Access To Web Services Research Articles

MiRbiom: Machine-learning on Bayesian causal nets of RBP-miRNA interactions successfully predicts miRNA profiles.

Formation of mature miRNAs and their expression is a highly controlled process. It is very much dependent upon the post-transcriptional regulatory events. Recent findings suggest that several RNA binding proteins beyond Drosha/Dicer are involved in the processing of miRNAs. Deciphering of conditional networks for these RBP-miRNA interactions may help to reason the spatio-temporal nature of miRNAs which can also be used to predict miRNA profiles. In this direction, >25TB of data from different platforms were studied (CLIP-seq/RNA-seq/miRNA-seq) to develop Bayesian causal networks capable of reasoning miRNA biogenesis. The networks ably explained the miRNA formation when tested across a large number of conditions and experimentally validated data. The networks were modeled into an XGBoost machine learning system where expression information of the network components was found capable to quantitatively explain the miRNAs formation levels and their profiles. The models were developed for 1,204 human miRNAs whose accurate expression level could be detected directly from the RNA-seq data alone without any need of doing separate miRNA profiling experiments like miRNA-seq or arrays. A first of its kind, miRbiom performed consistently well with high average accuracy (91%) when tested across a large number of experimentally established data from several conditions. It has been implemented as an interactive open access web-server where besides finding the profiles of miRNAs, their downstream functional analysis can also be done. miRbiom will help to get an accurate prediction of human miRNAs profiles in the absence of profiling experiments and will be an asset for regulatory research areas. The study also shows the importance of having RBP interaction information in better understanding the miRNAs and their functional projectiles where it also lays the foundation of such studies and software in future.

A Literature Review on Trust Management in Web Services Access Control

A Literature Review on Trust Management in Web Services Access Control

Web Service Access Control Based on Browser Fingerprint Detection

Web services have covered all areas of social life, and various browsers have become necessary software on computers and mobile phones, and they are also the entrances to Web services. All kinds of threats to web data security continue to appear, so web services and browsers have become the focus of security. In response to the requirements of Web service for access entity identification and data access control, this paper proposes a multi-dimensional browser fingerprint detection method based on adversarial learning, and designs a Web service access control framework combined with browser fingerprint detection. Through the joint use of multi-dimensional browser features, adversarial learning is used to improve the accuracy and robustness of browser fingerprint detection; a cross-server and browser-side Web service access control framework is established by creating tags for Web data resources and access entities. Based on the mapping relationship between browser fingerprint detection entities and data resources, fine-grained hierarchical data access control is realized. Through experiments and analysis, the browser fingerprint detection method proposed in this paper is superior to existing machine learning detection methods in terms of accuracy and robustness. Based on the adversarial learning method, good detection results can be obtained in the case of a small number of user samples. At the same time, the open source data set is further used to verify the advantages of the method in this paper. The Web service access control framework can satisfy the requirements of Web data security control, is an effective supplement to user identification technology, and is implementable.

NcRDense: A novel computational approach for classification of non-coding RNA family by deep learning

With the rapidly growing importance of biological research, non-coding RNAs (ncRNA) attract more attention in biology and bioinformatics. They play vital roles in biological processes such as transcription and translation. Classification of ncRNAs is essential to our understanding of disease mechanisms and treatment design. Many approaches to ncRNA classification have been developed, several of which use machine learning and deep learning. In this paper, we construct a novel deep learning-based architecture, ncRDense, to effectively classify and distinguish ncRNA families. In a comparative study, our model produces comparable results with existing state-of-the-art methods. Finally, we built a freely accessible web server for the ncRDense tool, which is available at http://nsclbio.jbnu.ac.kr/tools/ncRDense/.

Evaluating Protein Tyrosine Phosphatase 1B Inhibitory Activity of Bioactive Compounds from Momordica charantia for Diabetes Treatment Type 2 by using Molecular Docking Method

Evaluating Protein Tyrosine Phosphatase 1B Inhibitory Activity of Bioactive Compounds from Momordica charantia for Diabetes Treatment Type 2 by using Molecular Docking Method

RFID Based Petrol Pump Automation System

Everything has been digitized. In many existing systems, almost all petrol pumps have a controlling unit to perform the tasks like managing the electrical pump, drive the display, measure the flow & accordingly turn OFF the electrical pump. But still a person is required to collect the money and there is card to access petrol at different petrol stations of different petrol companies across the country and here, we are connecting all these petrol stations using single web server This web server access is secured by a password which is known only to the petrol companies. Whenever we want to fill the tank from the fuel dispenser, we just have to place the RFID card near the RFID reader

Demetra Application: An integrated genotype analysis web server for clinical genomics in endometriosis.

Demetra Application is a holistic integrated and scalable bioinformatics web-based tool designed to assist medical experts and researchers in the process of diagnosing endometriosis. The application identifies the most prominent gene variants and single nucleotide polymorphisms (SNPs) causing endometriosis using the genomic data provided for the patient by a medical expert. The present study analyzed >28.000 endometriosis-related publications using data mining and semantic techniques aimed towards extracting the endometriosis-related genes and SNPs. The extracted knowledge was filtered, evaluated, annotated, classified, and stored in the Demetra Application Database (DAD). Moreover, an updated gene regulatory network with the genes implements in endometriosis was established. This was followed by the design and development of the Demetra Application, in which the generated datasets and results were included. The application was tested and presented herein with whole-exome sequencing data from seven related patients with endometriosis. Endometriosis-related SNPs and variants identified in genome-wide association studies (GWAS), whole-genome (WGS), whole-exome (WES), or targeted sequencing information were classified, annotated and analyzed in a consolidated patient profile with clinical significance information. Probable genes associated with the patient's genomic profile were visualized using several graphs, including chromosome ideograms, statistic bars and regulatory networks through data mining studies with relative publications, in an effort to obtain a representative number of the most credible candidate genes and biological pathways associated with endometriosis. An evaluation analysis was performed on seven patients from a three-generation family with endometriosis. All the recognized gene variants that were previously considered to be associated with endometriosis were properly identified in the output profile per patient, and by comparing the results, novel findings emerged. This novel and accessible webserver tool of endometriosis to assist medical experts in the clinical genomics and precision medicine procedure is available at http://geneticslab.aua.gr/.

I6mA-VC: A Multi-Classifier Voting Method for the Computational Identification of DNA N6-methyladenine Sites.

DNA N6-methyladenine (6mA), as an essential component of epigenetic modification, cannot be neglected in genetic regulation mechanism. The efficient and accurate prediction of 6mA sites is beneficial to the development of biological genetics. Biochemical experimental methods are considered to be time-consuming and laborious. Most of the established machine learning methods have a single dataset. Although some of them have achieved cross-species prediction, their results are not satisfactory. Therefore, we designed a novel statistical model called i6mA-VC to improve the accuracy for 6mA sites. On the one hand, kmer and binary encoding are applied to extract features, and then gradient boosting decision tree (GBDT) embedded method is applied as the feature selection strategy. On the other hand, DNA sequences are represented by vectors through the feature extraction method of ring-function-hydrogen-chemical properties (RFHCP) and the feature selection strategy of ExtraTree. After fusing the two optimal features, a voting classifier based on gradient boosting decision tree (GBDT), light gradient boosting machine (LightGBM) and multilayer perceptron classifier (MLPC) is constructed for final classification and prediction. The accuracy of Rice dataset and M.musculus dataset with five-fold cross-validation are 0.888 and 0.967, respectively. The cross-species dataset is selected as independent testing dataset, and the accuracy reaches 0.848. Through rigorous experiments, it is demonstrated that the proposed predictor is convincing and applicable. The development of i6mA-VC predictor will become an effective way for the recognition of N6-methyladenine sites, and it will also be beneficial for biological geneticists to further study gene expression and DNA modification. In addition, an accessible web-server for i6mA-VC is available from http://www.zhanglab.site/ .

French FastContext: A publicly accessible system for detecting negation, temporality and experiencer in French clinical notes

The context of medical conditions is an important feature to consider when processing clinical narratives. NegEx and its extension ConText became the most well-known rule-based systems that allow determining whether a medical condition is negated, historical or experienced by someone other than the patient in English clinical text. In this paper, we present a French adaptation and enrichment of FastContext which is the most recent, n-trie engine-based implementation of the ConText algorithm. We compiled an extensive list of French lexical cues by automatic and manual translation and enrichment. To evaluate French FastContext, we manually annotated the context of medical conditions present in two types of clinical narratives: (i)death certificates and (ii)electronic health records. Results show good performance across different context values on both types of clinical notes (on average 0.93 and 0.86 F1, respectively). Furthermore, French FastContext outperforms previously reported French systems for negation detection when compared on the same datasets and it is the first implementation of contextual temporality and experiencer identification reported for French. Finally, French FastContext has been implemented within the SIFR Annotator: a publicly accessible Web service to annotate French biomedical text data (http://bioportal.lirmm.fr/annotator). To our knowledge, this is the first implementation of a Web-based ConText-like system in a publicly accessible platform allowing non-natural-language-processing experts to both annotate and contextualize medical conditions in clinical notes.

Screenning Bioactive Compounds from Allium sativum as HER2 Inhibitors Targeting Breast Cancer by Docking Methods

Screenning Bioactive Compounds from Allium sativum as HER2 Inhibitors Targeting Breast Cancer by Docking Methods

EZmito: a simple and fast tool for multiple mitogenome analyses

Complete mitochondrial genome data are frequently applied to address phylogenetic/phylogeographic issues at different taxonomic levels in ecology and evolution. While sample preparation/sequencing is becoming more and more straightforward thanks to dropping costs for next-generation sequencing (NGS), data preparation and visualization remains a manually intensive step that may lead to errors if improperly conducted. We have elaborated, and here introduce, EZmito, a simple and intuitive, freely accessible Web Server aimed at automating some of these tasks. EZmito is divided into three main tools: EZpipe that assembles DNA matrices for phylo-mitogenomic analyses; EZskew that calculates genome, strand, and codon nucleotide compositional skews and EZcodon which computes Relative Synonymous Codon Usage statistics as well as amino acid usage frequency over multiple mitogenomes. Output is produced in tabular format as well as publication-quality graphics.

Implementation of firewall for web server access management based on application gateway for TNI AD website

Along with the development of information technology, making information security is very important even more so on a network connected to the internet. The imbalance between each development of a technology not accompanied by development in the security system itself. As for the current problem is that there are still state website that are still working with outside companies in terms of network security systems. And most often the target of cyber attacks is the web server. Then any individual or organization is required to have a security system that minimizes of cyber crime. Instead of putting trust in an outside company I better make my own web server security system. The author uses the application gateway method in a firewall. Besides, firewall failover capabilities are evaluated. Based on the overall testing results, application gateway has better performance than other types of firewalls in another website. It has better ability in handling DoS attack, because this firewall works not only based on the secure, destination and attribute of the package, but can reach the contents of the package. It is conclu1ded that application gateway is suitable and safe to use on TNI AD web server.

Identification of RNA pseudouridine sites using deep learning approaches.

Pseudouridine(Ψ) is widely popular among various RNA modifications which have been confirmed to occur in rRNA, mRNA, tRNA, and nuclear/nucleolar RNA. Hence, identifying them has vital significance in academic research, drug development and gene therapies. Several laboratory techniques for Ψ identification have been introduced over the years. Although these techniques produce satisfactory results, they are costly, time-consuming and requires skilled experience. As the lengths of RNA sequences are getting longer day by day, an efficient method for identifying pseudouridine sites using computational approaches is very important. In this paper, we proposed a multi-channel convolution neural network using binary encoding. We employed k-fold cross-validation and grid search to tune the hyperparameters. We evaluated its performance in the independent datasets and found promising results. The results proved that our method can be used to identify pseudouridine sites for associated purposes. We have also implemented an easily accessible web server at http://103.99.176.239/ipseumulticnn/.

Geospatial and Semantic Mapping Platform for Massive COVID-19 Scientific Publication Search

Thousands of results of scientific publications on the topic of COVID-19 have emerged within a short time, raising significant challenges for scholars to organize the research, or even synthesize the knowledge in a timely or comprehensive manner. To facilitate this, we have developed a geospatial and semantic mapping platform to search and organize these large and unmapped digital collections. The semantic map visualizes research topics based on customized natural language processing algorithms, which helps users to identify their content of specific interest beyond keyword searches in web search engines. The resultant geospatial map extracts all the location names mentioned in the publications, illustrating where COVID-19 studies have been conducted and where neglected study areas might exist. The system has been deployed as a web service for public access.

A Method to Prevent SQL Injection Attack using an Improved Parameterized Stored Procedure

Structured Query Language (SQL) injection is one of the critical threats to database security. The effects of SQL injection attacks cause the data contained in the database to be at risk of being exploited by irresponsible parties, compromising data integrity, disrupting server operations and in return affecting the organization's image. Although SQL injection is an attack performed at the application level, SQL injection prevention requires security controls at all levels, namely application level, database level and network level. The absence of SQL injection prevention measures at the application level makes the database vulnerable to attack. Reviews indicate that the current approaches still not sufficient in addressing these three issues, which are i) improper use of dynamic SQL, ii) lack of input validation process and iii) inconsistent error handling. Currently, program and database code security is based solely on basic security measures that are focused at the network level such as network firewalls, database access control and web server request filtering. Unfortunately, these measures are still inadequate and not sufficient to safe guard the program code and databases from the attack. To overcome this shortcoming as addressed by these three issues, a new comprehensive method is proposed using an improved parameterized stored procedure to enhance database security. Experimental results prove that the proposed method is able to prevent SQL injection from occurring and able to shorten the processing time when compared with existing methods, hence able to improve database security.

Predicting Disasters Using Global Water Intelligence

Accurate knowledge of the water cycle is essential for predicting disasters such as floods and droughts. However, it’s not easy to obtain good information from traditional weather and water forecasts. The Group on Earth Observations Global Water Sustainability initiative (GEOGloWS) provides hydrologic forecasts through an accessible web service to assist local water users. Partnering with water scientists worldwide, Professor Jim Nelson of Brigham Young University worked with the European Centre for Medium-Range Weather Forecasts to develop a global streamflow service. This service provides local communities with actionable water intelligence, enabling them to focus on solutions to water-related problems.

In silico identification of natural products from Centella asiatica as severe acute respiratory syndromecoronavirus 2 main protease inhibitor.

Severe acute respiratory syndrome-coronavirus 2 (SARS-CoV-2) main protease (S-CoV-2 Mpro) is one of the main targets in designing antiviral against SARS-CoV-2. Centella asiatica contains several triterpenoids, polyacetylenes, and benzoic ester derivative with various biological activities including anti-inflammation and antiviral. Triterpenoids from C. asiatica could act as inhibitors of S-CoV-2 Mpro. The main objective of this study was to identify potential natural products from C. asiatica as S-CoV-2 Mpro inhibitor with better pharmacokinetic through in silico molecular docking method. : As much as 11 compounds from C. asiatica were docked with S-CoV-2 Mpro (PDB ID: 6LU7) using AutoDock v4.2.6. Pharmacokinetic parameters of these compounds were assessed using SwissADME (free access webserver). Molecular docking results of 11 natural products indicated that asiatate 6 and asiatate 10 have strong interaction with quite similar binding free energy compared to native ligand (‒9.00 and‒9.58 kcal/mol compared to ‒9.18 kcal/mol, respectively) with proper interaction to the catalytic dyad (His41 and Cys145). Pharmacokinetic analysis revealed that asiatate 4, asiatate 10, and asiatate 11 have poor pharmacokinetic properties. These results indicated that asiatate 6 could be recommended for further study as S-CoV-2 Mpro inhibitor.

Screening Virtual ACE2 Enzyme Inhibitory Activity of Compounds for COVID-19 Treatment Based on Molecular Docking

Screening Virtual ACE2 Enzyme Inhibitory Activity of Compounds for COVID-19 Treatment Based on Molecular Docking

IAMY-SCM: Improved prediction and analysis of amyloid proteins using a scoring card method with propensity scores of dipeptides

Fast, accurate identification and characterization of amyloid proteins at a large-scale is essential for understating their role in therapeutic intervention strategies. As a matter of fact, there exist only one in silico model for amyloid protein identification using the random forest (RF) model in conjunction with various feature types namely the RFAmy. However, it suffers from low interpretability for biologists. Thus, it is highly desirable to develop a simple and easily interpretable prediction method with robust accuracy as compared to the existing complicated model. In this study, we propose iAMY-SCM, the first scoring card method-based predictor for predicting and analyzing amyloid proteins. Herein, the iAMY-SCM made use of a simple weighted-sum function in conjunction with the propensity scores of dipeptides for the amyloid protein identification. Cross-validation results indicated that iAMY-SCM provided an accuracy of 0.895 that corresponded to 10–22% higher performance than that of widely used machine learning models. Furthermore, iAMY-SCM achieving an accuracy of 0.827 as evaluated by an independent test, which was found to be comparable to that of RFAmy and was approximately 9–13% higher than widely used machine learning models. Furthermore, the analysis of estimated propensity scores of amino acids and dipeptides were performed to provide insights into the biophysical and biochemical properties of amyloid proteins. As such, this demonstrates that the proposed iAMY-SCM is efficient and reliable in terms of simplicity, interpretability and implementation. To facilitate ease of use of the proposed iAMY-SCM, a user-friendly and publicly accessible web server at http://camt.pythonanywhere.com/iAMY-SCM has been established. We anticipate that that iAMY-SCM will be an important tool for facilitating the large-scale prediction and characterization of amyloid protein.

IDPPIV-SCM: A Sequence-Based Predictor for Identifying and Analyzing Dipeptidyl Peptidase IV (DPP-IV) Inhibitory Peptides Using a Scoring Card Method

The inhibition of dipeptidyl peptidase IV (DPP-IV, E.C.3.4.14.5) is well recognized as a new avenue for the treatment of Type 2 diabetes (T2D). Until now, peptide-like DDP-IV inhibitors have been shown to normalize the blood glucose concentration in T2D subjects. To the best of our knowledge, there is yet no computational model for predicting and analyzing DPP-IV inhibitory peptides using sequence information. In this study, we present for the first time a simple and easily interpretable sequence-based predictor using the scoring card method (SCM) for modeling the bioactivity of DPP-IV inhibitory peptides (iDPPIV-SCM). Particularly, the iDPPIV-SCM was developed by employing the SCM method together with the propensity scores of amino acids. Rigorous independent test results demonstrated that the proposed iDPPIV-SCM was found to be superior to those of well-known machine learning (ML) classifiers (e.g., k-nearest neighbor, logistic regression, and decision tree) with demonstrated improvements of 2-11, 4-22, and 7-10% for accuracy, MCC, and AUC, respectively, while also achieving comparable results to that of the support vector machine. Furthermore, the analysis of estimated propensity scores of amino acids as derived from the iDPPIV-SCM was performed so as to provide a more in-depth understanding on the molecular basis for enhancing the DPP-IV inhibitory potency. Taken together, these results revealed that iDPPIV-SCM was superior to those of other well-known ML classifiers owing to its simplicity, interpretability, and validity. For the convenience of biologists, the predictive model is deployed as a publicly accessible web server at http://camt.pythonanywhere.com/iDPPIV-SCM. It is anticipated that iDPPIV-SCM can serve as an important tool for the rapid screening of promising DPP-IV inhibitory peptides prior to their synthesis.