AIMC Topic: Sequence Analysis, DNA
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Deep convolutional neural networks for accurate somatic mutation detection.
Nature communications
Mar 4, 2019
Accurate detection of somatic mutations is still a challenge in cancer analysis. Here we present NeuSomatic, the first convolutional neural network approach for somatic mutation detection, which significantly outperforms previous methods on different...
Machine learning approaches outperform distance- and tree-based methods for DNA barcoding of Pterocarpus wood.
Planta
Mar 1, 2019
Machine-learning approaches (MLAs) for DNA barcoding outperform distance- and tree-based methods on identification accuracy and cost-effectiveness to arrive at species-level identification of wood. DNA barcoding is a promising tool to combat illegal ...
A multi-task convolutional deep neural network for variant calling in single molecule sequencing.
Nature communications
Mar 1, 2019
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...
iEnhancer-5Step: Identifying enhancers using hidden information of DNA sequences via Chou's 5-step rule and word embedding.
Analytical biochemistry
Feb 26, 2019
An enhancer is a short (50-1500bp) region of DNA that plays an important role in gene expression and the production of RNA and proteins. Genetic variation in enhancers has been linked to many human diseases, such as cancer, disorder or inflammatory b...
ClinTAD: a tool for copy number variant interpretation in the context of topologically associated domains.
Journal of human genetics
Feb 14, 2019
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...
A Multi-Label Supervised Topic Model Conditioned on Arbitrary Features for Gene Function Prediction.
Genes
Jan 17, 2019
With the continuous accumulation of biological data, more and more machine learning algorithms have been introduced into the field of gene function prediction, which has great significance in decoding the secret of life. Recently, a multi-label super...
Annotation of gene product function from high-throughput studies using the Gene Ontology.
Database : the journal of biological databases and curation
Jan 1, 2019
High-throughput studies constitute an essential and valued source of information for researchers. However, high-throughput experimental workflows are often complex, with multiple data sets that may contain large numbers of false positives. The repres...
Physicochemical property based computational scheme for classifying DNA sequence elements of Saccharomyces cerevisiae.
Computational biology and chemistry
Dec 26, 2018
GenerationE of huge "omics" data necessitates the development and application of computational methods to annotate the data in terms of biological features. In the context of DNA sequence, it is important to unravel the hidden physicochemical signatu...
Multi-trait, Multi-environment Deep Learning Modeling for Genomic-Enabled Prediction of Plant Traits.
G3 (Bethesda, Md.)
Dec 10, 2018
Multi-trait and multi-environment data are common in animal and plant breeding programs. However, what is lacking are more powerful statistical models that can exploit the correlation between traits to improve prediction accuracy in the context of ge...
Multi-environment Genomic Prediction of Plant Traits Using Deep Learners With Dense Architecture.
G3 (Bethesda, Md.)
Dec 10, 2018
Genomic selection is revolutionizing plant breeding and therefore methods that improve prediction accuracy are useful. For this reason, active research is being conducted to build and test methods from other areas and adapt them to the context of gen...
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