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Genomics

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Deciphering microbial gene function using natural language processing.

Nature communications
Revealing the function of uncharacterized genes is a fundamental challenge in an era of ever-increasing volumes of sequencing data. Here, we present a concept for tackling this challenge using deep learning methodologies adopted from natural language...

Cooperative learning for multiview analysis.

Proceedings of the National Academy of Sciences of the United States of America
We propose a method for supervised learning with multiple sets of features ("views"). The multiview problem is especially important in biology and medicine, where "-omics" data, such as genomics, proteomics, and radiomics, are measured on a common se...

Multinomial Convolutions for Joint Modeling of Regulatory Motifs and Sequence Activity Readouts.

Genes
A common goal in the convolutional neural network (CNN) modeling of genomic data is to discover specific sequence motifs. Post hoc analysis methods aid in this task but are dependent on parameters whose optimal values are unclear and applying the dis...

Smart breeding driven by big data, artificial intelligence, and integrated genomic-enviromic prediction.

Molecular plant
The first paradigm of plant breeding involves direct selection-based phenotypic observation, followed by predictive breeding using statistical models for quantitative traits constructed based on genetic experimental design and, more recently, by inco...

Artificial Intelligence Applied to Cardiomyopathies: Is It Time for Clinical Application?

Current cardiology reports
PURPOSE OF REVIEW: Artificial intelligence (AI) techniques have the potential to remarkably change the practice of cardiology in order to improve and optimize outcomes in heart failure and specifically cardiomyopathies, offering us novel tools to int...

Taxonomic classification of DNA sequences beyond sequence similarity using deep neural networks.

Proceedings of the National Academy of Sciences of the United States of America
Taxonomic classification, that is, the assignment to biological clades with shared ancestry, is a common task in genetics, mainly based on a genome similarity search of large genome databases. The classification quality depends heavily on the databas...

Image-based deep learning identifies glioblastoma risk groups with genomic and transcriptomic heterogeneity: a multi-center study.

European radiology
OBJECTIVES: To develop and validate a deep learning imaging signature (DLIS) for risk stratification in patients with multiforme (GBM), and to investigate the biological pathways and genetic alterations underlying the DLIS.

Model-free prediction test with application to genomics data.

Proceedings of the National Academy of Sciences of the United States of America
Testing the significance of predictors in a regression model is one of the most important topics in statistics. This problem is especially difficult without any parametric assumptions on the data. This paper aims to test the null hypothesis that give...

ZayyuNet - A Unified Deep Learning Model for the Identification of Epigenetic Modifications Using Raw Genomic Sequences.

IEEE/ACM transactions on computational biology and bioinformatics
Epigenetic modifications have a vital role in gene expression and are linked to cellular processes such as differentiation, development, and tumorigenesis. Thus, the availability of reliable and accurate methods for identifying and defining these cha...

Link Prediction Based on Stochastic Information Diffusion.

IEEE transactions on neural networks and learning systems
Link prediction (LP) in networks aims at determining future interactions among elements; it is a critical machine-learning tool in different domains, ranging from genomics to social networks to marketing, especially in e-commerce recommender systems....