AIMC Topic: Sequence Analysis, DNA

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AnomiGAN: Generative Adversarial Networks for Anonymizing Private Medical Data.

Pacific Symposium on Biocomputing. Pacific Symposium on Biocomputing
Typical personal medical data contains sensitive information about individuals. Storing or sharing the personal medical data is thus often risky. For example, a short DNA sequence can provide information that can identify not only an individual, but ...

Development of Genome-Derived Tumor Type Prediction to Inform Clinical Cancer Care.

JAMA oncology
IMPORTANCE: Diagnosing the site of origin for cancer is a pillar of disease classification that has directed clinical care for more than a century. Even in an era of precision oncologic practice, in which treatment is increasingly informed by the pre...

DeePaC: predicting pathogenic potential of novel DNA with reverse-complement neural networks.

Bioinformatics (Oxford, England)
MOTIVATION: We expect novel pathogens to arise due to their fast-paced evolution, and new species to be discovered thanks to advances in DNA sequencing and metagenomics. Moreover, recent developments in synthetic biology raise concerns that some stra...

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...

Functional interpretation of genetic variants using deep learning predicts impact on chromatin accessibility and histone modification.

Nucleic acids research
Identifying functional variants underlying disease risk and adoption of personalized medicine are currently limited by the challenge of interpreting the functional consequences of genetic variants. Predicting the functional effects of disease-associa...

HMMRATAC: a Hidden Markov ModeleR for ATAC-seq.

Nucleic acids research
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 fragm...

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

European review for medical and pharmacological sciences
OBJECTIVE: While next generation sequencing (NGS) has become the technology of choice for clinical diagnostics, most genetic laboratories still use Sanger sequencing for orthogonal confirmation of NGS results. Previous studies have shown that when th...

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

Briefings in bioinformatics
With the avalanche of biological sequences generated in the post-genomic age, one of the most challenging problems is how to computationally analyze their structures and functions. Machine learning techniques are playing key roles in this field. Typi...

AIControl: replacing matched control experiments with machine learning improves ChIP-seq peak identification.

Nucleic acids research
ChIP-seq is a technique to determine binding locations of transcription factors, which remains a central challenge in molecular biology. Current practice is to use a 'control' dataset to remove background signals from a immunoprecipitation (IP) 'targ...

DeepGSR: an optimized deep-learning structure for the recognition of genomic signals and regions.

Bioinformatics (Oxford, England)
MOTIVATION: Recognition of different genomic signals and regions (GSRs) in DNA is crucial for understanding genome organization, gene regulation, and gene function, which in turn generate better genome and gene annotations. Although many methods have...