Deep learning: new computational modelling techniques for genomics
As a data-driven science, genomics largely utilizes machine learning to capture
dependencies in data and derive novel biological hypotheses. However, the ability to extract …
dependencies in data and derive novel biological hypotheses. However, the ability to extract …
Machine learning in genomic medicine: a review of computational problems and data sets
In this paper, we provide an introduction to machine learning tasks that address important
problems in genomic medicine. One of the goals of genomic medicine is to determine how …
problems in genomic medicine. One of the goals of genomic medicine is to determine how …
Predicting effects of noncoding variants with deep learning–based sequence model
J Zhou, OG Troyanskaya - Nature methods, 2015 - nature.com
Identifying functional effects of noncoding variants is a major challenge in human genetics.
To predict the noncoding-variant effects de novo from sequence, we developed a deep …
To predict the noncoding-variant effects de novo from sequence, we developed a deep …
Effective gene expression prediction from sequence by integrating long-range interactions
How noncoding DNA determines gene expression in different cell types is a major unsolved
problem, and critical downstream applications in human genetics depend on improved …
problem, and critical downstream applications in human genetics depend on improved …
An automated framework for efficiently designing deep convolutional neural networks in genomics
Convolutional neural networks (CNNs) have become a standard for analysis of biological
sequences. Tuning of network architectures is essential for a CNN's performance, yet it …
sequences. Tuning of network architectures is essential for a CNN's performance, yet it …
Deep learning of genomic variation and regulatory network data
The human genome is now investigated through high-throughput functional assays, and
through the generation of population genomic data. These advances support the …
through the generation of population genomic data. These advances support the …
Benchmarking of deep neural networks for predicting personal gene expression from DNA sequence highlights shortcomings
Deep learning methods have recently become the state of the art in a variety of regulatory
genomic tasks,,,,–, including the prediction of gene expression from genomic DNA. As such …
genomic tasks,,,,–, including the prediction of gene expression from genomic DNA. As such …
Deep learning sequence-based ab initio prediction of variant effects on expression and disease risk
Key challenges for human genetics, precision medicine and evolutionary biology include
deciphering the regulatory code of gene expression and understanding the transcriptional …
deciphering the regulatory code of gene expression and understanding the transcriptional …
Cross-species regulatory sequence activity prediction
DR Kelley - PLoS computational biology, 2020 - journals.plos.org
Machine learning algorithms trained to predict the regulatory activity of nucleic acid
sequences have revealed principles of gene regulation and guided genetic variation …
sequences have revealed principles of gene regulation and guided genetic variation …
[HTML][HTML] Deep learning for inferring transcription factor binding sites
PK Koo, M Ploenzke - Current opinion in systems biology, 2020 - Elsevier
Deep learning is a powerful tool for predicting transcription factor binding sites from DNA
sequence. Despite their high predictive accuracy, there are no guarantees that a high …
sequence. Despite their high predictive accuracy, there are no guarantees that a high …