Genetics

[From genome analysis to construction of an integrated omics knowledgebase for crops].

The advances in high-throughput technologies have enabled high-speed accumulation of omics data, which contain a large amount of genetic variations and their functional information. The integration and deep utilization of those data will be a long-term and difficult task, which requires highly efficient data storage and powerful data analysis and mining tools. In the past several years, our group ...

HMMRATAC: a Hidden Markov ModeleR for ATAC-seq.

ATAC-seq has been widely adopted to identify accessible chromatin regions across the genome. However, current data analysis still utilizes approaches initially designed for ChIP-seq or DNase-seq, without considering the transposase digested DNA fragments that contain additional nucleosome positioning information. We present the first dedicated ATAC-seq analysis tool, a semi-supervised machine lear...

Compositional data network analysis via lasso penalized D-trace loss.

MOTIVATION: With the development of high-throughput sequencing techniques for 16S-rRNA gene profiling, the analysis of microbial communities is becomi...

Machine learning analysis of DNA methylation profiles distinguishes primary lung squamous cell carcinomas from head and neck metastases.

Head and neck squamous cell carcinoma (HNSC) patients are at risk of suffering from both pulmonary metastases or a second squamous cell carcinoma of t...

Prediction of regulatory motifs from human Chip-sequencing data using a deep learning framework.

The identification of transcription factor binding sites and cis-regulatory motifs is a frontier whereupon the rules governing protein-DNA binding are...

Encoding Clinical Data with the Human Phenotype Ontology for Computational Differential Diagnostics.

The Human Phenotype Ontology (HPO) is a standardized set of phenotypic terms that are organized in a hierarchical fashion. It is a widely used resourc...

Using Machine Learning and Natural Language Processing to Review and Classify the Medical Literature on Cancer Susceptibility Genes.

PURPOSE: The medical literature relevant to germline genetics is growing exponentially. Clinicians need tools that help to monitor and prioritize the ...

Analysis of machine learning algorithms as integrative tools for validation of next generation sequencing data.

OBJECTIVE: While next generation sequencing (NGS) has become the technology of choice for clinical diagnostics, most genetic laboratories still use Sa...

TAGOOS: genome-wide supervised learning of non-coding loci associated to complex phenotypes.

Genome-wide association studies (GWAS) associate single nucleotide polymorphisms (SNPs) to complex phenotypes. Most human SNPs fall in non-coding regi...

Uncovering the mouse olfactory long non-coding transcriptome with a novel machine-learning model.

Very little is known about long non-coding RNAs (lncRNAs) in the mammalian olfactory sensory epithelia. Deciphering the non-coding transcriptome in ol...

Validation of a Semiautomated Natural Language Processing-Based Procedure for Meta-Analysis of Cancer Susceptibility Gene Penetrance.

PURPOSE: Quantifying the risk of cancer associated with pathogenic mutations in germline cancer susceptibility genes-that is, penetrance-enables the p...

BioSeq-Analysis: a platform for DNA, RNA and protein sequence analysis based on machine learning approaches.

With the avalanche of biological sequences generated in the post-genomic age, one of the most challenging problems is how to computationally analyze t...

Artificial intelligence in drug combination therapy.

Currently, the development of medicines for complex diseases requires the development of combination drug therapies. It is necessary because in many c...

Computational functional genomics-based reduction of disease-related gene sets to their key components.

MOTIVATION: The genetic architecture of diseases becomes increasingly known. This raises difficulties in picking suitable targets for further research...

Comprehensive evaluation of deep learning architectures for prediction of DNA/RNA sequence binding specificities.

MOTIVATION: Deep learning architectures have recently demonstrated their power in predicting DNA- and RNA-binding specificity. Existing methods fall i...

Representation transfer for differentially private drug sensitivity prediction.

MOTIVATION: Human genomic datasets often contain sensitive information that limits use and sharing of the data. In particular, simple anonymization st...

GkmExplain: fast and accurate interpretation of nonlinear gapped k-mer SVMs.

SUMMARY: Support Vector Machines with gapped k-mer kernels (gkm-SVMs) have been used to learn predictive models of regulatory DNA sequence. However, i...

Prediction of mRNA subcellular localization using deep recurrent neural networks.

MOTIVATION: Messenger RNA subcellular localization mechanisms play a crucial role in post-transcriptional gene regulation. This trafficking is mediate...

DIFFUSE: predicting isoform functions from sequences and expression profiles via deep learning.

MOTIVATION: Alternative splicing generates multiple isoforms from a single gene, greatly increasing the functional diversity of a genome. Although gen...

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

MOTIVATION: Amyotrophic lateral sclerosis (ALS) is a neurodegenerative disease caused by aberrations in the genome. While several disease-causing vari...