AIMC Topic: Signal Transduction
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Machine Learning to Quantitate Neutrophil NETosis.
Scientific reports
Nov 15, 2019
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
Nov 4, 2019
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
Oct 30, 2019
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
Oct 10, 2019
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
Oct 9, 2019
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
Sep 9, 2019
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
Aug 29, 2019
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
Aug 14, 2019
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...
Signal Transduction
Medicine, Chinese Traditional
Klotho Proteins
Glucuronidase
Receptor, IGF Type 1
Antigens, CD
Ligands
Inhibitory Concentration 50
Molecular Docking Simulation
Drug Discovery
Protein Interaction Maps
Protein Binding
Artificial Intelligence
Algorithms
Databases, Factual
Binding Sites
Thermodynamics
Receptor, Insulin
Exploration of potential key pathways and genes in multiple ocular cancers through bioinformatics analysis.
Graefe's archive for clinical and experimental ophthalmology = Albrecht von Graefes Archiv fur klinische und experimentelle Ophthalmologie
Jul 15, 2019
PURPOSE: Primary cancers of the eye are common in ocular diseases. The objective of this study was to explore the underlying mechanisms and the potential target genes in multiple ocular cancers by bioinformatics approach.
Exploring the druggable space around the Fanconi anemia pathway using machine learning and mechanistic models.
BMC bioinformatics
Jul 2, 2019
BACKGROUND: In spite of the abundance of genomic data, predictive models that describe phenotypes as a function of gene expression or mutations are difficult to obtain because they are affected by the curse of dimensionality, given the disbalance bet...
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