Genetics

Integrated multi-omics analysis of ovarian cancer using variational autoencoders.

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 accountable for cancer and its development. In recent years, Deep Learning (DL) approaches have become a useful tool in integrated multi-omics analysis of canc...

Machine Learning Prediction and Experimental Validation of Antigenic Drift in H3 Influenza A Viruses in Swine.

The antigenic diversity of influenza A viruses (IAV) circulating in swine challenges the development of effective vaccines, increasing zoonotic threat and pandemic potential. High-throughput sequencing technologies can quantify IAV genetic diversity, but there are no accurate approaches to adequately describe antigenic phenotypes. This study evaluated an ensemble of nonlinear regression models to ...

PhotoModPlus: A web server for photosynthetic protein prediction from genome neighborhood features.

A new web server called PhotoModPlus is presented as a platform for predicting photosynthetic proteins via genome neighborhood networks (GNN) and geno...

Sparse deep neural networks on imaging genetics for schizophrenia case-control classification.

Deep learning methods hold strong promise for identifying biomarkers for clinical application. However, current approaches for psychiatric classificat...

Deep learning detects genetic alterations in cancer histology generated by adversarial networks.

Deep learning can detect microsatellite instability (MSI) from routine histology images in colorectal cancer (CRC). However, ethical and legal barrier...

AptaNet as a deep learning approach for aptamer-protein interaction prediction.

Aptamers are short oligonucleotides (DNA/RNA) or peptide molecules that can selectively bind to their specific targets with high specificity and affin...

JSOM: Jointly-evolving self-organizing maps for alignment of biological datasets and identification of related clusters.

With the rapid advances of various single-cell technologies, an increasing number of single-cell datasets are being generated, and the computational t...

DeepTCR is a deep learning framework for revealing sequence concepts within T-cell repertoires.

Deep learning algorithms have been utilized to achieve enhanced performance in pattern-recognition tasks. The ability to learn complex patterns in dat...

A two-stream convolutional neural network for microRNA transcription start site feature integration and identification.

MicroRNAs (miRNAs) play important roles in post-transcriptional gene regulation and phenotype development. Understanding the regulation of miRNA genes...

DeepVISP: Deep Learning for Virus Site Integration Prediction and Motif Discovery.

Approximately 15% of human cancers are estimated to be attributed to viruses. Virus sequences can be integrated into the host genome, leading to genom...

Deep learning-based enhancement of epigenomics data with AtacWorks.

ATAC-seq is a widely-applied assay used to measure genome-wide chromatin accessibility; however, its ability to detect active regulatory regions can d...

Prioritizing non-coding regions based on human genomic constraint and sequence context with deep learning.

Elucidating functionality in non-coding regions is a key challenge in human genomics. It has been shown that intolerance to variation of coding and pr...

Development of a neuron model based on DNAzyme regulation.

Neural networks based on DNA molecular circuits play an important role in molecular information processing and artificial intelligence systems. In fac...

seqQscorer: automated quality control of next-generation sequencing data using machine learning.

Controlling quality of next-generation sequencing (NGS) data files is a necessary but complex task. To address this problem, we statistically characte...

Deep Matrix Factorization Improves Prediction of Human CircRNA-Disease Associations.

In recent years, more and more evidence indicates that circular RNAs (circRNAs) with covalently closed loop play various roles in biological processes...

Machine Learning Model for Computational Tracking and Forecasting the COVID-19 Dynamic Propagation.

A computational model with intelligent machine learning for analysis of epidemiological data, is proposed. The innovations of adopted methodology cons...

Discovering Collective Variables of Molecular Transitions via Genetic Algorithms and Neural Networks.

With the continual improvement of computing hardware and algorithms, simulations have become a powerful tool for understanding all sorts of (bio)molec...

Artificial intelligence prediction model for overall survival of clear cell renal cell carcinoma based on a 21-gene molecular prognostic score system.

We developed and validated a new prognostic model for predicting the overall survival in clear cell renal cell carcinoma (ccRCC) patients. In this stu...

DeeReCT-APA: Prediction of Alternative Polyadenylation Site Usage Through Deep Learning.

Alternative polyadenylation (APA) is a crucial step in post-transcriptional regulation. Previous bioinformatic studies have mainly focused on the reco...

A machine learning-based gene signature of response to the novel alkylating agent LP-184 distinguishes its potential tumor indications.

BACKGROUND: Non-targeted cytotoxics with anticancer activity are often developed through preclinical stages using response criteria observed in cell l...