Access To Web Services Research Articles

MuMCyp_Net: A multimodal neural network for the prediction of Cyp450 inhibition

The cytochrome P450 (CYP450) enzymes, heme-containing monooxygenases, are indispensable in metabolizing a broad range of drugs and other xenobiotics. Nonetheless, specific chemical entities (molecules) can inhibit these enzymes, resulting in undesirable drug-drug interactions, alterations in pharmacokinetics, and potentially compromising the efficacy and safety of drugs. Therefore, it is critical to identify molecules exhibiting inhibitory activities toward the five majors human CYP450 enzymes (1A2, 2C9, 2C19, 2D6, and 3A4) in the preliminary phases of drug development. This study presents a novel deep-learning model, MuMCyp_Net, for predicting CYP450 inhibition by small molecules. The architecture of MuMCyp_Net integrates a convolutional neural network (CNN) with an Attention mechanism, a bi-directional gated recurrent unit (biGRU) network, and a deep neural network (DNN) to efficiently process local chemical context information and global molecular properties of molecules. A total of 25,753 distinct compound molecules were utilized to train and evaluate the model’s performance. The proposed model demonstrates competitive performance as indicated by matthew’s correlation coefficients (MCC), ranging from 0.63 to 0.68, an accuracy rate between 0.82 and 0.90, and an AUC of 0.86 to 0.92. This novel approach shows considerable promise in accurately identifying CYP450 inhibitors, contributing to safer and more effective drug development. To further utilize the potential of the proposed model, a freely accessible web server tool for the virtual screening of molecules was developed, which will aid in identifying CYP450 inhibitors from non-inhibitors. The web-based tool can be accessed at https://mumcypnet.streamlit.app/.

Ac4C-AFL: A high-precision identification of human mRNA N4-acetylcytidine sites based on adaptive feature representation learning

RNA N4-acetylcytidine (ac4C) is a highly conserved RNA modification that plays a crucial role in controlling mRNA stability, processing, and translation. Consequently, accurate identification of ac4C sites across the genome is critical for understanding gene expression regulation mechanisms. In this study, we have developed ac4C-AFL, a bioinformatics tool that precisely identifies ac4C sites from primary RNA sequences. In ac4C-AFL, we identified the optimal sequence length for model building and implemented an adaptive feature representation strategy that is capable of extracting the most representative features from RNA. To identify the most relevant features, we proposed a novel ensemble feature importance scoring strategy to rank features effectively. We then used this information to conduct the sequential forward search, which individually determine the optimal feature set from the 16 sequence-derived feature descriptors. Utilizing these optimal feature descriptors, we constructed 176 baseline models using 11 popular classifiers. The most efficient baseline models were identified using the two-step feature selection approach, whose predicted scores were integrated and trained with the appropriate classifier to develop the final prediction model. Our rigorous cross-validations and independent tests demonstrate that ac4C-AFL surpasses contemporary tools in predicting ac4C sites. Moreover, we have developed a publicly accessible web server at https://balalab-skku.org/ac4C-AFL/.

PySupercharge: a python algorithm for enabling ABC transporter bacterial secretion of all proteins through amino acid mutation.

The process of producing proteins in bacterial systems and secreting them through ATP-binding cassette (ABC) transporters is an area that has been actively researched and used due to its high protein production capacity and efficiency. However, some proteins are unable to pass through the ABC transporter after synthesis, a phenomenon we previously determined to be caused by an excessive positive charge in certain regions of their amino acid sequence. If such an excessive charge is removed, the secretion of any protein through ABC transporters becomes possible. In this study, we introduce 'linear charge density' as the criteria for possibility of protein secretion through ABC transporters and confirm that this criterion can be applied to various non-secretable proteins, such as SARS-CoV-2 spike proteins, botulinum toxin light chain, and human growth factors. Additionally, we develop a new algorithm, PySupercharge, that enables the secretion of proteins containing regions with high linear charge density. It selectively converts positively charged amino acids into negatively charged or neutral amino acids after linear charge density analysis to enable protein secretion through ABC transporters. PySupercharge, which also minimizes functional/structural stability loss of the pre-mutation proteins through the use of sequence conservation data, is currently being operated on an accessible web server. We verified the efficacy of PySupercharge-driven protein supercharging by secreting various previously non-secretable proteins commonly used in research, and so suggest this tool for use in future research requiring effective protein production.

Unlocking the potential: A novel prognostic index signature for acute myeloid leukemia

Acute myeloid leukemia (AML) is an aggressive malignancy characterized by challenges in treatment, including drug resistance and frequent relapse. Recent research highlights the crucial roles of tumor microenvironment (TME) in assisting tumor cell immune escape and promoting tumor aggressiveness. This study delves into the interplay between AML and TME. Through the exploration of potential driver genes, we constructed an AML prognostic index (AMLPI). Cross-platform data and multi-dimensional internal and external validations confirmed that the AMLPI outperforms existing models in terms of areas under the receiver operating characteristic curves, concordance index values, and net benefits. High AMLPIs in AML patients were indicative of unfavorable prognostic outcomes. Immune analyses revealed that the high-AMLPI samples exhibit higher expression of HLA-family genes and immune checkpoint genes (including PD1 and CTLA4), along with lower T cell infiltration and higher macrophage infiltration. Genetic variation analyses revealed that the high-AMLPI samples associate with adverse variation events, including TP53 mutations, secondary NPM1 co-mutations, and copy number deletions. Biological interpretation indicated that ALDH2 and SPATS2L contribute significantly to AML patient survival, and their abnormal expression correlates with DNA methylation at cg12142865 and cg11912272. Drug response analyses revealed that different AMLPI samples tend to have different clinical selections, with low-AMLPI samples being more likely to benefit from immunotherapy. Finally, to facilitate broader access to our findings, a user-friendly and publicly accessible webserver was established and available at http://bioinfor.imu.edu.cn/amlpi. This server provides tools including TME-related AML driver genes mining, AMLPI construction, multi-dimensional validations, AML patients risk assessment, and figures drawing.

Indexing and real-time user-friendly queries in terabyte-sized complex genomic datasets with kmindex and ORA.

Public sequencing databases contain vast amounts of biological information, yet they are largely underutilized as it is challenging to efficiently search them for any sequence(s) of interest. We present kmindex, an approach that can index thousands of metagenomes and perform sequence searches in a fraction of a second. The index construction is an order of magnitude faster than previous methods, while search times are two orders of magnitude faster. With negligible false positive rates below 0.01%, kmindex outperforms the precision of existing approaches by four orders of magnitude. Here we demonstrate the scalability of kmindex by successfully indexing 1,393 marine seawater metagenome samples from the Tara Oceans project. Additionally, we introduce the publicly accessible web server Ocean Read Atlas, which enables real-time queries on the Tara Oceans dataset.

MRSLpred-a hybrid approach for predicting multi-label subcellular localization of mRNA at the genome scale.

In the past, several methods have been developed for predicting the single-label subcellular localization of messenger RNA (mRNA). However, only limited methods are designed to predict the multi-label subcellular localization of mRNA. Furthermore, the existing methods are slow and cannot be implemented at a transcriptome scale. In this study, a fast and reliable method has been developed for predicting the multi-label subcellular localization of mRNA that can be implemented at a genome scale. Machine learning-based methods have been developed using mRNA sequence composition, where the XGBoost-based classifier achieved an average area under the receiver operator characteristic (AUROC) of 0.709 (0.668-0.732). In addition to alignment-free methods, we developed alignment-based methods using motif search techniques. Finally, a hybrid technique that combines the XGBoost model and the motif-based approach has been developed, achieving an average AUROC of 0.742 (0.708-0.816). Our method-MRSLpred-outperforms the existing state-of-the-art classifier in terms of performance and computation efficiency. A publicly accessible webserver and a standalone tool have been developed to facilitate researchers (webserver: https://webs.iiitd.edu.in/raghava/mrslpred/).

Comprehensive Analysis and Evaluation of Anomalous User Activity in Web Server Logs

In this study, we present a novel machine learning framework for web server anomaly detection that uniquely combines the Isolation Forest algorithm with expert evaluation, focusing on individual user activities within NGINX server logs. Our approach addresses the limitations of traditional methods by effectively isolating and analyzing subtle anomalies in vast datasets. Initially, the Isolation Forest algorithm was applied to extensive NGINX server logs, successfully identifying outlier user behaviors that conventional methods often overlook. We then employed DBSCAN for detailed clustering of these anomalies, categorizing them based on user request times and types. A key innovation of our methodology is the incorporation of post-clustering expert analysis. Cybersecurity professionals evaluated the identified clusters, adding a crucial layer of qualitative assessment. This enabled the accurate distinction between benign and potentially harmful activities, leading to targeted responses such as access restrictions or web server configuration adjustments. Our approach demonstrates a significant advancement in network security, offering a more refined understanding of user behavior. By integrating algorithmic precision with expert insights, we provide a comprehensive and nuanced strategy for enhancing cybersecurity measures. This study not only advances anomaly detection techniques but also emphasizes the critical need for a multifaceted approach in protecting web server infrastructures.

ProFun-SOM: Protein Function Prediction for Specific Ontology Based on Multiple Sequence Alignment Reconstruction.

Protein function prediction is crucial for understanding species evolution, including viral mutations. Gene ontology (GO) is a standardized representation framework for describing protein functions with annotated terms. Each ontology is a specific functional category containing multiple child ontologies, and the relationships of parent and child ontologies create a directed acyclic graph. Protein functions are categorized using GO, which divides them into three main groups: cellular component ontology, molecular function ontology, and biological process ontology. Therefore, the GO annotation of protein is a hierarchical multilabel classification problem. This hierarchical relationship introduces complexities such as mixed ontology problem, leading to performance bottlenecks in existing computational methods due to label dependency and data sparsity. To overcome bottleneck issues brought by mixed ontology problem, we propose ProFun-SOM, an innovative multilabel classifier that utilizes multiple sequence alignments (MSAs) to accurately annotate gene ontologies. ProFun-SOM enhances the initial MSAs through a reconstruction process and integrates them into a deep learning architecture. It then predicts annotations within the cellular component, molecular function, biological process, and mixed ontologies. Our evaluation results on three datasets (CAFA3, SwissProt, and NetGO2) demonstrate that ProFun-SOM surpasses state-of-the-art methods. This study confirmed that utilizing MSAs of proteins can effectively overcome the two main bottlenecks issues, label dependency and data sparsity, thereby alleviating the root problem, mixed ontology. A freely accessible web server is available at http://bliulab.net/ ProFun-SOM/.

‘Practically useful, scientifically important, and to the honour of the country’: geological maps and services provided by the Geological Survey of Norway these past 165 years

Abstract Geological maps document knowledge of considerable value (economic, cultural and aesthetic) for societies, enterprises and people in general. Knowledge illustrated in geological maps also helps us to foresee how landscape interventions and climate change may affect our living ground and make us vulnerable to geohazards. The present paper presents an overview of geological mapping carried out by the Geological Survey of Norway since 1858 while also focusing on mapping activities and products produced in the present millennium. Key issues to consider are how we are moving into the digital world, how we use geological mapping to follow up on national agendas like Green Shift and Blue Growth and how national agreements and cooperation help us to make geological and other geographic information available to everyone through open access web portals and services.

Design and Build a Web Service for Financial Technology Dashboard at PT. Mitra Kasih Perkasa with React JS

PT Mitra Kasih Perkasa or MKP is a financial technology or fintech company, as well as a system integrator. MKP processes thousands, even millions, of financial transaction data every day, requiring efficient data management through the use of a dashboard. Information from the dashboard is used to monitor business processes and improve company performance. This research focuses on the development of efficient and effective web services for financial technology dashboards, integrating attractive and user-friendly user interfaces (UI/UX). Additionally, this research will discuss how to access web services using React JS, a popular JavaScript framework for responsive user interface development. The research will also outline the steps in building a financial technology dashboard that can help PT. Mitra Kasih Perkasa efficiently and effectively manage financial information using modern and innovative React JS technology.

MIST: A microbial identification and source tracking system for next-generation sequencing data.

The Professional Committee of Microbiology of the National Pharmacopoeia Commission organized the drafting of the Technical Guidelines for Microbial Whole Genome Sequencing (WGS), aiming to standardize the method process and technical indicators of microbial WGS and ensure the accuracy of sequencing and identification. On the basis of the Guidelines, we developed an integrated microbial identification and source tracking (MIST) system, which could meet the needs of microbial identification and contamination investigation in food and drug quality control. MIST integrates three analysis pipelines: 16S/18S/internal transcribed spacer amplicon-based microbial identification, WGS-based microbial identification, and single-nucleotide polymorphism-based microbial source tracking. MIST can analyze sequence data in a variety of formats, such as Fasta, base call file, and FASTQ. It can be connected to a high-throughput sequencing instrument to acquire sequencing data directly. We also developed a publicly accessible web server for MIST (http://syj.i-sanger.cn).

HiFun: homology independent protein function prediction by a novel protein-language self-attention model.

Protein function prediction based on amino acid sequence alone is an extremely challenging but important task, especially in metagenomics/metatranscriptomics field, in which novel proteins have been uncovered exponentially from new microorganisms. Many of them are extremely low homology to known proteins and cannot be annotated with homology-based or information integrative methods. To overcome this problem, we proposed a Homology Independent protein Function annotation method (HiFun) based on a unified deep-learning model by reassembling the sequence as protein language. The robustness of HiFun was evaluated using the benchmark datasets and metrics in the CAFA3 challenge. To navigate the utility of HiFun, we annotated 2 212 663 unknown proteins and discovered novel motifs in the UHGP-50 catalog. We proved that HiFun can extract latent function related structure features which empowers it ability to achieve function annotation for non-homology proteins. HiFun can substantially improve newly proteins annotation and expand our understanding of microorganisms' adaptation in various ecological niches. Moreover, we provided a free and accessible webservice at http://www.unimd.org/HiFun, requiring only protein sequences as input, offering researchers an efficient and practical platform for predicting protein functions.

Mitochondrial genome annotation with MFannot: a critical analysis of gene identification and gene model prediction.

Compared to nuclear genomes, mitochondrial genomes (mitogenomes) are small and usually code for only a few dozen genes. Still, identifying genes and their structure can be challenging and time-consuming. Even automated tools for mitochondrial genome annotation often require manual analysis and curation by skilled experts. The most difficult steps are (i) the structural modelling of intron-containing genes; (ii) the identification and delineation of Group I and II introns; and (iii) the identification of moderately conserved, non-coding RNA (ncRNA) genes specifying 5S rRNAs, tmRNAs and RNase P RNAs. Additional challenges arise through genetic code evolution which can redefine the translational identity of both start and stop codons, thus obscuring protein-coding genes. Further, RNA editing can render gene identification difficult, if not impossible, without additional RNA sequence data. Current automated mito- and plastid-genome annotators are limited as they are typically tailored to specific eukaryotic groups. The MFannot annotator we developed is unique in its applicability to a broad taxonomic scope, its accuracy in gene model inference, and its capabilities in intron identification and classification. The pipeline leverages curated profile Hidden Markov Models (HMMs), covariance (CMs) and ERPIN models to better capture evolutionarily conserved signatures in the primary sequence (HMMs and CMs) as well as secondary structure (CMs and ERPIN). Here we formally describe MFannot, which has been available as a web-accessible service (https://megasun.bch.umontreal.ca/apps/mfannot/) to the research community for nearly 16 years. Further, we report its performance on particularly intron-rich mitogenomes and describe ongoing and future developments.

Automated identification and quantification of metabolites in human fecal extracts by nuclear magnetic resonance spectroscopy.

We report the development of a software program, called MagMet-F, that automates the processing and quantification of 1D 1 H NMR of human fecal extracts. To optimize the program, we identified 82 potential fecal metabolites using 1D 1 H NMR of six human fecal extracts using manual profiling and a literature review of known fecal metabolites. We acquired pure versions of those metabolites and then acquired their 1D 1 H NMR spectra at 700 MHz to generate a fecal metabolite spectral library for MagMet-F. The fitting of these metabolites by MagMet-F was iteratively optimized to replicate manual profiling. We validated MagMet-F's automated profiling using a test set of six fecal extracts. It correctly identified 80% of the compounds and quantified those within <20% of the values determined by manual profiling using Chenomx. We also compared MagMet-F's profiling performance to two other open-access NMR profiling tools, Bayesil and Batman. MagMet-F outperformed both. Bayesil repeatedly overestimated metabolite concentrations by 10% to 40% while Batman was unable to properly quantify any compounds and took 10-20× longer. We have implemented MagMet-F as a freely accessible web server to enable automated, fast and convenient 1D 1 H NMR spectral profiling of fecal samples. MagMet-F is available at https://www.magmet.ca.

In Silico Study of the 5-Hydroxytryptamine-2C Receptor Antagonist Activity of Anthocyanins as Antidepressant Therapy

This study aimed to evaluate the drug-likeness, pharmacokinetic and safety prediction of six types of anthocyanins (ANC) as well as virtual molecular interaction between ANC and 5-hydroxytryptamine-2C (5HT-2C) receptor for antagonist target of antidepressant drug development. The Lipinski rule of five was used to predict the oral drug-likeness of ANC. The pharmacokinetic and safety prediction was analyzed with a free accessible web server. The ligands of ANC were retrieved from PubChem National Centre for Biotechnology Information (NCBI) database. The protein of the 5HT-2C receptor was obtained from Protein Data Bank. Molecular docking was performed by PyRx software and visualized using Discovery Studio Software. The results showed ANC is proposed as an oral drug candidate. The pharmacokinetic prediction of ANC was demonstrated to have high absorption in the intestinal route, solubility in the aqueous phase, capability to evade hepatic first-pass metabolism and high total clearance from the kidney. Virtual toxicity prediction showed a higher threshold of chronic lethal dose than control with no toxicity on the salmonella typhimurium reverses mutation assay (AMES) test, liver, and skin. Molecular prediction found ANC type of delphinidin has the most similar interaction site with the control antagonist ligand on the 5HT-2C receptor which is facilitated with hydrogen bonds and hydrophobic bonds at amino residues of Trp324, Phe328, Ala222 and Val135. We concluded ANC particularly delphinidin is proposed as an oral drug candidate potentially used as a 5HT-2C receptor antagonist and thus, further in vitro and in vivo studies are necessary to confirm the effect on antidepressant activity.

Implementation of Pddikti Neo Feeder Web Service in Recording of Independent Campus Activities

Independent Learning-Independent Campus Program (MBKM) is a policy of granting the right for students to be able to take study activities outside the study program as many as three semesters with the division of two semesters of study outside the college and one semester in different study programs in one college. As well as teaching and learning activities, universities must report Independent Learning-Independent Campus activities to DIKTI every semester through the Neo Feeder PDDIKTI application. The Neo Feeder PDDIKTI application has a feature to enter the activities the operator will carry out. The operator enters this data individually on the Neo Feeder PDDIKTI application. This is a significant problem because the data entry process will take quite a lot of time, even though PDDIKTI has provided web service access to universities to optimize the data reporting process using the Neo Feeder PDDIKTI application. Building an application that can be used for recording MBKM activities by utilizing web services provided by PDDIKTI is the primary purpose of this study. The development of an application certainly requires a method as a framework or guide in facilitating the manufacturing process. The extreme Programming (XP) method becomes essential for applications with variable or non-fixed needs. This method has four working elements: planning, designing, coding, testing and software increment. The output generated by this study is an application for recording MBKM activities that use web service restful API technology so that data entry can be done en masse and not one by one.

IProm-Sigma54: A CNN Base Prediction Tool for σ54 Promoters.

The sigma (σ) factor of RNA holoenzymes is essential for identifying and binding to promoter regions during gene transcription in prokaryotes. σ54 promoters carried out various ancillary methods and environmentally responsive procedures; therefore, it is crucial to accurately identify σ54 promoter sequences to comprehend the underlying process of gene regulation. Herein, we come up with a convolutional neural network (CNN) based prediction tool named "iProm-Sigma54" for the prediction of σ54 promoters. The CNN consists of two one-dimensional convolutional layers, which are followed by max pooling layers and dropout layers. A one-hot encoding scheme was used to extract the input matrix. To determine the prediction performance of iProm-Sigma54, we employed four assessment metrics and five-fold cross-validation; performance was measured using a benchmark and test dataset. According to the findings of this comparison, iProm-Sigma54 outperformed existing methodologies for identifying σ54 promoters. Additionally, a publicly accessible web server was constructed.

DL-m6A: Identification of N6-Methyladenosine Sites in Mammals Using Deep Learning Based on Different Encoding Schemes.

N6-methyladenosine (m6A) is a common post-transcriptional alteration that plays a critical function in a variety of biological processes. Although experimental approaches for identifying m6A sites have been developed and deployed, they are currently expensive for transcriptome-wide m6A identification. Some computational strategies for identifying m6A sites have been presented as an effective complement to the experimental procedure. However, their performance still requires improvement. In this study, we have proposed a novel tool called DL-m6A for the identification of m6A sites in mammals using deep learning based on different encoding schemes. The proposed tool uses three encoding schemes which give the required contextual feature representation to the input RNA sequence. Later these contextual feature vectors individually go through several neural network layers for shallow feature extraction after which they are concatenated to a single feature vector. The concatenated feature map is then used by several other layers to extract the deep features so that the insight features of the sequence can be used for the prediction of m6A sites. The proposed tool is firstly evaluated on the tissue-specific dataset and later on a full transcript dataset. To ensure the generalizability of the tool we assessed the proposed model by training it on a full transcript dataset and test on the tissue-specific dataset. The achieved results by the proposed model have outperformed the existing tools. The results demonstrate that the proposed tool can be of great use for the biology experts and therefore a freely accessible web-server is created which can be accessed at: http://nsclbio.jbnu.ac.kr/tools/DL-m6A/.

A cost-effective machine learning-based method for preeclampsia risk assessment and driver genes discovery

BackgroundThe placenta, as a unique exchange organ between mother and fetus, is essential for successful human pregnancy and fetal health. Preeclampsia (PE) caused by placental dysfunction contributes to both maternal and infant morbidity and mortality. Accurate identification of PE patients plays a vital role in the formulation of treatment plans. However, the traditional clinical methods of PE have a high misdiagnosis rate.ResultsHere, we first designed a computational biology method that used single-cell transcriptome (scRNA-seq) of healthy pregnancy (38 wk) and early-onset PE (28–32 wk) to identify pathological cell subpopulations and predict PE risk. Based on machine learning methods and feature selection techniques, we observed that the Tuning ReliefF (TURF) score hybrid with XGBoost (TURF_XGB) achieved optimal performance, with 92.61% accuracy and 92.46% recall for classifying nine cell subpopulations of healthy placentas. Biological landscapes of placenta heterogeneity could be mapped by the 110 marker genes screened by TURF_XGB, which revealed the superiority of the TURF feature mining. Moreover, we processed the PE dataset with LASSO to obtain 497 biomarkers. Integration analysis of the above two gene sets revealed that dendritic cells were closely associated with early-onset PE, and C1QB and C1QC might drive preeclampsia by mediating inflammation. In addition, an ensemble model-based risk stratification card was developed to classify preeclampsia patients, and its area under the receiver operating characteristic curve (AUC) could reach 0.99. For broader accessibility, we designed an accessible online web server (http://bioinfor.imu.edu.cn/placenta).ConclusionSingle-cell transcriptome-based preeclampsia risk assessment using an ensemble machine learning framework is a valuable asset for clinical decision-making. C1QB and C1QC may be involved in the development and progression of early-onset PE by affecting the complement and coagulation cascades pathway that mediate inflammation, which has important implications for better understanding the pathogenesis of PE.

UMN Sims: Development of Student Life Simulation Game to Introduce Onsite Campus Activities using React.js

After two years of the implementation of the online learning mechanism due to the Covid-19 pandemic, Universitas Multimedia Nusantara, a private university in Tangerang, Indonesia, has again implemented an onsite learning mechanism. Based on the initial survey conducted, it was found that many students were not aware of various academic and non-academic activities that could be carried out on campus and its surroundings. Therefore, in this paper, we try to make a simulation game to introduce students to various activities that can be done on campus and its surroundings. Simulation games are developed mainly using React.js with the Axioos library to perform HTTP requests to access web services. We evaluated this game using the Hedonic-Motivation System Adoption Model or HMSAM to determine the level of player satisfaction. We conducted experiments on high school students, freshmen and sophomores and found that high school students' HMSAM scores were higher, assuming high school students had a curiosity about life as university students. Behavioral intention to use and immersion obtained using HMSAM were 80.25% and 80.31%, respectively, this means that these games strongly agree that respondents are interested in playing UMNSims and playing it immersively. Keywords: HMSAM, ReactJS, Simulation Game, university, web-based