Web Services Research Articles

PANGEN: an online platform for the comparison and creation of diagnostic gene panels.

Targeted gene panel sequencing is used to limit the search for causative genetic variants solely to genes with an established association with the phenotype. The design of gene panels is challenging due to the lack of consensus regarding phenotypic associations for some genes, which results in high variation in gene composition for the same panel offered by different laboratories. We developed PANGEN, a platform that provides a centralized resource for gene panel information, with the ability to compare and generate new intelligent diagnostic panels. Gene-phenotype associations were collected from 12 public and commercial sources (Blueprint, Cegat, Centogene, ClinGen, Fulgent, GeneDx, Health in Code, Human Phenotype Ontology, Invitae, PanelApp, Prevention genetics, and Pronto diagnostics). Gene-phenotype associations are categorized into tiers according to categories derived from the original source panel. Pairwise panel similarity was calculated by dividing the number of common genes by the total number of genes in both panels. Regions with extreme guanine-cytosine (GC) content were collected from the Genome in a Bottle stratifications dataset, and putative genomic duplications were retrieved from the University of Santa Cruz database. Overall, 1533 panels, 9759 phenotypes, and 6979 genes were collected. The platform provides an interface to (i) explore and compare collected panels, (ii) find similar panels, (iii) identify genes with high GC content or duplication levels, (iv) generate gene panels by combining panels from various sources, and (v) stratify a generated panel into genes with a strong phenotype association ('core') and those with a weaker association ('extended'). The presented platform represents a unique resource for gene panel exploration and comparison that facilitates the generation of tailored diagnostic panels through a public online web server. Database URL: https://c-gc.shinyapps.io/PANGEN/.

M3S-ALG: Improved and robust prediction of allergenicity of chemical compounds by using a novel multi-step stacking strategy

A wide variety of chemicals cannot be introduced to the marketplace because of their high allergenicity. Therefore, it is fundamentally crucial to assess the allergenic potential of chemicals before introducing them into clinical therapeutics. However, assessing the allergenicity of chemical compounds experimentally is time-consuming and costly. To tackle this challenge, we propose M3S-ALG, a novel multi-step stacking strategy (M3S) for rapid and accurate identification of the allergenicity of chemical compounds by using only the SMILES notation. The proposed M3S method involves three steps, as follows. First, ten different balanced datasets were constructed using an under-sampling approach. Second, for each balanced dataset, 144 base-classifiers were trained and optimized to generate the prediction scores of allergenic chemical compounds considered as new probabilistic features. Third, we selected the important probabilistic features and employed them to construct the final stacked model (M3S-ALG). Experimental results show that M3S-ALG outperforms conventional ensemble strategies and its constituent base-classifiers on both the training and independent test datasets. This indicates the effectiveness and robustness of our proposed strategy in identifying the allergenicity of chemical compounds. In addition, M3S-ALG exhibited excellent prediction performance compared to existing methods on the independent test dataset, achieving a balanced accuracy of 0.877, MCC of 0.712, and AUC of 0.931. Finally, we developed a user-friendly online web server at https://pmlabqsar.pythonanywhere.com/M3SALG. This new approach is anticipated to facilitate the drug discovery and development community for the large-scale identification of chemical compounds with no allergenic properties.

Rapid, Accurate, Ranking of Protein-Ligand Binding Affinities with VM2, the Second-Generation Mining Minima Method.

The structure-based technologies most widely used to rank the affinities of candidate small molecule drugs for proteins range from faster but less reliable docking methods to slower but more accurate explicit solvent free energy methods. In recent years, we have advanced another technology, which is called mining minima because it "mines" out the main contributions to the chemical potentials of the free and bound molecular species by identifying and characterizing their main local energy minima. The present study provides systematic benchmarks of the accuracy and computational speed of mining minima, as implemented in the VeraChem Mining Minima Generation 2 (VM2) code, across two well-regarded protein-ligand benchmark data sets, for which there are already benchmark data for docking, free energy, and other computational methods. A core result is that VM2's accuracy approaches that of explicit solvent free energy methods at a far lower computational cost. In finer-grained analyses, we also examine the influence of various run settings, such as the treatment of crystallographic water molecules, on the accuracy, and define the costs in time and dollars of representative runs on Amazon Web Services (AWS) compute instances with various CPU and GPU combinations. We also use the benchmark data to determine the importance of VM2's correction from generalized Born to finite-difference Poisson-Boltzmann results for each energy well and find that this correction affords a remarkably consistent improvement in accuracy at a modest computational cost. The present results establish VM2 as a distinctive technology for early-stage drug discovery, which provides a strong combination of efficiency and predictivity.

Web-based Internet of Things on environmental and lighting control and monitoring system using node-RED, MQTT and Modbus communications within embedded Linux platform

This paper presents our innovative approach in the field of Internet of Things (IoT). Our focus was on enhancing the research environment at Cheng Shiu University's laboratory through the creation of a dynamic network monitoring setup and lighting control system. Our aim was to simplify equipment management and promote energy efficiency. The core of our development is the Raspberry Pi 4B embedded Linux platform, which serves as a host for both a Message Queuing Telemetry Transport (MQTT) broker and a Node-RED server. Python programs, developed in PyCharm IDE, utilize multiple threads for efficient data exchange via Modbus TCP and MQTT protocols, facilitating seamless interaction between PLCs and MQTT broker servers. The Node-RED IoT development tool was instrumental in crafting a cross-platform Human-Machine Interface (HMI) web server with MQTT Publish/Subscribe capabilities, ensuring smooth communication with the MQTT broker server. By configuring the Apache HTTP server to direct to the Node-RED web directory, we can effectively monitor laboratory conditions and manage the lighting system. The experimental results validate the cost-effectiveness of our approach in transforming traditional control systems into a web-based supervisory control system, thereby enhancing the overall functionality of existing equipment. This project underscores our commitment to advancing IoT solutions in practical settings.

In-silico screening of bioactive compounds of Moringa oleifera as potential inhibitors targeting HIF-1α/VEGF/GLUT-1 pathway against Breast Cancer.

Breast cancer is among the most heterogeneous and aggressive diseases and a foremost cause of death in women globally. Hypoxic activation of HIF-1α in breast cancers triggers the transcription of a battery of genes encoding proteins that facilitate tumor growth and metastasis and is correlated with a poor prognosis. Based on the reported cytotoxic and anti-cancer properties of Moringa oleifera (Mo), this study explores the inhibitory effect of bioactive compounds from M.oleifera and breast cancer target proteins HIF-1α, VEGF, and GLUT-1 in silico. The X-ray crystallographic structures of HIF-1α, VEGF, and GLUT1 were sourced from the Protein Data Bank(PDB) and docked with 70 3D PubChem structures of bioactive compounds of M.oleifera using AutoDock Vina, and binding modes were analyzed using Discovery Studio. Five compounds with the highest binding energies were selected and further drug-likeness, oral bioavailability, ADME, and toxicity profiles were analyzed using SwissADME, ADMETSaR, and ADMETlab 3.0 web server. Out of the screened 70 bioactive compounds, the top five compounds with the best binding energies were identified namely Apigenin, Ellagic Acid, Isorhamnetin, Luteolin, and Myricetin with each receptor. Molecular docking results indicated that the ligands interact strongly with the target HIF-1α, VEGF, and GLUT-1 receptors through hydrogen bonds and hydrophobic interactions. These compounds showed favorable drug-like and pharmacokinetic properties, possessed no substantial toxicity, and were fairly bioavailable. Results suggested that the compounds possess strong potential in developing putative lead compounds targeting HIF-1α that are safe natural plant-based drugs against breast cancer.

Automatic Chemical Profiling of Wine by Proton Nuclear Magnetic Resonance Spectroscopy.

We report the development of MagMet-W (magnetic resonance for metabolomics of wine), a software program that can automatically determine the chemical composition of wine via 1H nuclear magnetic resonance (NMR) spectroscopy. MagMet-W is an extension of MagMet developed for the automated metabolomic analysis of human serum by 1H NMR. We identified 70 compounds suitable for inclusion into MagMet-W. We then obtained 1D 1H NMR reference spectra of the pure compounds at 700 MHz and incorporated these spectra into the MagMet-W compound library. The processing of the wine NMR spectra and profiling of the 70 wine compounds were then optimized based on manual 1H NMR analysis. MagMet-W can automatically identify 70 wine compounds in most wine samples and can quantify them to 10-15% of the manually determined concentrations, and it can analyze multiple spectra simultaneously, at 10 min per spectrum. The MagMet-W Web server is available at https://www.magmet.ca.

Mapping Web Service Characteristics to Queueing Theory Models for Performance Analysis

The article presents a comprehensive analysis of mapping the characteristics of Web services to queueing theory models, specifically the M/M/1 queueing model. The authors aim to establish a theoretical foundation for modeling the performance of Web services and deriving response time formulae. The key points covered in the article are as follows: 1. Analyzing the characteristics of Web services: The article examines the six fundamental characteristics of queueing systems, including arrival patterns, service patterns, queue discipline, system capacity, and the number of servers, and maps them to the Web service environment. 2. Representation using Kendall's notation: The Web service characteristics are represented using Kendall's notation, resulting in the M/M/1/∞/FCFS model, where M denotes exponential distributions for arrival and service patterns, 1 represents a single server, ∞ represents infinite system capacity, and FCFS represents the first-come, first-served queue discipline. 3. Derivation of response time formulae: The article provides a detailed derivation of the response time formulae for the M/M/1 model using stochastic processes, Markov chains, and the birth-death process. The derivation incorporates steady-state conditions and results in a formula that calculates the probability of completing a request within a user-specified response time. 4. Practical applications: The derived response time formula can be used by service providers to determine the probability of completing user requests within specified Service Level Agreements (SLAs). By setting a threshold value (filter value), the service provider can select requests with a higher probability of completion for further processing. Overall, the article contributes to the understanding of Web service performance modeling by establishing a theoretical foundation based on queueing theory. The mapping of Web service characteristics to the M/M/1 model and the derivation of response time formulae provide valuable insights for service providers to analyze and optimize the performance of their Web services while adhering to SLAs

Executing automotive test workflows with BPMN and web service orchestration in software-defined car manufacturing

Executing automotive test workflows with BPMN and web service orchestration in software-defined car manufacturing

PiscesCSM: prediction of anticancer synergistic drug combinations

While drug combination therapies are of great importance, particularly in cancer treatment, identifying novel synergistic drug combinations has been a challenging venture. Computational methods have emerged in this context as a promising tool for prioritizing drug combinations for further evaluation, though they have presented limited performance, utility, and interpretability. Here, we propose a novel predictive tool, piscesCSM, that leverages graph-based representations to model small molecule chemical structures to accurately predict drug combinations with favourable anticancer synergistic effects against one or multiple cancer cell lines. Leveraging these insights, we developed a general supervised machine learning model to guide the prediction of anticancer synergistic drug combinations in over 30 cell lines. It achieved an area under the receiver operating characteristic curve (AUROC) of up to 0.89 on independent non-redundant blind tests, outperforming state-of-the-art approaches on both large-scale oncology screening data and an independent test set generated by AstraZeneca (with more than a 16% improvement in predictive accuracy). Moreover, by exploring the interpretability of our approach, we found that simple physicochemical properties and graph-based signatures are predictive of chemotherapy synergism. To provide a simple and integrated platform to rapidly screen potential candidate pairs with favourable synergistic anticancer effects, we made piscesCSM freely available online at https://biosig.lab.uq.edu.au/piscescsm/ as a web server and API. We believe that our predictive tool will provide a valuable resource for optimizing and augmenting combinatorial screening libraries to identify effective and safe synergistic anticancer drug combinations.Scientific contributionThis work proposes piscesCSM, a machine-learning-based framework that relies on well-established graph-based representations of small molecules to identify and provide better predictive accuracy of syngenetic drug combinations. Our model, piscesCSM, shows that combining physiochemical properties with graph-based signatures can outperform current architectures on classification prediction tasks. Furthermore, implementing our tool as a web server offers a user-friendly platform for researchers to screen for potential synergistic drug combinations with favorable anticancer effects against one or multiple cancer cell lines.

Genetic fuzzy rules and hybrid QDCNN-F-DSAE for detecting attacker behavior with tuning of firewall

ABSTRACT The application of computer technology is increasing quickly as the field of information technology develops over time. Because of the growth of computer networks, there is a rise in network attacks. Moreover, the majority of transactions are conducted via the Internet, and then the web application security is becoming increasingly important. A network firewall shields the web servers from unauthorised and dangerous traffic. In this work, the hybrid Quantum Dilated Convolutional Neural Network fused Deep-Stacked Auto Encoder (QDCNN-F-DSAE) model with Genetic Fuzzy System (GFS) is developed for network protection. With the utilisation of genetic fuzzy system (GFS) and firewall tuning, the QDCNN-F-DSAE identified the attack, ransomware activity, and prompt decision-making. The data offered by the simulated environment is used to make decisions, and the decisions should be made quickly within a few seconds using the proposed QDCNN-F-DSAE with GFS. Furthermore, it identifies the attacks and alerts the users to store the data from the supplied source. The malicious function and protected private Local Area Network (LAN) are discovered and removed by the firewall after the formation of genetic fuzzy rules. Moreover, this model is estimated using the accuracy, sensitivity, and specificity measures that offered the finest values of 0.915, 0.908, and 0.920.

The judge, the AI, and the Crown: a collusive network

ABSTRACT The article examines the potential implications of ChatGPT ChatGPT (Generative Pre-trained Transformer) [Alec Radford and others, ‘Improving Language Understanding by Generative Pre-Training’ Amazon Web Services (2018) <https://s3-us-west-2.amazonaws.com/openai-assets/research-covers/language-unsupervised/language_understanding_paper.pdf> accessed 28 May 2024; When this article was submitted, only ChatGPT-3.5 and ChatGPT-4 was available. However, during the revision stage, ChatGPT-4o (also known as GPT-4 Omnus) was introduced to the market, as announced on 13 May 2024.] in judicial decision-making process. This is especially salient given that judicial decisions are made within an interactive ritual chain (Bergman Blix) rather than an ivory tower. This cutting-edge natural language processing model leverages deep learning techniques to potentially aid judiciary members in formulating legal judgements. Therefore, this article assesses the capabilities, limitations, and potential applications of ChatGPT, aiming to evaluate the model's feasibility as a collaborative contributor to legal judgements. In particular, the article examines the utilisation of ChatGPT as an adjunctive tool, supporting human judges in their decision-making processes and, consequently, establishing to what extent artificial intelligence (AI) is used as a tool or collaborator in judicial decision-making. This determines authorship and, accordingly, ownership of judgements.

Identification of Anticancer Peptides from the Genome of Candida albicans: in Silico Screening, in Vitro and in Vivo Validations.

Anticancer peptides (ACPs) are promising future therapeutics, but their experimental discovery remains time-consuming and costly. To accelerate the discovery process, we propose a computational screening workflow to identify, filter, and prioritize peptide sequences based on predicted class probability, antitumor activity, and toxicity. The workflow was applied to identify novel ACPs with potent activity against colorectal cancer from the genome sequences of Candida albicans. As a result, four candidates were identified and validated in the HCT116 colon cancer cell line. Among them, PCa1 and PCa2 emerged as the most potent, displaying IC50 values of 3.75 and 56.06 μM, respectively, and demonstrating a 4-fold selectivity for cancer cells over normal cells. In the colon xenograft nude mice model, the administration of both peptides resulted in substantial inhibition of tumor growth without causing significant adverse effects. In conclusion, this work not only contributes a proven computational workflow for ACP discovery but also introduces two peptides, PCa1 and PCa2, as promising candidates poised for further development as targeted therapies for colon cancer. The method as a web service is available at https://app.cbbio.online/acpep/home and the source code at https://github.com/cartercheong/AcPEP_classification.git.

Contract-based Testing Using the Ontology Approach

This article analyzes the use of contract testing to verify the compatibility of two components, specifically web servers that use an Application Programming Interface (API) for data transmission. Additionally, the article includes a comparison of APIs and contract tests and describes cases where the latter have an advantage. A contract structure for contract testing is described. The article contains a description of the ontological approach for comparing knowledge about the business systems of the provider, in the form of ontologies, with the software representation of the consumer system, stored in the form of classes. A structure of the object that supplements the contract by providing it with business logic level checks is developed. Basic predicates that form the basis of this method are proposed and described. The use of the mapping field allows for the use of thesauruses or dictionaries for automated replacement of concepts when using synonym words. An example is provided that demonstrates the operation of this approach, namely: a simplified representation of the provider system’s concepts, a simplified structure of the consumer system’s classes, an example of an API and a contract for it, an extension using the developed approach, and the result of test execution.

Infrared Spectroscopy of SARS-CoV-2 Viral Protein: from Receptor Binding Domain to Spike Protein.

Spike (S) glycoprotein is the largest structural protein of SARS-CoV-2 virus and the main one involved in anchoring of the host receptor ACE2 through the receptor binding domain (RBD). S protein secondary structure is of great interest for shedding light on various aspects, from functionality to pathogenesis, finally to spectral fingerprint for the design of optical biosensors. In this paper, the secondary structure of SARS-CoV-2 S protein and its constituting components, namely RBD, S1 and S2 regions, are investigated at serological pH by measuring their amide I infrared absorption bands through Attenuated Total Reflection Infrared (ATR-IR) spectroscopy. Experimental data in combination with MultiFOLD predictions, Define Secondary Structure of Proteins (DSSP) web server and Gravy value calculations, provide a comprehensive understanding of RBD, S1, S2, and S proteins in terms of their secondary structure content, conformational order, and interaction with the solvent.

Information System For Thesis Title Submission At The Faculty Of Computer Science, Dehasen University Bengkulu Based On Web Service

Information System For Thesis Title Submission At The Faculty Of Computer Science, Dehasen University Bengkulu Based On Web Service

ExpHRD: an individualized, transcriptome-based prediction model for homologous recombination deficiency assessment in cancer

BackgroundHomologous recombination deficiency (HRD) stands as a clinical indicator for discerning responsive outcomes to platinum-based chemotherapy and poly ADP-ribose polymerase (PARP) inhibitors. One of the conventional approaches to HRD prognostication has generally centered on identifying deleterious mutations within the BRCA1/2 genes, along with quantifying the genomic scars, such as Genomic Instability Score (GIS) estimation with scarHRD. However, the scarHRD method has limitations in scenarios involving tumors bereft of corresponding germline data. Although several RNA-seq-based HRD prediction algorithms have been developed, they mainly support cohort-wise classification, thereby yielding HRD status without furnishing an analogous quantitative metric akin to scarHRD. This study introduces the expHRD method, which operates as a novel transcriptome-based framework tailored to n-of-1-style HRD scoring.ResultsThe prediction model has been established using the elastic net regression method in the Cancer Genome Atlas (TCGA) pan-cancer training set. The bootstrap technique derived the HRD geneset for applying the expHRD calculation. The expHRD demonstrated a notable correlation with scarHRD and superior performance in predicting HRD-high samples. We also performed intra- and extra-cohort evaluations for clinical feasibility in the TCGA-OV and the Genomic Data Commons (GDC) ovarian cancer cohort, respectively. The innovative web service designed for ease of use is poised to extend the realms of HRD prediction across diverse malignancies, with ovarian cancer standing as an emblematic example.ConclusionsOur novel approach leverages the transcriptome data, enabling the prediction of HRD status with remarkable precision. This innovative method addresses the challenges associated with limited available data, opening new avenues for utilizing transcriptomics to inform clinical decisions.

NRIMD, a Web Server for Analyzing Protein Allosteric Interactions Based on Molecular Dynamics Simulation.

Long-range allosteric communication between distant sites and active sites in proteins is central to biological regulation but still poorly characterized, limiting the development of protein engineering and drug design. Addressing this gap, NRIMD is an open-access web server for analyzing long-range interactions in proteins from molecular dynamics (MD) simulations, such as the effect of mutations at distal sites or allosteric ligand binding at allosteric sites on the active center. Based on our recent works on neural relational inference using graph neural networks, this cloud-based web server accepts MD simulation data on any length of residues in the alpha-carbon skeleton format from mainstream MD software. The input trajectory data are validated at the frontend deployed on the cloud and then processed on the backend deployed on a high-performance computer system with a collection of complementary tools. The web server provides a one-stop-shop MD analysis platform to predict long-range interactions and their paths between distant sites and active sites. It provides a user-friendly interface for detailed analysis and visualization. To the best of our knowledge, NRIMD is the first-of-its-kind online service to provide comprehensive long-range interaction analysis on MD simulations, which significantly lowers the barrier of predictions on protein long-range interactions using deep learning. The NRIMD web server is publicly available at https://nrimd.luddy.indianapolis.iu.edu/.

SimSpliceEvol2: alternative splicing-aware simulation of biological sequence evolution and transcript phylogenies

BackgroundSimSpliceEvol is a tool for simulating the evolution of eukaryotic gene sequences that integrates exon-intron structure evolution as well as the evolution of the sets of transcripts produced from genes. It takes a guide gene tree as input and generates a gene sequence with its transcripts for each node of the tree, from the root to the leaves. However, the sets of transcripts simulated at different nodes of the guide gene tree lack evolutionary connections. Consequently, SimSpliceEvol is not suitable for evaluating methods for transcript phylogeny inference or gene phylogeny inference that rely on transcript conservation.ResultsHere, we introduce SimSpliceEvol2, which, compared to the first version, incorporates an explicit model of transcript evolution for simulating alternative transcripts along the branches of a guide gene tree, as well as the transcript phylogenies inferred. We offer a comprehensive software with a graphical user interface and an updated version of the web server, ensuring easy and user-friendly access to the tool.ConclusionSimSpliceEvol2 generates synthetic datasets that are useful for evaluating methods and tools for spliced RNA sequence analysis, such as spliced alignment methods, methods for identifying conserved transcripts, and transcript phylogeny reconstruction methods. The web server is accessible at https://simspliceevol.cobius.usherbrooke.ca, where you can also download the standalone software. Comprehensive documentation for the software is available at the same address. For developers interested in the source code, which requires the installation of all prerequisites to run, it is provided at https://github.com/UdeS-CoBIUS/SimSpliceEvol.

The Open Pediatric Cancer Project.

In 2019, the Open Pediatric Brain Tumor Atlas (OpenPBTA) was created as a global, collaborative open-science initiative to genomically characterize 1,074 pediatric brain tumors and 22 patient-derived cell lines. Here, we extend the OpenPBTA to create the Open Pediatric Cancer (OpenPedCan) Project, a harmonized open-source multi-omic dataset from 6,112 pediatric cancer patients with 7,096 tumor events across more than 100 histologies. Combined with RNA-Seq from the Genotype-Tissue Expression (GTEx) and The Cancer Genome Atlas (TCGA), OpenPedCan contains nearly 48,000 total biospecimens (24,002 tumor and 23,893 normal specimens). We utilized Gabriella Miller Kids First (GMKF) workflows to harmonize WGS, WXS, RNA-seq, and Targeted Sequencing datasets to include somatic SNVs, InDels, CNVs, SVs, RNA expression, fusions, and splice variants. We integrated summarized CPTAC whole cell proteomics and phospho-proteomics data, miRNA-Seq data, and have developed a methylation array harmonization workflow to include m-values, beta-vales, and copy number calls. OpenPedCan contains reproducible, dockerized workflows in GitHub, CAVATICA, and Amazon Web Services (AWS) to deliver harmonized and processed data from over 60 scalable modules which can be leveraged both locally and on AWS. The processed data are released in a versioned manner and accessible through CAVATICA or AWS S3 download (from GitHub), and queryable through PedcBioPortal and the NCI's pediatric Molecular Targets Platform. Notably, we have expanded PBTA molecular subtyping to include methylation information to align with the WHO 2021 Central Nervous System Tumor classifications, allowing us to create research- grade integrated diagnoses for these tumors. OpenPedCan data and its reproducible analysis module framework are openly available and can be utilized and/or adapted by researchers to accelerate discovery, validation, and clinical translation.

Computational Reconstruction of the Transcription Factor Regulatory Network Induced by Auxin in Arabidopsis thaliana L.

In plant hormone signaling, transcription factor regulatory networks (TFRNs), which link the master transcription factors to the biological processes under their control, remain insufficiently characterized despite their crucial function. Here, we identify a TFRN involved in the response to the key plant hormone auxin and define its impact on auxin-driven biological processes. To reconstruct the TFRN, we developed a three-step procedure, which is based on the integrated analysis of differentially expressed gene lists and a representative collection of transcription factor binding profiles. Its implementation is available as a part of the CisCross web server. With the new method, we distinguished two transcription factor subnetworks. The first operates before auxin treatment and is switched off upon hormone application, the second is switched on by the hormone. Moreover, we characterized the functioning of the auxin-regulated TFRN in control of chlorophyll and lignin biosynthesis, abscisic acid signaling, and ribosome biogenesis.