AIMC Topic: Sequence Analysis, DNA

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Hierarchical Hidden Markov models enable accurate and diverse detection of antimicrobial resistance sequences.

Communications biology Aug 6, 2019
The characterization of antimicrobial resistance genes from high-throughput sequencing data has become foundational in public health research and regulation. This requires mapping sequence reads to databases of known antimicrobial resistance genes to...
High-Throughput Nucleotide Sequencing Markov Chains Metagenomics Sequence Analysis, DNA Machine Learning Drug Resistance, Microbial Reproducibility of Results Data Mining Databases, Genetic
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Deep Splicing Code: Classifying Alternative Splicing Events Using Deep Learning.

Genes Aug 1, 2019
Alternative splicing (AS) is the process of combining different parts of the pre-mRNA to produce diverse transcripts and eventually different protein products from a single gene. In computational biology field, researchers try to understand AS behavi...
Humans Deep Learning Sequence Analysis, DNA Alternative Splicing
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Cancer classification and pathway discovery using non-negative matrix factorization.

Journal of biomedical informatics Jul 2, 2019
OBJECTIVES: Extracting genetic information from a full range of sequencing data is important for understanding disease. We propose a novel method to effectively explore the landscape of genetic mutations and aggregate them to predict cancer type.
Support Vector Machine Humans Regression Analysis Databases, Genetic Neoplasms Pilot Projects Algorithms Diagnosis, Computer-Assisted Sequence Analysis, DNA Mutation Cell Line, Tumor Reproducibility of Results Gene Expression Regulation, Neoplastic Exome
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Machine learning to predict microbial community functions: An analysis of dissolved organic carbon from litter decomposition.

PloS one Jul 1, 2019
Microbial communities are ubiquitous and often influence macroscopic properties of the ecosystems they inhabit. However, deciphering the functional relationship between specific microbes and ecosystem properties is an ongoing challenge owing to the c...
Sequence Analysis, DNA Machine Learning Carbon Bacteria Microbiota RNA, Ribosomal, 16S
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Performance of neural network basecalling tools for Oxford Nanopore sequencing.

Genome biology Jun 24, 2019
BACKGROUND: Basecalling, the computational process of translating raw electrical signal to nucleotide sequence, is of critical importance to the sequencing platforms produced by Oxford Nanopore Technologies (ONT). Here, we examine the performance of ...
Sequence Analysis, DNA Neural Networks, Computer Nanopores Software Klebsiella pneumoniae
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Machine learning performance in a microbial molecular autopsy context: A cross-sectional postmortem human population study.

PloS one Apr 15, 2019
BACKGROUND: The postmortem microbiome can provide valuable information to a death investigation and to the human health of the once living. Microbiome sequencing produces, in general, large multi-dimensional datasets that can be difficult to analyze ...
Autopsy High-Throughput Nucleotide Sequencing Postmortem Changes Datasets as Topic DNA, Archaeal Humans RNA, Ribosomal, 16S Machine Learning Sequence Analysis, DNA Female Microbiota Adolescent Adult Aged Time Factors Male Cross-Sectional Studies Aged, 80 and over DNA, Bacterial Middle Aged Young Adult
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MRCNN: a deep learning model for regression of genome-wide DNA methylation.

BMC genomics Apr 4, 2019
BACKGROUND: Determination of genome-wide DNA methylation is significant for both basic research and drug development. As a key epigenetic modification, this biochemical process can modulate gene expression to influence the cell differentiation which ...
Models, Statistical Regression Analysis CpG Islands Epigenesis, Genetic Sequence Analysis, DNA DNA Methylation Genome, Human Deep Learning Genomics Humans Algorithms
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Predicting DNA Methylation States with Hybrid Information Based Deep-Learning Model.

IEEE/ACM transactions on computational biology and bioinformatics Apr 3, 2019
DNA methylation plays an important role in the regulation of some biological processes. Up to now, with the development of machine learning models, there are several sequence-based deep learning models designed to predict DNA methylation states, whic...
Cell Line, Tumor Computational Biology Sequence Analysis, DNA DNA Methylation Humans Deep Learning DNA High-Throughput Nucleotide Sequencing Histone Code
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Selene: a PyTorch-based deep learning library for sequence data.

Nature methods Mar 28, 2019
To enable the application of deep learning in biology, we present Selene (https://selene.flatironinstitute.org/), a PyTorch-based deep learning library for fast and easy development, training, and application of deep learning model architectures for ...
Humans Neural Networks, Computer Gene Library Models, Statistical Area Under Curve Mutation Alzheimer Disease Mutagenesis Genomics Software Programming Languages Normal Distribution Computational Biology Algorithms Sequence Analysis, DNA Deep Learning
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Evolutionarily informed deep learning methods for predicting relative transcript abundance from DNA sequence.

Proceedings of the National Academy of Sciences of the United States of America Mar 6, 2019
Deep learning methodologies have revolutionized prediction in many fields and show potential to do the same in molecular biology and genetics. However, applying these methods in their current forms ignores evolutionary dependencies within biological ...
Gene Expression Regulation Base Sequence Transcription, Genetic Sequence Analysis, DNA Evolution, Molecular DNA Deep Learning Models, Theoretical
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