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Structure-Based Virtual Screening, Molecular Docking, Molecular Dynamics Simulation of EGFr for the Clinical Treatment of Glioblastoma

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APPLIED BIOCHEMISTrY AND BIOTECHNOLOGY 2023年第8期195卷 5094-5119页

作者： Bhrdwaj, Anushka Abdalla, Mohnad Pande, Aditi Madhavi, Maddala Chopra, Ishita Soni, Lovely Vijayakumar, Natchimuthu Panwar, Umesh Khan, Mohd. Aqueel Prajapati, Leena Gujrati, Deepika Belapurkar, Pranoti Albogami, Sarah Hussain, Tajamul Selvaraj, Chandrabose Nayarisseri, Anuraj Singh, Sanjeev Kumar Eminent Biosci In Silico Res Lab 91 Sect A Indore 452010 Madhya Pradesh India Shandong Univ Cheeloo Coll Med Sch Pharmaceut Sci Dept PharmaceutKey Lab Chem BiolMinist Educ 44 Cultural West Rd Jinan 250012 Shandong Peoples R China Osmania Univ Dept Zool Hyderabad 500007 Telangana India M Kumarasamy Coll Engn Dept Phys Karur 639113 Tamil Nadu India Alagappa Univ Dept Bioinformat Comp Aided Drug Designing & Mol Modeling Lab Karaikkudi 630003 Tamil Nadu India Osmania Univ Inst Genet Hyderabad 500016 India Osmania Univ Hosp Genet Dis Hyderabad 500016 India Acropolis Inst Dept Biosci Indore 453771 Madhya Pradesh India Taif Univ Coll Sci Dept Biotechnol POB 11099 Taif 21944 Saudi Arabia King Saud Univ Coll Sci Res Chair Biomed Applicat Nanomat Biochem Dept Riyadh Saudi Arabia King Saud Univ Coll Sci Ctr Excellence Biotechnol Res Riyadh Saudi Arabia Saveetha Univ Dept Pharmacol Ctr Transdisciplinary Res Saveetha Coll Dent & HospSIMATS Chennai 600077 Tamil Nadu India LeGene Biosci Pvt Ltd Bioinformat Res Lab 91 Sect A Indore 452010 Madhya Pradesh India Ctr Biomed Res Dept Data Sci SGPGIMS CampusRaebareli Rd Lucknow 226014 Uttar Pradesh India

Glioblastoma (GBM) is a WHO Grade IV tumor with poor visibility, a high risk of comorbidity, and exhibit limited treatment options. resurfacing from second-rate glioma was originally classified as either mandatory or optional. recent interest in personalized medicine has motivated research toward biomarker stratification-based individualized illness therapy. GBM biomarkers have been investigated for their potential utility in prognostic stratification, driving the development of targeted therapy and customizing therapeutic treatment. Due to the availability of a specific EGFrvIII mutational variation with a clear function in glioma-genesis, recent research suggests that EGFr has the potential to be a prognostic factor in GBM, while others have shown no clinical link between EGFr and survival. The pre-existing pharmaceutical lapatinib (PubChem ID: 208,908) with a higher affinity score is used for virtual screening. As a result, the current study revealed a newly screened chemical (PubChem CID: 59,671,768) with a higher affinity than the previously known molecule. When the two compounds are compared, the former has the lowest re-rank score. The time-resolved features of a virtually screened chemical and an established compound were investigated using molecular dynamics simulation. Both compounds are equivalent, according to the ADMET study. This report implies that the virtual screened chemical could be a promising Glioblastoma therapy.

关键词： EGFr Glioblastoma Virtual screening Molecular docking MD simulation r programming ADMET Boiled egg plot

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Monitoring and mapping rural urbanization and land use changes using Landsat data in the northeast subtropical region of Vietnam

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EGYPTIAN JOUrNAL OF rEMOTE SENSING AND SPACE SCIENCES 2020年第1期23卷 11-19页

作者： Ha, Tuyen V. Tuohy, Mike Irwin, Matthew Tuan, Pham V. Massey Univ Inst Agr & Environm Palmerston North 4474 New Zealand Thai Nguyen Univ Agr & Forestry Fac Nat Resource Management Thai Nguyen City 25000 Vietnam

rapid land use change has taken place in many neighboring provinces of the capital of Vietnam such as Thai Nguyen province over the past 2 decades due to urbanization and industrialization. Deriving accurate and updated land cover and land-use change information is essential for the environmental monitoring, evaluation and management. In this study, a robust classification algorithm, random Forest (rF) was employed in r programming to map and monitor temporal and spatial characteristics of urban expansion and land-use change in Thai Nguyen province, Vietnam. The results showed that there has been a substantial and uneven urban growth and a significant loss of forest and cropland between 2000 and 2016. Most of the conversion of agriculture and forest into built-up and mining uses were largely detected in rural regions and suburbs of Thai Nguyen. Further GIS analysis revealed that rapid urban and industrial expansion was spatially occurred in the southern rural portions and central area of the province. This study also demonstrates the potential of Landsat data and combination of r programming language and GIS to provide a timely, accurate and economical means to map and analyze temporal land cover and land use changes for future national and local land development planning. (C) 2018 National Authority for remote Sensing and Space Sciences. Production and hosting by Elsevier B.V. This is an open access article under the CC BY-NC-ND license (http://***/licenses/bync-nd/4.0/).

关键词： rural land cover remote sensing GIS r programming

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Predicting short-term stock prices using ensemble methods and online data sources

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EXPErT SYSTEMS WITH APPLICATIONS 2018年 112卷 258-273页

作者： Weng, Bin Lu, Lin Wang, Xing Megahed, Fadel M. Martinez, Waldyn Auburn Univ Dept Ind & Syst Engn Auburn AL 36849 USA Miami Univ Dept Informat Syst & Analyt Oxford OH 45056 USA

With the ubiquity of the Internet, platforms such as: Google, Wikipedia and the like can provide insights pertaining to firms' financial performance as well as capture the collective interest of traders through search trends, number of web page visitors and/or financial news sentiment. Information emanating from these platforms can significantly affect, or be affected by, changes in the stock market. The overarching goal of this paper is to develop a financial expert system that incorporates these features to predict short term stock prices. Our expert system is comprised of two main modules: a knowledge base and an artificial intelligence (AI) platform. The "knowledge base" for our expert system captures: (a) historical stock prices;(b) several well-known technical indicators;(c) counts and sentiment scores of published news articles for a given stock;(d) trends in Google searches for the given stock ticker;and (e) number of unique visitors for pertinent Wikipedia pages. Once the data is collected, we use a structured approach for data preparation. Then, the AI platform trains four machine learning ensemble methods: (a) a neural network regression ensemble;(b) a support vector regression ensemble;(c) a boosted regression tree;and (d) a random forest regression. In the cross-validation phase, the AI platform picks the "best" ensemble for a given stock. To evaluate the efficacy of our expert system, we first present a case study based on the Citi Group stock ($C) with data collected from 01/01/2013 - 12/31/2016. We show the expert system can predict the 1-day ahead $C stock price with a mean absolute percent error (MAPE) <= 1.50% and the 1-10 day ahead with a MAPE <= 1.89%, which is better than the reported results in the literature. We show that the use of features extracted from online sources does not substitute the traditional financial metrics, but rather supplements them to improve upon the prediction performance of machine learning based methods. To highlight

关键词： Big data Ensembles Google trends r programming Sentiment analysis Wikipedia

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The Fuzzy Natural Transformations, the Algebra P(ω)/fin and Generalized Encoding Theory

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IEEE ACCESS 2022年 10卷 15135-15150页

作者： Jobczyk, Krystian AGH Univ Sci & Technol Dept Appl Comp Sci PL-30059 Krakow Poland CUNY Saul Kripke Ctr New York NY 10016 USA

A category theory constitutes a convenient conceptual apparatus to organize the worlds of mathematical entities. The concept of fuzzy natural transformation as an abstract mapping on functors is one of the essential concepts of this theory. If we admit a piece of non-commutativity in its definition diagrams, then the 'upward' and the 'downward' diagram parts generate different result sets. In this way, we can introduce the concept of fuzzy natural transformation. We deal with the multi-fuzzy natural transformation if such a transformation is based on a multi-diagram. This paper aims to describe the multi-fuzzy natural transformation situation when the symmetric difference of the result is finite. It allows us to organize the whole spectrum of the result sets in a unique quotient algebra P-comp(omega)/fin. Different algebraic properties of this structure will be explored, and a piece of classical encoding theory will be reconstructed in the environment determined by this algebra. In particular, the concepts of the k-multi similarity, the abstract k-multi similarity, the k-similarity balls, and the abstract k-similarity balls are introduced as some generalization of the idea of Hamming's distances and Hamming's balls. Finally, it is shown how to automate some verification processes in this context using an r-based programming environment.

关键词： Algebra Encoding Organizations Cognition Semantics Licenses Machinery The fuzzy natural transformation P(omega)/fin generalized encoding theory Hamming distances r programming

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Synthesis of non-active electrode (TiO2/GO/Ag) for the photo-electro-Fenton oxidation of micropollutants in wastewater

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INTErNATIONAL JOUrNAL OF ENVIrONMENTAL SCIENCE AND TECHNOLOGY 2023年第1期20卷 639-652页

作者： Kaur, P. Park, Y. Imteaz, M. A. Sillanpaa, M. Aalto Univ Dept Chem & Met Engn Espoo 00076 Finland LUT Univ Dept Separat Sci Mikkeli 50130 Finland Seoul Natl Univ Sci & Technol Dept Environm Engn Seoul 01811 South Korea Swinburne Univ Technol Dept Civil & Construct Engn Melbourne Vic Australia Univ Johannesburg Sch Min Met & Chem Engn Dept Chem Engn POB 17011 ZA-2028 Doornfontein South Africa Chandigarh Univ Univ Ctr Res & Dev Dept Civil Engn Mohali 140413 Punjab India King Saud Univ Coll Sci Chem Dept Riyadh 11451 Saudi Arabia Zhejiang Rongsheng Environm Protect Paper Co LTD 588 East Zhennan Rd Jiaxing 314213 Zhejiang Peoples R China

Heterogeneous photo-electro-Fenton oxidation (hPEF) is known to be a robust technique, which can be employed for promoting organic degradation. This paper describes an environmentally friendly approach with the combination of photocatalysis and electrocatalysis in less acidic pH, aiming to achieve faster mineralization of a pharmaceutical micropollutant without adding any external oxidants. TiO2/GO/loaded Ag non-active electrodes are synthesized for the degradation of bupropion hydrochloride (antidepressant drug). The present work also seeks the parametric modeling and optimization of hPEF process parameters by using r programming. Nonlinear kinetic modeling was performed for the determination of kinetic parameters. The role of selected process parameters on the mineralization of bupropion was also explained in detail. The OH center dot and O-2(center dot-) showed their active participation in the degradation process, while Ag and UV-C played an active role in the disinfection of treated wastewater.

关键词： Heterogeneous photo-electro-Fenton TiO2 GO Ag-loaded electrodes r programming response surface model Micropollutants

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Stock market one-day ahead movement prediction using disparate data sources

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EXPErT SYSTEMS WITH APPLICATIONS 2017年 79卷 153-163页

作者： Weng, Bin Ahmed, Mohamed A. Megahed, Fadel M. Auburn Univ Dept Ind & Syst Engn Auburn AL 36849 USA Miami Univ Farmer Sch Business Oxford OH 45056 USA Miami Univ Ctr Analyt & Data Sci Oxford OH 45056 USA

There are several commercial financial expert systems that can be used for trading on the stock exchange. However, their predictions are somewhat limited since they primarily rely on time-series analysis of the market. With the rise of the Internet, new forms of collective intelligence (e.g. Google and Wikipedia) have emerged, representing a new generation of "crowd-sourced" knowledge bases. They collate information on publicly traded companies, while capturing web traffic statistics that reflect the public's collective interest. Google and Wikipedia have become important "knowledge bases" for investors. In this research, we hypothesize that combining disparate online data sources with traditional time-series and technical indicators for a stock can provide a more effective and intelligent daily trading expert system. Three machine learning models, decision trees, neural networks and support vector machines, serve as the basis for our "inference engine". To evaluate the performance of our expert system, we present a case study based on the AAPL (Apple NASDAQ) stock. Our expert system had an 85% accuracy in predicting the next-day AAPL stock movement, which outperforms the reported rates in the literature. Our results suggest that: (a) the knowledge base of financial expert systems can benefit from data captured from nontraditional "experts" like Google and Wikipedia;(b) diversifying the knowledge base by combining data from disparate sources can help improve the performance of financial expert systems;and (c) the use of simple machine learning models for inference and rule generation is appropriate with our rich knowledge database. Finally, an intelligent decision making tool is provided to assist investors in making trading decisions on any stock, commodity or index. (c) 2017 Elsevier Ltd. All rights reserved.

关键词： Artificial intelligence Feature selection Financial expert system Google news r programming Wikipedia

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Linear Model Selection and regularization for Serum Prostate-Specific Antigen Prediction of Patients with Prostate Cancer Using r

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IEEE ACCESS 2021年 9卷 97591-97602页

作者： Li, Gongli Li, Han Australian Natl Univ Canberra ACT 2601 Australia

Prostate cancer is the commonly diagnosed cancer worldwide, and there were 1,276 thousand new prostate cancer cases and 359 thousand deaths in 2018. Prostate-specific antigen (PSA) blood level is often elevated in men with prostate cancer, so PSA testing can detect prostate tumours when they are small, low-grade, and localized. The PSA testing is hard to apply on the less developed and poor areas without sufficient medical funds, so the early accurate PSA level prediction by statistical machine learning models is significant to avoid later stages of prostate cancer that spread outside the Prostate. In this literature, we compare three linear model selection and regularization methods (shrinkage, subset selection, dimension reduction) and nine candidate models (OLS regression, ridge regression, Lasso regression, Elastic net, best subset selection, forward subset selection, backward subset selection, PCr, PLS) based on leave-oneout-cross-validation (LOOCV) prediction error. As the selection criteria leave-one-out cross-validation is sensitive to outliers, Mahalanobis distance is used for outlier detection and deletion before running each model. The shrinkage method (only lasso and elastic net models) and subset selection method (based on adjusted r-2, BIC, Cp, and cross-validation prediction error) can select the variables out. The feature selection results show that prostate weight, cancer volume, amount of benign prostatic hyperplasia, and whether seminal vesicle invasion is necessary variables must include predicting PSA. Age and capsular penetration are the least important variables. The variables of Gleason score, a percent of Gleason scores 4 or 5 are essential sometimes. All the diagnostic figures and results are coded by r, open access, and published on IEEE Xplore Code Ocean.

关键词： Machine learning linear model selection and regularization prostate-specific antigen prediction prostate cancer screening r programming

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Quantification of rhBMP2 in bioactive bone materials

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regenerative Biomaterials 2020年第1期7卷 71-75页

作者： Huan Lian Han Wang Qianqian Han Chunren Wang Division of Biomaterials Department of Medical DevicesNational Institutes for Food and Drug ControlNo.31 Huatuo RoadBeijing 102629China

Bone morphogenetic protein(BMP),belongs to transforming growth factor-b(TGF-b)superfamily except *** BMP into muscular tissues induces ectopic bone formation at the site of implantation,which provides opportunity for the treatment of bone *** human BMP-2(rhBMP-2)has been used clinically,but the lack of standard methods for quantifying rhBMP-2 biological activity greatly hindered the progress of *** this article,we describe an in vitro rhBMP-2 quantification method,as well as the data analyzation pipeline through logistic regression in *** studies indicated that alkaline phosphatase(ALP)activity of C2C12 cells was significantly increased when exposed to rhBMP-2,and showed dose-dependent effects in a certain concentration range of ***,we chose to quantify ALP activity as an indicator of rhBMP-2 bioactivity in vitro.A sigmoid relationship between the ALP activity and concentration of rhBMP-2 was ***,there are tons of regression models for such a non-linear *** has always been a major concern for researchers to choose a proper model that not only fit data accurately,but also have parameters representing practical ***,to fit our rhBMP-2 quantification data,we applied two logistic regression models,three-parameter log-logistic model and four-parameter log-logistic *** four-parameter log-logistic model(adj-r2>0.98)fits better than three-parameter log-logistic model(adj-r2>0.75)for the sigmoid ***,our results indicate rhBMP-2 quantification in vitro can be accomplished by detecting ALP activity and fitting four-parameter log-logistic ***,we also provide a highly adaptable r script for any additional logistic models.

关键词： rhBMP-2 bioactivity quantification statistical model fitting r programming

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A Survey of Orbitrap All Ion Fragmentation Analysis Assessed by an r MetaboList Package to Study Small-Molecule Metabolites

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CHrOMATOGrAPHIA 2018年第7期81卷 981-994页

作者： Sentandreu, Enrique Peris-Diaz, Manuel D. Sweeney, Shannon r. Chiou, Jennifer Munoz, Nathalie Tiziani, Stefano Dell Pediat Res Inst 1400 Barbara Jordan Blvd Austin TX 78723 USA Univ Texas Austin Dept Nutr Sci Austin TX 78712 USA Univ Texas Austin Inst Cellular & Mol Biol Austin TX 78712 USA Univ Wroclaw Fac Biotechnol Dept Chem Biol F Joliot Curie 14a PL-50383 Wroclaw Poland

Leukemia cell and melanoma tumor tissue extracts were studied for small (mostly m/z < 250) polar metabolites by LC-ESI-HrMSn analysis powered by a hybrid Quadrupole-Orbitrap. MS data were simultaneously acquired in fast polarity switching mode operating in MS1 and MS/MS (All Ion Fragmentation, AIF) full-scan analyses at high mass resolution. Positive metabolite assignments were achieved by AIF analysis considering at least two characteristic transitions. Targeted metabolite profiling was achieved by the relative quantification of 18 metabolites through spiking of their respective deuterated counterparts. Manual data processing of MS1 and AIF scans were compared for the accurate determination of natural metabolites and their deuterated analogs by chromatographic alignment and peak area integration. Evaluation of manual and automated (MetaboList r package) AIF data processing yielded comparable results. The versatility of AIF analysis also enabled the untargeted metabolite profiling of leukemia and melanoma samples in which 22 and 53 compounds were, respectively, identified outside those studied by labeling. The main limitation of this method was that low abundance metabolites with scan rates below 8 scans/peak could not be accurately quantified by AIF analysis. The combination of AIF analysis with MetaboList r package represents an opportunity to move towards automated, faster, and more global metabolomics approaches supported by an entirely flexible open source data processing platform freely available from Comprehensive r Archive Network (CrAN, https://***/package=MetaboList).

关键词： Liquid chromatography High resolution mass spectrometry Orbitrap All ion fragmentation Targeted analysis Untargeted analysis Metabolomics r programming MS/MS automated data processing MetaboList

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Applying intelligent data traffic adaptation to high-performance multiple big data analytics platforms

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COMPUTErS & ELECTrICAL ENGINEErING 2018年 70卷 998-1018页

作者： Chang, Bao rong Tsai, Hsiu-Fen Liao, Po-Hao Natl Univ Kaohsiung Dept Comp Sci & Informat Engn 700 Kaohsiung Univ Rd Kaohsiung 811 Taiwan Shu Te Univ Dept Mkt Management 59 Hun Shang Rd Yen Chao 824 Kaohsiung Count Taiwan

This paper introduces the integration of two data processing platforms, rHhadoop and Sparkr, to carry out rapid big data retrieval and analytics using r programming, which can serve as part of business intelligence. Besides, it has developed the job scheduling optimization called Memory-Sensitive Heterogeneous Earliest Finish Time algorithm to enhance system throughput. However, the bottleneck of system throughput is definitely relevant to data traffic problem over network, especially a large amount of data exchange between distributed computing nodes within a cluster. The objective of this paper is to propose an intelligence approach to tackle the crucial problems of inefficient data traffic flow. Adaptive network-based fuzzy inference system along with particle swarm optimization has employed to tune the network-related parameters at computing nodes for improving network QoS and speed up data transportation significantly. In order to examine the computing efficiency, performance index has been evaluated for all of treatments in the experiment. (C) 2017 Elsevier Ltd. All rights reserved.

关键词： Big data platform Intelligent data traffic adaptation r programming Scheduling optimization algorithm Performance index

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