AIMC Topic: Signal Transduction

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Deep learning models predict regulatory variants in pancreatic islets and refine type 2 diabetes association signals.

eLife
Genome-wide association analyses have uncovered multiple genomic regions associated with T2D, but identification of the causal variants at these remains a challenge. There is growing interest in the potential of deep learning models - which predict e...

Biophysical prediction of protein-peptide interactions and signaling networks using machine learning.

Nature methods
In mammalian cells, much of signal transduction is mediated by weak protein-protein interactions between globular peptide-binding domains (PBDs) and unstructured peptidic motifs in partner proteins. The number and diversity of these PBDs (over 1,800 ...

Machine Learning to Quantitate Neutrophil NETosis.

Scientific reports
We introduce machine learning (ML) to perform classification and quantitation of images of nuclei from human blood neutrophils. Here we assessed the use of convolutional neural networks (CNNs) using free, open source software to accurately quantitate...

Big Data Challenges Targeting Proteins in GPCR Signaling Pathways; Combining PTML-ChEMBL Models and [S]GTPγS Binding Assays.

ACS chemical neuroscience
G-protein-coupled receptors (GPCRs), also known as 7-transmembrane receptors, are the single largest class of drug targets. Consequently, a large amount of preclinical assays having GPCRs as molecular targets has been released to public sources like ...

A comparison of machine learning classifiers for dementia with Lewy bodies using miRNA expression data.

BMC medical genomics
BACKGROUND: Dementia with Lewy bodies (DLB) is the second most common subtype of neurodegenerative dementia in humans following Alzheimer's disease (AD). Present clinical diagnosis of DLB has high specificity and low sensitivity and finding potential...

Study on miR-384-5p activates TGF-β signaling pathway to promote neuronal damage in abutment nucleus of rats based on deep learning.

Artificial intelligence in medicine
BACKGROUND: Any ailment in our organs can be visualized by using different modality signals and images. Hospitals are encountering a massive influx of large multimodality patient data to be analysed accurately and with context understanding. The deep...

Analysis of defective pathways and drug repositioning in Multiple Sclerosis via machine learning approaches.

Computers in biology and medicine
BACKGROUND: Although some studies show that there could be a genetic predisposition to develop Multiple Sclerosis (MS), attempts to find genetic signatures related to MS diagnosis and development are extremely rare.

Designing combination therapies with modeling chaperoned machine learning.

PLoS computational biology
Chemotherapy resistance is a major challenge to the effective treatment of cancer. Thus, a systematic pipeline for the efficient identification of effective combination treatments could bring huge biomedical benefit. In order to facilitate rational d...

Machine Learning to Understand the Immune-Inflammatory Pathways in Fibromyalgia.

International journal of molecular sciences
Fibromyalgia (FM) is a chronic syndrome characterized by widespread musculoskeletal pain, and physical and emotional symptoms. Although its pathophysiology is largely unknown, immune-inflammatory pathways may be involved. We examined serum interleuki...

Artificial Intelligence Approach To Investigate the Longevity Drug.

The journal of physical chemistry letters
Longevity is a very important and interesting topic, and has been demonstrated to be related to longevity. We combined network pharmacology, machine learning, deep learning, and molecular dynamics (MD) simulation to investigate potent lead drugs. Re...