AIMC Topic: Gene Regulatory Networks

Clear Filters Showing 431 to 440 of 830 articles

Genes and regulatory mechanisms associated with experimentally-induced bovine respiratory disease identified using supervised machine learning methodology.

Scientific reports
Bovine respiratory disease (BRD) is a multifactorial disease involving complex host immune interactions shaped by pathogenic agents and environmental factors. Advancements in RNA sequencing and associated analytical methods are improving our understa...

A Graph Feature Auto-Encoder for the prediction of unobserved node features on biological networks.

BMC bioinformatics
BACKGROUND: Molecular interaction networks summarize complex biological processes as graphs, whose structure is informative of biological function at multiple scales. Simultaneously, omics technologies measure the variation or activity of genes, prot...

Classification and Functional Analysis between Cancer and Normal Tissues Using Explainable Pathway Deep Learning through RNA-Sequencing Gene Expression.

International journal of molecular sciences
Deep learning has proven advantageous in solving cancer diagnostic or classification problems. However, it cannot explain the rationale behind human decisions. Biological pathway databases provide well-studied relationships between genes and their pa...

LGFC-CNN: Prediction of lncRNA-Protein Interactions by Using Multiple Types of Features through Deep Learning.

Genes
Long noncoding RNA (lncRNA) plays a crucial role in many critical biological processes and participates in complex human diseases through interaction with proteins. Considering that identifying lncRNA-protein interactions through experimental methods...

Manipulating cellular microRNAs and analyzing high-dimensional gene expression data using machine learning workflows.

STAR protocols
MicroRNAs (miRNAs) are elements of the gene regulatory network and manipulating their abundance is essential toward elucidating their role in patho-physiological conditions. We present a detailed workflow that identifies important miRNAs using a mach...

Prediction of Essential Genes in Comparison States Using Machine Learning.

IEEE/ACM transactions on computational biology and bioinformatics
Identifying essential genes in comparison states (EGS) is vital to understanding cell differentiation, performing drug discovery, and identifying disease causes. Here, we present a machine learning method termed Prediction of Essential Genes in Compa...

VEGA is an interpretable generative model for inferring biological network activity in single-cell transcriptomics.

Nature communications
Deep learning architectures such as variational autoencoders have revolutionized the analysis of transcriptomics data. However, the latent space of these variational autoencoders offers little to no interpretability. To provide further biological ins...

A network of core and subtype-specific gene expression programs in myositis.

Acta neuropathologica
Myositis comprises a heterogeneous group of skeletal muscle disorders which converge on chronic muscle inflammation and weakness. Our understanding of myositis pathogenesis is limited, and many myositis patients lack effective therapies. Using muscle...

HOX cluster and their cofactors showed an altered expression pattern in eutopic and ectopic endometriosis tissues.

Reproductive biology and endocrinology : RB&E
Endometriosis is major gynecological disease that affects over 10% of women worldwide and 30%-50% of these women have pelvic pain, abnormal uterine bleeding and infertility. The cause of endometriosis is unknown and there is no definite cure mainly b...

Identification of biomarkers for acute leukemia via machine learning-based stemness index.

Gene
Traditional methods to understand leukemia stem cell (LSC)'s biological characteristics include constructing LSC-like cells and mouse models by transgenic or knock-in methods. However, there are some potential pitfalls in using this method, such as r...