AI Medical Compendium Journal:
Genomics

Showing 1 to 10 of 38 articles

Multiomics identification of programmed cell death-related characteristics for nonobstructive azoospermia based on a 675-combination machine learning computational framework.

Genomics
BACKGROUND: Abnormal programmed cell death (PCD) plays a central role in spermatogenic dysfunction. However, the molecular mechanisms and biomarkers of PCD in patients with nonobstructive azoospermia (NOA) remain unclear.

Enhancing schizophrenia phenotype prediction from genotype data through knowledge-driven deep neural network models.

Genomics
This article explores deep learning model design, drawing inspiration from the omnigenic model and genetic heterogeneity concepts, to improve schizophrenia prediction using genotype data. It introduces an innovative three-step approach leveraging neu...

Apoptosis and NETotic cell death affect diabetic nephropathy independently: An study integrative study encompassing bioinformatics, machine learning, and experimental validation.

Genomics
OBJECTIVE: Although programmed cell death (PCD) and diabetic nephropathy (DN) are intrinsically conneted, the interplay among various PCD forms remains elusive. In this study, We aimed at identifying independently DN-associated PCD pathways and bioma...

MetaAc4C: A multi-module deep learning framework for accurate prediction of N4-acetylcytidine sites based on pre-trained bidirectional encoder representation and generative adversarial networks.

Genomics
MOTIVATION: N4-acetylcytidine (ac4C) is a highly conserved RNA modification that plays a crucial role in various biological processes. Accurately identifying ac4C sites is of paramount importance for gaining a deeper understanding of their regulatory...

DeepPlnc: Bi-modal deep learning for highly accurate plant lncRNA discovery.

Genomics
We present here a bi-modal CNN based deep-learning system, DeepPlnc, to identify plant lncRNAs with high accuracy while using sequence and structural properties. Unlike most of the existing software, it works accurately even in conditions with ambigu...

iProm-Zea: A two-layer model to identify plant promoters and their types using convolutional neural network.

Genomics
A promoter is a short DNA sequence near the start codon, responsible for initiating the transcription of a specific gene in the genome. The accurate recognition of promoters is important for achieving a better understanding of transcriptional regulat...

PanClassif: Improving pan cancer classification of single cell RNA-seq gene expression data using machine learning.

Genomics
Cancer is one of the major causes of human death per year. In recent years, cancer identification and classification using machine learning have gained momentum due to the availability of high throughput sequencing data. Using RNA-seq, cancer researc...

EditPredict: Prediction of RNA editable sites with convolutional neural network.

Genomics
RNA editing exerts critical impacts on numerous biological processes. While millions of RNA editings have been identified in humans, much more are expected to be discovered. In this work, we constructed Convolutional Neural Network (CNN) models to pr...

Evaluation of deep learning approaches for modeling transcription factor sequence specificity.

Genomics
As a key component of gene regulation, transcription factors (TFs) play an important role in a number of biological processes. To fully understand the underlying mechanism of TF-mediated gene regulation, it is therefore critical to accurately identif...

ncRDense: A novel computational approach for classification of non-coding RNA family by deep learning.

Genomics
With the rapidly growing importance of biological research, non-coding RNAs (ncRNA) attract more attention in biology and bioinformatics. They play vital roles in biological processes such as transcription and translation. Classification of ncRNAs is...