AIMC Topic: Genome

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Improved survival analysis by learning shared genomic information from pan-cancer data.

Bioinformatics (Oxford, England)
MOTIVATION: Recent advances in deep learning have offered solutions to many biomedical tasks. However, there remains a challenge in applying deep learning to survival analysis using human cancer transcriptome data. As the number of genes, the input v...

Genome-wide prediction for complex traits under the presence of dominance effects in simulated populations using GBLUP and machine learning methods.

Journal of animal science
The aim of this study was to compare the predictive performance of the Genomic Best Linear Unbiased Predictor (GBLUP) and machine learning methods (Random Forest, RF; Support Vector Machine, SVM; Artificial Neural Network, ANN) in simulated populatio...

BiGG Models 2020: multi-strain genome-scale models and expansion across the phylogenetic tree.

Nucleic acids research
The BiGG Models knowledge base (http://bigg.ucsd.edu) is a centralized repository for high-quality genome-scale metabolic models. For the past 12 years, the website has allowed users to browse and search metabolic models. Within this update, we detai...

The Genome3D Consortium for Structural Annotations of Selected Model Organisms.

Methods in molecular biology (Clifton, N.J.)
Genome3D consortium is a collaborative project involving protein structure prediction and annotation resources developed by six world-leading structural bioinformatics groups, based in the United Kingdom (namely Blundell, Murzin, Gough, Sternberg, Or...

Scaling tree-based automated machine learning to biomedical big data with a feature set selector.

Bioinformatics (Oxford, England)
MOTIVATION: Automated machine learning (AutoML) systems are helpful data science assistants designed to scan data for novel features, select appropriate supervised learning models and optimize their parameters. For this purpose, Tree-based Pipeline O...

Machine learning meets genome assembly.

Briefings in bioinformatics
MOTIVATION: With the recent advances in DNA sequencing technologies, the study of the genetic composition of living organisms has become more accessible for researchers. Several advances have been achieved because of it, especially in the health scie...

HiCNN: a very deep convolutional neural network to better enhance the resolution of Hi-C data.

Bioinformatics (Oxford, England)
MOTIVATION: High-resolution Hi-C data are indispensable for the studies of three-dimensional (3D) genome organization at kilobase level. However, generating high-resolution Hi-C data (e.g. 5 kb) by conducting Hi-C experiments needs millions of mammal...

Using the structure of genome data in the design of deep neural networks for predicting amyotrophic lateral sclerosis from genotype.

Bioinformatics (Oxford, England)
MOTIVATION: Amyotrophic lateral sclerosis (ALS) is a neurodegenerative disease caused by aberrations in the genome. While several disease-causing variants have been identified, a major part of heritability remains unexplained. ALS is believed to have...