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MIANN models in medicinal, physical and organic chemistry
(Bentham, 2013)
[Abstract] Reducing costs in terms of time, animal sacrifice, and material resources with computational methods has become a promising goal in Medicinal, Biological, Physical and Organic Chemistry. There are many computational ...
The Rücker–Markov invariants of complex bio-systems: applications in parasitology and neuroinformatics
(Elsevier, 2013-02-23)
[Abstract] Rücker's walk count (WC) indices are well-known topological indices (TIs) used in Chemoinformatics to quantify the molecular structure of drugs represented by a graph in Quantitative structure–activity/property ...
Experimental and chemometric studies of cell membrane permeability
(Elsevier, 2016-03-18)
[Abstract] Cell membrane permeability (P) governs the molecules or ions to transport through the cell membrane. In this study, we measured P of ruminal microbes in different initial levels of surface tension (ST) and ...
Gene prioritization, communality analysis, networking and metabolic integrated pathway to better understand breast cancer pathogenesis
(Nature, 2018-11-12)
[Abstract] Consensus strategy was proved to be highly efficient in the recognition of gene-disease association. Therefore, the main objective of this study was to apply theoretical approaches to explore genes and communities ...
Gastrointestinal Spatiotemporal mRNA Expression of Ghrelin vs Growth Hormone Receptor and New Growth Yield Machine Learning Model Based on Perturbation Theory
(Nature, 2016-07-27)
[Abstract] The management of ruminant growth yield has economic importance. The current work presents a study of the spatiotemporal dynamic expression of Ghrelin and GHR at mRNA levels throughout the gastrointestinal tract ...
Perturbation theory/machine learning model of ChEMBL data for dopamine targets: docking, synthesis, and assay of new l-prolyl-l-leucyl-glycinamide peptidomimetics
(American Chemical Society, 2018-05-23)
[Abstract] Predicting drug–protein interactions (DPIs) for target proteins involved in dopamine pathways is a very important goal in medicinal chemistry. We can tackle this problem using Molecular Docking or Machine Learning ...
Mapping networks of anti-HIV drug cocktails vs. AIDS epidemiology in the US counties
(Elsevier, 2014-08-20)
[Abstract] The implementation of the highly active antiretroviral therapy (HAART) and the combination of anti-HIV drugs have resulted in longer survival and a better quality of life for the people infected with the virus. ...
Bio-AIMS collection of chemoinformatics web tools based on molecular graph information and artificial intelligence models
(Bentham, 2015-09-01)
[Abstract] The molecular information encoding into molecular descriptors is the first step into in silico Chemoinformatics methods in Drug Design. The Machine Learning methods are a complex solution to find prediction ...
Experimental and computational studies of fatty acid distribution networks
(Royal Society of Chemistry, 2015-08-06)
[Abstract] Unbalanced uptake of Omega 6/Omega 3 (ω-6/ω-3) ratios could increase chronic disease occurrences, such as inflammation, atherosclerosis, or tumor proliferation, and methylation methods for measuring the ruminal ...
Carbon nanotubes’ effect on mitochondrial oxygen flux dynamics: polarography experimental study and machine learning models using star graph trace invariants of Raman spectra
(MDPI, 2017-11-11)
[Abstract] This study presents the impact of carbon nanotubes (CNTs) on mitochondrial oxygen mass flux (Jm) under three experimental conditions. New experimental results and a new methodology are reported for the first ...