Genetics

Deep Learning on Chromatin Accessibility.

DNA accessibility has been a powerful tool in locating active regulatory elements in a cell type, but dissecting the combinatorial logic within these regulatory elements has been a continued challenge in the field. Deep learning models have been shown to be highly predictive models of regulatory DNA and have led to new biological insights on regulatory syntax and logic. Here, we provide a framewor...

DeepZ: A Deep Learning Approach for Z-DNA Prediction.

Here we describe an approach that uses deep learning neural networks such as CNN and RNN to aggregate information from DNA sequence; physical, chemical, and structural properties of nucleotides; and omics data on histone modifications, methylation, chromatin accessibility, and transcription factor binding sites and data from other available NGS experiments. We explain how with the trained model on...

Robotic DNA Extraction Utilizing Qiagen BioSprint 96 Workstation.

After an examination of evidentiary or reference samples has been performed, the next step is DNA extraction. This crucial step allows for deoxyribonu...

Network Biology and Medicine to Rescue: Applications for Retinal Disease Mechanisms and Therapy.

Inherited retinal degenerations (IRDs) are clinically and genetically heterogenous blinding diseases that manifest through dysfunction of target cells...

A Transferable Machine Learning Framework for Predicting Transcriptional Responses of Genes Across Species.

Leveraging existing resources in studied species to predict gene functions has the potential to rapidly expand understanding of annotated genes in oth...

Accurate and fast clade assignment via deep learning and frequency chaos game representation.

BACKGROUND: Since the beginning of the coronavirus disease 2019 pandemic, there has been an explosion of sequencing of the severe acute respiratory sy...

Defining the extent of gene function using ROC curvature.

MOTIVATION: Interactions between proteins help us understand how genes are functionally related and how they contribute to phenotypes. Experiments pro...

DeepPerVar: a multi-modal deep learning framework for functional interpretation of genetic variants in personal genome.

MOTIVATION: Understanding the functional consequence of genetic variants, especially the non-coding ones, is important but particularly challenging. G...

E-SNPs&GO: embedding of protein sequence and function improves the annotation of human pathogenic variants.

MOTIVATION: The advent of massive DNA sequencing technologies is producing a huge number of human single-nucleotide polymorphisms occurring in protein...

Optimization scheme of machine learning model for genetic division between northern Han, southern Han, Korean and Japanese.

Han Chinese, Korean and Japanese are the main populations of East Asia, and Han Chinese presents a gradient admixture from north to south. There are d...

dynDeepDRIM: a dynamic deep learning model to infer direct regulatory interactions using time-course single-cell gene expression data.

Time-course single-cell RNA sequencing (scRNA-seq) data have been widely used to explore dynamic changes in gene expression of transcription factors (...

ILGBMSH: an interpretable classification model for the shRNA target prediction with ensemble learning algorithm.

Short hairpin RNA (shRNA)-mediated gene silencing is an important technology to achieve RNA interference, in which the design of potent and reliable s...

ICSDA: a multi-modal deep learning model to predict breast cancer recurrence and metastasis risk by integrating pathological, clinical and gene expression data.

Breast cancer patients often have recurrence and metastasis after surgery. Predicting the risk of recurrence and metastasis for a breast cancer patien...

Predicting colorectal cancer tumor mutational burden from histopathological images and clinical information using multi-modal deep learning.

MOTIVATION: Tumor mutational burden (TMB) is an indicator of the efficacy and prognosis of immune checkpoint therapy in colorectal cancer (CRC). In ge...

Deep learning-assisted genome-wide characterization of massively parallel reporter assays.

Massively parallel reporter assay (MPRA) is a high-throughput method that enables the study of the regulatory activities of tens of thousands of DNA o...

learnMET: an R package to apply machine learning methods for genomic prediction using multi-environment trial data.

We introduce the R-package learnMET, developed as a flexible framework to enable a collection of analyses on multi-environment trial breeding data wit...

BayeStab: Predicting effects of mutations on protein stability with uncertainty quantification.

Predicting protein thermostability change upon mutation is crucial for understanding diseases and designing therapeutics. However, accurately estimati...

Interpretable deep learning for chromatin-informed inference of transcriptional programs driven by somatic alterations across cancers.

Cancer is a disease of gene dysregulation, where cells acquire somatic and epigenetic alterations that drive aberrant cellular signaling. These altera...

Disease classification for whole-blood DNA methylation: Meta-analysis, missing values imputation, and XAI.

BACKGROUND: DNA methylation has a significant effect on gene expression and can be associated with various diseases. Meta-analysis of available DNA me...

DeepToA: an ensemble deep-learning approach to predicting the theater of activity of a microbiome.

MOTIVATION: Metagenomics is the study of microbiomes using DNA sequencing. A microbiome consists of an assemblage of microbes that is associated with ...