A importancia da Táboa Periódica na Ciencia dos Materiais

2020 ◽  
Vol 90 ◽  
pp. 159-168
Author(s):  
Francisco Rivadulla
Keyword(s):  
Data Mining ◽  
In Silico

Como en calquera outra rama da Química, a TPEQ xogou un papel fundamental no desenvolvemento da Ciencia de Materiais, ó influír de xeito decisivo na comprensión da relación entre as propiedades químicas e físicas dos materiais e as propiedades dos átomos que os compoñen. Tal vez para comprender a gran capacidade que nos da a TPEQ para desenvolver esta intuición á hora de preparar compostos está ben recordar que collendo só os 70 elementos máis comúns, son posibles 2415 combinacións binarias, 54740 ternarias e 916895 cuaternarias. Non dispor deste sistema clasificatorio teríanos condenados á proba-erro hasta a eternidade. Os métodos computacionais de data mining e cálculo in-silico xogarán tal vez o papel da TPEQ no futuro da Ciencia de Materiais.

scholarly journals Reconstruction of the miR-506-Quaking axis in Idiopathic Pulmonary Fibrosis using integrative multi-source bioinformatics

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Stevan D. Stojanović ◽  
Maximilian Fuchs ◽  
Chunguang Liang ◽  
Kevin Schmidt ◽  
Ke Xiao ◽  
...  
Keyword(s):  
Data Mining ◽  
Idiopathic Pulmonary Fibrosis ◽  
Pulmonary Fibrosis ◽  
Rna Sequencing ◽  
In Silico ◽  
Rna Binding ◽  
Human Lung ◽  
Model Systems ◽  
Lung Fibroblasts ◽  
Sequencing Analysis

AbstractThe family of RNA-binding proteins (RBP) functions as a crucial regulator of multiple biological processes and diseases. However, RBP function in the clinical setting of idiopathic pulmonary fibrosis (IPF) is still unknown. We developed a practical in silico screening approach for the characterization of RBPs using multi-sources data information and comparative molecular network bioinformatics followed by wet-lab validation studies. Data mining of bulk RNA-Sequencing data of tissues of patients with IPF identified Quaking (QKI) as a significant downregulated RBP. Cell-type specific expression was confirmed by single-cell RNA-Sequencing analysis of IPF patient data. We systematically analyzed the molecular interaction network around QKI and its functional interplay with microRNAs (miRs) in human lung fibroblasts and discovered a novel regulatory miR-506-QKI axis contributing to the pathogenesis of IPF. The in silico results were validated by in-house experiments applying model systems of miR and lung biology. This study supports an understanding of the intrinsic molecular mechanisms of IPF regulated by the miR-506-QKI axis. Initially applied to human lung disease, the herein presented integrative in silico data mining approach can be adapted to other disease entities, underlining its practical relevance in RBP research.

In silico prediction, LC-HRMS/MS analysis, and targeted/untargeted data-mining workflow for the profiling of phenylfentanyl in vitro metabolites

Talanta ◽  
2021 ◽  
pp. 122740
Author(s):  
Annagiulia Di Trana ◽  
Pietro Brunetti ◽  
Raffaele Giorgetti ◽  
Enrico Marinelli ◽  
Simona Zaami ◽  
...  
Keyword(s):  
Data Mining ◽  
In Silico ◽  
In Silico Prediction ◽  
Ms Analysis

scholarly journals A Program for Automated Data Mining of PubChem to Screen a Billion Compounds and Generate by Machine Learning Based AutoQSAR Algorithm Anti-Corona Viral Drug Leads (Replicase Polyprotein 1ab Inhibitors) and in Silico Study of the Top Drug Lead Compounds

2020 ◽  
Author(s):  
Ben Geoffrey A S ◽  
Akhil Sanker ◽  
Host Antony Davidd ◽  
Judith Gracia
Keyword(s):  
Machine Learning ◽  
Data Mining ◽  
In Silico ◽  
Drug Target ◽  
Data Set ◽  
Lead Compounds ◽  
Pubchem Database ◽  
Drug Lead ◽  
Lead Generation ◽  
Drug Leads

Our work is composed of a python program for automatic data mining of PubChem database to collect data associated with the corona virus drug target replicase polyprotein 1ab (UniProt identifier : POC6X7 ) of data set involving active compounds, their activity value (IC50) and their chemical/molecular descriptors to run a machine learning based AutoQSAR algorithm on the data set to generate anti-corona viral drug leads. The machine learning based AutoQSAR algorithm involves feature selection, QSAR modelling, validation and prediction. The drug leads generated each time the program is run is reflective of the constantly growing PubChem database is an important dynamic feature of the program which facilitates fast and dynamic anti-corona viral drug lead generation reflective of the constantly growing PubChem database. The program prints out the top anti-corona viral drug leads after screening PubChem library which is over a billion compounds. The interaction of top drug lead compounds generated by the program and two corona viral drug target proteins, 3-Cystiene like Protease (3CLPro) and Papain like protease (PLpro) was studied and analysed using molecular docking tools. The compounds generated as drug leads by the program showed favourable interaction with the drug target proteins and thus we recommend the program for use in anti-corona viral compound drug lead generation as it helps reduce the complexity of virtual screening and ushers in an age of automatic ease in drug lead generation. The leads generated by the program can further be tested for drug potential through further In Silico, In Vitro and In Vivo testing <div><br></div><div><div>The program is hosted, maintained and supported at the GitHub repository link given below</div><div><br></div><div>https://github.com/bengeof/Drug-Discovery-P0C6X7</div></div><div><br></div>

scholarly journals Machine learning and data mining frameworks for predicting drug response in cancer: An overview and a novel in silico screening process based on association rule mining

2019 ◽  
Vol 203 ◽  
pp. 107395 ◽  
Author(s):  
Konstantinos Vougas ◽  
Theodore Sakellaropoulos ◽  
Athanassios Kotsinas ◽  
George-Romanos P. Foukas ◽  
Andreas Ntargaras ◽  
...  
Keyword(s):  
Machine Learning ◽  
Data Mining ◽  
Association Rule ◽  
In Silico ◽  
Association Rule Mining ◽  
Drug Response ◽  
Rule Mining ◽  
In Silico Screening ◽  
Screening Process

Antiplasmodial Biodereplication Based on Highly Efficient Methods

2020 ◽  
Author(s):  
Pedro Vásquez-Ocmín ◽  
Jean-François Gallard ◽  
Anne-Cécile Van Baelen ◽  
Karine Leblanc ◽  
Sandrine Cojean ◽  
...  
Keyword(s):  
Mass Spectrometry ◽  
Data Mining ◽  
Molecular Docking ◽  
Mechanism Of Action ◽  
In Silico ◽  
Binding Assay ◽  
Antimalarial Drugs ◽  
Heme Binding ◽  
Molecular Networking ◽  
In Silico Modeling

We propose a biodereplication method using mass spectrometry and combining the classical dereplication approach with the predominant mechanism of action of antimalarial drugs. The method encompasses a biomimetic heme binding assay (heme adducts detection by MS), molecular networking for quick data mining, CPC for extract fractionation and compounds isolation and in silico modeling of heme adducts by molecular docking.

IN SILICO DATA MINING FOR DEVELOPMENT OF CAPSICUM MICROSATELLITES

2007 ◽  
pp. 123-127 ◽  
Author(s):  
A.G. İnce ◽  
A.N. Onus ◽  
S.Y. Elmasulu ◽  
M. Bilgen ◽  
M. Karaca
Keyword(s):  
Data Mining ◽  
In Silico

In Silico Investigation into H2 Uptake in MOFs: Combined Text/Data Mining and Structural Calculations

Langmuir ◽  
2019 ◽  
Vol 36 (1) ◽  
pp. 119-129 ◽  
Author(s):  
Omer Tayfuroglu ◽  
Abdulkadir Kocak ◽  
Yunus Zorlu
Keyword(s):  
Data Mining ◽  
In Silico ◽  
Text Data ◽  
Text Data Mining

ARSENIC ACCUMULATION IN PLANTS – OUTLINING STRATEGIES FOR DEVELOPING IMPROVED VARIETY OF CROPS FOR AVOIDING ARSENIC TOXICITY IN FOODS

2010 ◽  
Vol 18 (01) ◽  
pp. 223-241 ◽  
Author(s):  
DAN LUNDH ◽  
DENNIS LARSSON ◽  
NOOR NAHAR ◽  
ABUL MANDAL
Keyword(s):  
Data Mining ◽  
In Silico ◽  
System Biology ◽  
Cellular Localization ◽  
Molecular Mechanisms ◽  
Efflux Pump ◽  
Arsenate Reductase ◽  
Potential Health ◽  
System Biology Model ◽  
In Silico Studies

Contamination of food with arsenics is a potential health risk for both humans and animals in many regions of the world, especially in Asia. Arsenics can be accumulated in humans, animals and plants for a longer period and a long-term exposure of humans to arsenics results in severe damage of kidney, lever, heart etc. and many other vascular diseases. Arsenic contamination in human may also lead to development of cancer. In this paper we report our results on data mining approach (an in silico analysis based on searching of the existing genomic databases) for identification and characterization of genes that might be responsible for uptake, accumulation or metabolism of arsenics. For these in silico analyses we have involved the model plant Arabidopsis thaliana in our investigation. By employing a system biology model (a kinetic model) we have studied the molecular mechanisms of these processes in this plant. This model contains equations for uptake, metabolism and sequestration of different types of arsenic; As(V), As(III), MMAA and DMAA. The model was then implemented in the software XPP. The model was also validated against the data existing in the literatures. Based on the results of these in silico studies we have developed some strategies that can be used for reducing arsenic contents in different parts of the plant. Data mining experiments resulted in identification of two candidate genes (ACR2, arsenate reductase 2 and PCS1, phytochelatin synthase 1) that are involved either in uptake, transport or cellular localization of arsenic in A. thaliana. However, our system biology model revealed that by increasing the level of arsenate reductase together with an increased rate of arsenite sequestration in the vacuoles (by involving an arsenite efflux pump MRP1/2), it is possible to reduce the amount of arsenics in the shoots of A. thaliana to 11–12%.

scholarly journals In Silico Data Mining of Single Nucleotide Polymorphisms in EZH2 and Their Role in Cancer

2017 ◽  
Vol 11 (2) ◽  
Author(s):  
Trupti N. Patel ◽  
Richa Vasan ◽  
Manjari Trivedi ◽  
Manali Chakraborty ◽  
Priyanjali Bhattacharya
Keyword(s):  
Data Mining ◽  
Single Nucleotide Polymorphisms ◽  
In Silico ◽  
Nucleotide Polymorphisms ◽  
Single Nucleotide
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