Genetics

Off the deep end: What can deep learning do for the gene expression field?

After a COVID-related hiatus, the fifth biennial symposium on Evolution and Core Processes in Gene Regulation met at the Stowers Institute in Kansas City, Missouri July 21 to 24, 2022. This symposium, sponsored by the American Society for Biochemistry and Molecular Biology (ASBMB), featured experts in gene regulation and evolutionary biology. Topic areas covered enhancer evolution, the cis-regulat...

Temporal Network Embedding for Link Prediction via VAE Joint Attention Mechanism.

Network representation learning or embedding aims to project the network into a low-dimensional space that can be devoted to different network tasks. Temporal networks are an important type of network whose topological structure changes over time. Compared with methods on static networks, temporal network embedding (TNE) methods are facing three challenges: 1) it cannot describe the temporal depen...

CFA: An explainable deep learning model for annotating the transcriptional roles of cis-regulatory modules based on epigenetic codes.

Metazoa gene expression is controlled by modular DNA segments called cis-regulatory modules (CRMs). CRMs can convey promoter/enhancer/insulator roles,...

CNN-Pred: Prediction of single-stranded and double-stranded DNA-binding protein using convolutional neural networks.

DNA-binding proteins play a vital role in biological activity including DNA replication, DNA packing, and DNA reparation. DNA-binding proteins can be ...

High-confidence cancer patient stratification through multiomics investigation of DNA repair disorders.

Multiple cancer types have limited targeted therapeutic options, in part due to incomplete understanding of the molecular processes underlying tumorig...

DrugnomeAI is an ensemble machine-learning framework for predicting druggability of candidate drug targets.

The druggability of targets is a crucial consideration in drug target selection. Here, we adopt a stochastic semi-supervised ML framework to develop D...

Sperm-cell DNA fragmentation prediction using label-free quantitative phase imaging and deep learning.

In intracytoplasmic sperm injection (ICSI), a single sperm cell is selected and injected into an egg. The quality of the chosen sperm and specifically...

Survival prediction of stomach cancer using expression data and deep learning models with histopathological images.

Accurately predicting patient survival is essential for cancer treatment decision. However, the prognostic prediction model based on histopathological...

Deep Learning Prediction of Pathologic Complete Response in Breast Cancer Using MRI and Other Clinical Data: A Systematic Review.

Breast cancer patients who have pathological complete response (pCR) to neoadjuvant chemotherapy (NAC) are more likely to have better clinical outcome...

An integrated deep learning model for the prediction of pathological complete response to neoadjuvant chemotherapy with serial ultrasonography in breast cancer patients: a multicentre, retrospective study.

BACKGROUND: The biological phenotype of tumours evolves during neoadjuvant chemotherapy (NAC). Accurate prediction of pathological complete response (...

Artificial Intelligence Based Study Association between p53 Gene Polymorphism and Endometriosis: A Systematic Review and Meta-analysis.

BACKGROUND: The P53 gene is critical to the onset and progression of cancers. Currently, relevant study findings indicate that the p53 gene may have a...

Direct Evaluation of Treatment Response in Brain Metastatic Disease with Deep Neuroevolution.

Cancer centers have an urgent and unmet clinical and research need for AI that can guide patient management. A core component of advancing cancer trea...

Machine learning applications for transcription level and phenotype predictions.

Predicting phenotypes and complex traits from genomic variations has always been a big challenge in molecular biology, at least in part because the ta...

A Graph Neural Network Approach for the Analysis of siRNA-Target Biological Networks.

Many biological systems are characterised by biological entities, as well as their relationships. These interaction networks can be modelled as graphs...

Optimization of deep learning models for the prediction of gene mutations using unsupervised clustering.

Deep learning models are increasingly being used to interpret whole-slide images (WSIs) in digital pathology and to predict genetic mutations. Current...

iEnhancer-DCLA: using the original sequence to identify enhancers and their strength based on a deep learning framework.

Enhancers are small regions of DNA that bind to proteins, which enhance the transcription of genes. The enhancer may be located upstream or downstream...

Gene-gene interaction detection with deep learning.

The extent to which genetic interactions affect observed phenotypes is generally unknown because current interaction detection approaches only conside...

Shuffle-ResNet: Deep learning for predicting LGG IDH1 mutation from multicenter anatomical MRI sequences.

The world health organization recommended to incorporate gene information such as isocitrate dehydrogenase 1 (IDH1) mutation status to improve prognos...

Fuzzy Logic as a Strategy for Combining Marker Statistics to Optimize Preselection of High-Density and Sequence Genotype Data.

The high dimensionality of genotype data available for genomic evaluations has presented a motivation for developing strategies to identify subsets of...

GeneSelectML: a comprehensive way of gene selection for RNA-Seq data via machine learning algorithms.

Selection of differentially expressed genes (DEGs) is a vital process to discover the causes of diseases. It has been shown that modelling of genomics...