AIMC Topic: Gene Regulatory Networks

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Learning and interpreting the gene regulatory grammar in a deep learning framework.

PLoS computational biology
Deep neural networks (DNNs) have achieved state-of-the-art performance in identifying gene regulatory sequences, but they have provided limited insight into the biology of regulatory elements due to the difficulty of interpreting the complex features...

Network-based machine learning in colorectal and bladder organoid models predicts anti-cancer drug efficacy in patients.

Nature communications
Cancer patient classification using predictive biomarkers for anti-cancer drug responses is essential for improving therapeutic outcomes. However, current machine-learning-based predictions of drug response often fail to identify robust translational...

A Comprehensive Analysis of MicroRNAs in Human Osteoporosis.

Frontiers in endocrinology
MicroRNAs (miRNAs) are single-stranded RNA molecules that control gene expression in various processes, such as cancers, Alzheimer's disease, and bone metabolic diseases. However, the regulatory roles of miRNAs in osteoporosis have not been systemati...

Discovery of gene module acting on ubiquitin-mediated proteolysis pathway by co-expression network analysis for endometriosis.

Reproductive biomedicine online
RESEARCH QUESTION: Is abnormal gene module expression in the eutopic endometrium related to the occurrence of endometriosis?

Spage2vec: Unsupervised representation of localized spatial gene expression signatures.

The FEBS journal
Investigations of spatial cellular composition of tissue architectures revealed by multiplexed in situ RNA detection often rely on inaccurate cell segmentation or prior biological knowledge from complementary single-cell sequencing experiments. Here,...

Recent advances on the machine learning methods in predicting ncRNA-protein interactions.

Molecular genetics and genomics : MGG
Recent transcriptomics and bioinformatics studies have shown that ncRNAs can affect chromosome structure and gene transcription, participate in the epigenetic regulation, and take part in diseases such as tumorigenesis. Biologists have found that mos...

Robust edge-based biomarker discovery improves prediction of breast cancer metastasis.

BMC bioinformatics
BACKGROUND: The abundance of molecular profiling of breast cancer tissues entailed active research on molecular marker-based early diagnosis of metastasis. Recently there is a surging interest in combining gene expression with gene networks such as p...

Machine learning based refined differential gene expression analysis of pediatric sepsis.

BMC medical genomics
BACKGROUND: Differential expression (DE) analysis of transcriptomic data enables genome-wide analysis of gene expression changes associated with biological conditions of interest. Such analysis often provides a wide list of genes that are differentia...

Colorectal Cancer Prediction Based on Weighted Gene Co-Expression Network Analysis and Variational Auto-Encoder.

Biomolecules
An effective feature extraction method is key to improving the accuracy of a prediction model. From the Gene Expression Omnibus (GEO) database, which includes 13,487 genes, we obtained microarray gene expression data for 238 samples from colorectal c...

Establishment and Analysis of a Combined Diagnostic Model of Polycystic Ovary Syndrome with Random Forest and Artificial Neural Network.

BioMed research international
Polycystic ovary syndrome (PCOS) is one of the most common metabolic and reproductive endocrinopathies. However, few studies have tried to develop a diagnostic model based on gene biomarkers. In this study, we applied a computational method by combin...