Analysis, DNA Genetic Variation Genome, Human Adaptation, Biological Genetics, Population
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DeepNano: Deep recurrent neural networks for base calling in MinION nanopore reads.

PloS one Jun 5, 2017
The MinION device by Oxford Nanopore produces very long reads (reads over 100 kBp were reported); however it suffers from high sequencing error rate. We present an open-source DNA base caller based on deep recurrent neural networks and show that the ...
Neural Networks, Computer Escherichia coli Software Sequence Analysis, DNA High-Throughput Nucleotide Sequencing Datasets as Topic Nanopores Klebsiella pneumoniae
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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...
Sequence Analysis, DNA Neural Networks, Computer Genome
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A machine learning approach for viral genome classification.

BMC bioinformatics Apr 11, 2017
BACKGROUND: Advances in cloning and sequencing technology are yielding a massive number of viral genomes. The classification and annotation of these genomes constitute important assets in the discovery of genomic variability, taxonomic characteristic...
Classification Papillomaviridae Hepatitis B virus Genomics Sequence Analysis, RNA Sequence Analysis, DNA HIV-1 Genome, Viral Humans Machine Learning Computer Simulation
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Next-Generation Global Biomonitoring: Large-scale, Automated Reconstruction of Ecological Networks.

Trends in ecology & evolution Mar 27, 2017
We foresee a new global-scale, ecological approach to biomonitoring emerging within the next decade that can detect ecosystem change accurately, cheaply, and generically. Next-generation sequencing of DNA sampled from the Earth's environments would p...
Machine Learning Ecology DNA Ecosystem Biodiversity Environmental Monitoring High-Throughput Nucleotide Sequencing Sequence Analysis, DNA
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An artificial intelligence approach fit for tRNA gene studies in the era of big sequence data.

Genes & genetic systems Mar 24, 2017
Unsupervised data mining capable of extracting a wide range of knowledge from big data without prior knowledge or particular models is a timely application in the era of big sequence data accumulation in genome research. By handling oligonucleotide c...
Sequence Analysis, DNA RNA, Transfer Humans Software Artificial Intelligence Animals Genomics
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Prediction of virus-host infectious association by supervised learning methods.

BMC bioinformatics Mar 14, 2017
BACKGROUND: The study of virus-host infectious association is important for understanding the functions and dynamics of microbial communities. Both cellular and fractionated viral metagenomic data generate a large number of viral contigs with missing...
Metagenomics Host-Pathogen Interactions Genome, Viral Likelihood Functions Viruses DNA, Viral Sequence Analysis, DNA Bayes Theorem ROC Curve Models, Theoretical Reproducibility of Results Bacteria Support Vector Machine Logistic Models
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