AIMC Topic: Epigenomics
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A convolution based computational approach towards DNA N6-methyladenine site identification and motif extraction in rice genome.
Scientific reports
May 14, 2021
DNA N6-methylation (6mA) in Adenine nucleotide is a post replication modification responsible for many biological functions. Automated and accurate computational methods can help to identify 6mA sites in long genomes saving significant time and money...
Artificial intelligence and leukocyte epigenomics: Evaluation and prediction of late-onset Alzheimer's disease.
PloS one
Mar 31, 2021
We evaluated the utility of leucocyte epigenomic-biomarkers for Alzheimer's Disease (AD) detection and elucidates its molecular pathogeneses. Genome-wide DNA methylation analysis was performed using the Infinium MethylationEPIC BeadChip array in 24 l...
Epigenetic Target Fishing with Accurate Machine Learning Models.
Journal of medicinal chemistry
Mar 26, 2021
Epigenetic targets are of significant importance in drug discovery research, as demonstrated by the eight approved epigenetic drugs for treatment of cancer and the increasing availability of chemogenomic data related to epigenetics. This data represe...
Integrated multi-omics analysis of ovarian cancer using variational autoencoders.
Scientific reports
Mar 18, 2021
Cancer is a complex disease that deregulates cellular functions at various molecular levels (e.g., DNA, RNA, and proteins). Integrated multi-omics analysis of data from these levels is necessary to understand the aberrant cellular functions accountab...
Deep learning-based enhancement of epigenomics data with AtacWorks.
Nature communications
Mar 8, 2021
ATAC-seq is a widely-applied assay used to measure genome-wide chromatin accessibility; however, its ability to detect active regulatory regions can depend on the depth of sequencing coverage and the signal-to-noise ratio. Here we introduce AtacWorks...
Systems Approach to Pathogenic Mechanism of Type 2 Diabetes and Drug Discovery Design Based on Deep Learning and Drug Design Specifications.
International journal of molecular sciences
Dec 26, 2020
In this study, we proposed a systems biology approach to investigate the pathogenic mechanism for identifying significant biomarkers as drug targets and a systematic drug discovery strategy to design a potential multiple-molecule targeting drug for t...
A pitfall for machine learning methods aiming to predict across cell types.
Genome biology
Nov 19, 2020
Machine learning models that predict genomic activity are most useful when they make accurate predictions across cell types. Here, we show that when the training and test sets contain the same genomic loci, the resulting model may falsely appear to p...
SVFX: a machine learning framework to quantify the pathogenicity of structural variants.
Genome biology
Nov 9, 2020
There is a lack of approaches for identifying pathogenic genomic structural variants (SVs) although they play a crucial role in many diseases. We present a mechanism-agnostic machine learning-based workflow, called SVFX, to assign pathogenicity score...
Deep learning approach for predicting functional Z-DNA regions using omics data.
Scientific reports
Nov 5, 2020
Computational methods to predict Z-DNA regions are in high demand to understand the functional role of Z-DNA. The previous state-of-the-art method Z-Hunt is based on statistical mechanical and energy considerations about B- to Z-DNA transition using ...
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