User-friendly Web Application Research Articles

A Deep Learning-Based Decision Support Tool for Plant-Parasitic Nematode Management.

Plant-parasitic nematodes (PPN), especially sedentary endoparasitic nematodes like root-knot nematodes (RKN), pose a significant threat to major crops and vegetables. They are responsible for causing substantial yield losses, leading to economic consequences, and impacting the global food supply. The identification of PPNs and the assessment of their population is a tedious and time-consuming task. This study developed a state-of-the-art deep learning model-based decision support tool to detect and estimate the nematode population. The decision support tool is integrated with the fast inferencing YOLOv5 model and used pretrained nematode weight to detect plant-parasitic nematodes (juveniles) and eggs. The performance of the YOLOv5-640 model at detecting RKN eggs was as follows: precision = 0.992; recall = 0.959; F1-score = 0.975; and mAP = 0.979. YOLOv5-640 was able to detect RKN eggs with an inference time of 3.9 milliseconds, which is faster compared to other detection methods. The deep learning framework was integrated into a user-friendly web application system to build a fast and reliable prototype nematode decision support tool (NemDST). The NemDST facilitates farmers/growers to input image data, assess the nematode population, track the population growths, and recommend immediate actions necessary to control nematode infestation. This tool has the potential for rapid assessment of the nematode population to minimise crop yield losses and enhance financial outcomes.

Development of a Stacked Ensemble Model for Risk Stratification of Chronic Gvhd after Allogeneic HSCT: Trump Cohort Study

Background Effective risk stratification is vital for selecting donors and developing treatment strategies for allogeneic hematopoietic stem cell transplantation (allo-HSCT). Several pretransplant risk models have been proposed to predict post-transplant patient outcomes from pretransplant risk factors. However, it is still challenging to predict mid-to-long-term prognosis. To establish a novel and user-friendly tool for predicting chronic graft-versus-host disease (cGVHD), non-relapse mortality (NRM) and overall survival (OS), we used a Stacked Ensemble Model (SEM) designed to handle right-censored data and competing risk events (Iwasaki et al., 2021). Methods Using Japanese registry data, the Japanese Transplant Registry Unified Management Program, we analyzed 21,840 patients who underwent their first allo-HSCT between 2010 and 2018 for hematologic malignancies. Of these, 14,430 patients were alive without cGVHD and relapse on day 100 post-transplantation. Primary endpoint was cGVHD and secondary endpoints were NRM and OS. Data were randomly split into the training set (70% of the dataset) and the validation set (30%). We treated patient age and time from diagnosis to transplant as continuous variables and performance status, ABO type matching, sex matching, disease/status groups based on refined Disease Risk Index, Hematopoietic Cell Transplantation-specific Comorbidity Index, usage of total body irradiation, conditioning intensity, T cell depletion in vivo, human leukocyte antigen (HLA) locus matching, the number of HLA mismatching and CMV serostatus as categorical variables. To evaluate predictive accuracy at day 30, 60 and 100 posttransplant, we considered acute graft-versus-host disease (aGVHD) and its treatment, including grade 2 to 4 aGVHD occurrence, grade 3 to 4 aGVHD occurrence, stage of aGVHD before and after treatment, corticosteroid treatment, anti-thymocyte globulin treatment and treatment response of 1 st line therapy for aGVHD. Results The median age of the patients at transplantation was 51 years (inter quartile range (IQR): 39 - 60). The sources of stem cells were HLA-matched related bone marrow (BM) in 967 patients, HLA-matched related peripheral blood (PB) in 1,998 patients, haploidentical BM in 172 patients, haploidentical PB in 778 patients, unrelated BM in 5,539 patients, unrelated PB in 366 patients, and cord blood in 4,237 patients. Myeloablative conditioning was administered in 9,390 patients and reduced-intensity conditioning in 5,040 patients. The most common indication of HSCT was intermediate-risk acute myeloid leukemia (AML) in complete remission (CR, n=3,233) followed by acute lymphoblastic leukemia in first CR (n=2,164) and intermediate-risk AML in non-CR (n=1,618). Median follow-up period of survivors was 2.2 years (IQR: 0.9-4.5). The overall performance of SEM was superior across the endpoints evaluated, cGVHD, NRM and OS, while XGBoost showed a slightly higher C-index for cGVHD, demonstrating its robustness and reliability (Table 1, the C-index for cGVHD/NRM/OS-SEM: 0.567/0.635/0.607, Cox-PH: 0.550/0.624/0.602, Random Survival Forest: 0.555/0.632/0.597, XGBoost: 0.573/0.624/0.604, and Dynamic Deephit: 0.549/0.614/0.589). C-index of SEM for cGVHD, NRM, and OS were gradually improved as time goes by (Table 1, the C-index for cGVHD/NRM/OS: at day 30-0.589/0.643/0.635, at day 60-0.598/0.655/0.640, at day 100-0.598/0.688/0.640). Kaplan-Meier curves 100-day post-transplantation confirmed the accuracy of our predictions, effectively stratifying patients into four risk groups (Figure 1). SHapley Additive Explanations showed contribution of variables for predicting specific outcomes. Grade 3 to 4 aGVHD and stage of aGVHD after treatment influenced non-relapse mortality. Particularly, grade 3 to 4 aGVHD showed significant impact on OS in line with pretransplant risk factors including sex matching, donor source and disease/status groups. Grade 2 to 4 aGVHD and treatment response of 1 st line therapy for aGVHD influenced cGVHD. Conclusion Our study emphasizes the importance of aGVHD events in mid-to-long-term post-transplantation outcomes and presents SEM-based prediction models that account for early-phase post-transplantation events. We are currently developing a user-friendly web application tool for cGVHD, aiming to enhance post-transplant risk stratification and preemptive therapy for cGVHD.

VoteBlock: Blockchain Based E-Voting System

In the contemporary digital landscape, where the ease of hacking and system breaches prevails, the potential for data tampering looms, resulting in unfavourable circumstances. To mitigate this rick, blockchain technology emerges as steadfast solution, characterised by its near- impenetrable security features, rendering data alteration virally impossible. The act of voting, integral to any nation’s democratic process, stands as a cornerstone of societal decision making. The consequences of vote miscalculation or manipulation by external forces are dire, emphasising the need for a secure and reliable system. It is in this context that blockchain technology plays a cubical role, offering an avenue to enhance the integrity and accessibility of the voting process. This paper presents a comprehensive proposal for decentralised national e-voting system build upon blockchain technology. The system incorporates an administrative panel designed to facilitate the scheduling of elections, candidate management, and the declaration of election results. Concurrently, a user-friendly web application empowers citizens with an interface for inputting their personal details, including their name, mobile number, and a live photo captured during the voting process. To maintain the integrity of the voting system, voter eligibility is rigorously verified at the point of User ID entry. A crucial layer of security is added by confirming eligible voters through their registers phone numbers using One Time Passwords (OTPs). Subsequently, individual voters gain the privilege to cast their votes, with their actions monitored through a webcam or front cameras. Votes are securely recorded within a blockchain ledger, immune to tapering, while the system checks the voter’s addressed corresponding constituency in the backend. The voting results are diligently declared on a predefined date and managed by the administrator, with graphical representations available to provide clarity and transparency. This holistic system not only upholds the sanctity of the voting process but also offers citizens a technologically advanced and secure means to exercise their democratic rights

Design of prime-editing guide RNAs with deep transfer learning

Prime editors (PEs) are promising genome-editing tools, but effective optimization of prime-editing guide RNA (pegRNA) design remains a challenge owing to the lack of accurate and broadly applicable approaches. Here we develop Optimized Prime Editing Design (OPED), an interpretable nucleotide language model that leverages transfer learning to improve its accuracy and generalizability for the efficiency prediction and design optimization of pegRNAs. Comprehensive validations on various published datasets demonstrate its broad applicability in efficiency prediction across diverse scenarios. Notably, pegRNAs with high OPED scores consistently show significantly increased editing efficiencies. Furthermore, the versatility and efficacy of OPED in design optimization are confirmed by efficiently installing various ClinVar pathogenic variants using optimized pegRNAs in the PE2, PE3/PE3b and ePE editing systems. OPED consistently outperforms existing state-of-the-art approaches. We construct the OPEDVar database of optimized designs from over two billion candidates for all pathogenic variants and provide a user-friendly web application of OPED for any desired edit.

Prognosis Individualized: Survival predictions for WHO grade II and III gliomas with a machine learning-based web application

WHO grade II and III gliomas demonstrate diverse biological behaviors resulting in variable survival outcomes. In the context of glioma prognosis, machine learning (ML) approaches could facilitate the navigation through the maze of factors influencing survival, aiding clinicians in generating more precise and personalized survival predictions. Here we report the utilization of ML models in predicting survival at 12, 24, 36, and 60 months following grade II and III glioma diagnosis. From the National Cancer Database, we analyze 10,001 WHO grade II and 11,456 grade III cranial gliomas. Using the area under the receiver operating characteristic (AUROC) values, we deploy the top-performing models in a web application for individualized predictions. SHapley Additive exPlanations (SHAP) enhance the interpretability of the models. Top-performing predictive models are the ones built with LightGBM and Random Forest algorithms. For grade II gliomas, the models yield AUROC values ranging from 0.813 to 0.896 for predicting mortality across different timeframes, and for grade III gliomas, the models yield AUROCs ranging from 0.855 to 0.878. ML models provide individualized survival forecasts for grade II and III glioma patients across multiple clinically relevant time points. The user-friendly web application represents a pioneering digital tool to potentially integrate predictive analytics into neuro-oncology clinical practice, to empower prognostication and personalize clinical decision-making.

Web-based Application for Cancerous Object Segmentation in Ultrasound Images Using Active Contour Method

Segmentation, or the process of separating clinical objects from surrounding tissue in medical images, is an important step in the Computer-Aided Diagnosis (CAD) system. The CAD system is developed to assist radiologists in diagnosing cancer malignancy, which in this research is found in ultrasound (US) medical imaging. The manual segmentation process, which cannot be accessed remotely, is a limitation of the CAD system because cancer objects are screened frequently, continuously, and at all times. Therefore, this research aims to build a user-friendly web application called COSION (Cancerous Object Segmentation) that provides easy access for radiologists to segment cancer objects in US images by adopting an active contour method called HERBAC (Hybrid Edge & Region-Based Active Contour). The waterfall method was used to develop the web application with Django as the web framework. The successfully built web application is named Cosion. Cosion was tested on 114 radiology breast and thyroid US images. Functional, portability, efficiency, reliability, expert validation, and usability testing concluded that Cosion runs well and is suitable for use with a functionality value of 0.9375, an average GTmetrix score of 96.43±0.66%, 100% stress testing percentage, 77.5% expert validation, and 75.8% usability. These quantitative performances indicate that the COSION web application is suitable for implementation in the CAD system for US medical imaging.

Semi-automated assessment of the risk of bias due to missing evidence in network meta-analysis: a guidance paper for the ROB-MEN web-application

Network meta-analysis compares multiple interventions and estimates the relative treatment effects between all interventions, combining both direct and indirect evidence. Recently, a framework was developed to assess the Risk Of Bias due to Missing Evidence in Network meta-analysis (ROB-MEN) which is part of the more comprehensive framework to evaluate the Confidence In the evidence for Network Meta-Analysis (CINeMA). To produce an overall risk of bias judgement for each network estimate, ROB-MEN: performs an assessment of the bias due to missing evidence in each possible pairwise comparison; combines the assessment with the contribution from the direct pairwise comparisons; considers the potential for small-study effects. To facilitate and semi-automate this process, ROB-MEN has been implemented in a user-friendly web-application (https://cinema.ispm.unibe.ch/rob-men). Here we provide a tutorial detailing the functionality and use of the application consisting of data upload, analysis configuration, output visualisation, and production of the tool’s output tables for recording the risk of bias assessment. We also illustrate an example application using the demo dataset available for download on the application’s homepage. The ROB-MEN web-application is open-source and freely available (https://github.com/esm-ispm-unibe-ch/rob-men).

KidneyGPS: a user-friendly web application to help prioritize kidney function genes and variants based on evidence from genome-wide association studies

BackgroundGenome-wide association studies (GWAS) have identified hundreds of genetic loci associated with kidney function. By combining these findings with post-GWAS information (e.g., statistical fine-mapping to identify independent association signals and to narrow down signals to causal variants; or different sources of annotation data), new hypotheses regarding physiology and disease aetiology can be obtained. These hypotheses need to be tested in laboratory experiments, for example, to identify new therapeutic targets. For this purpose, the evidence obtained from GWAS and post-GWAS analyses must be processed and presented in a way that they are easily accessible to kidney researchers without specific GWAS expertise.MainHere we present KidneyGPS, a user-friendly web-application that combines genetic variant association for estimated glomerular filtration rate (eGFR) from the Chronic Kidney Disease Genetics consortium with annotation of (i) genetic variants with functional or regulatory effects (“SNP-to-gene” mapping), (ii) genes with kidney phenotypes in mice or human (“gene-to-phenotype”), and (iii) drugability of genes (to support re-purposing). KidneyGPS adopts a comprehensive approach summarizing evidence for all 5906 genes in the 424 GWAS loci for eGFR identified previously and the 35,885 variants in the 99% credible sets of 594 independent signals. KidneyGPS enables user-friendly access to the abundance of information by search functions for genes, variants, and regions. KidneyGPS also provides a function (“GPS tab”) to generate lists of genes with specific characteristics thus enabling customizable Gene Prioritisation (GPS). These specific characteristics can be as broad as any gene in the 424 loci with a known kidney phenotype in mice or human; or they can be highly focussed on genes mapping to genetic variants or signals with particularly with high statistical support. KidneyGPS is implemented with RShiny in a modularized fashion to facilitate update of input data (https://kidneygps.ur.de/gps/).ConclusionWith the focus on kidney function related evidence, KidneyGPS fills a gap between large general platforms for accessing GWAS and post-GWAS results and the specific needs of the kidney research community. This makes KidneyGPS an important platform for kidney researchers to help translate in silico research results into in vitro or in vivo research.

An R Shiny Application (SDOH) for Predictive Modeling Using Regional Social Determinants of Health Survey Responses.

Social determinants of health (SDoH) surveys are data sets that provide useful health-related information about individuals and communities. This study aims to develop a user-friendly web application that allows clinicians to get a predictive insight into the social needs of their patients before their in-patient visits using SDoH survey data to provide an improved and personalized service. The study used a longitudinal survey that consisted of 108,563 patient responses to 12 questions. Questions were designed to have a binary outcome as the response and the patient's most recent responses for each of these questions were modeled independently by incorporating explanatory variables. Multiple classification and regression techniques were used, including logistic regression, Bayesian generalized linear model, extreme gradient boosting, gradient boosting, neural networks, and random forests. Based on the area under the curve values, gradient boosting models provided the highest precision values. Finally, the models were incorporated into an R Shiny application, enabling users to predict and compare the impact of SDoH on patients' lives. The tool is freely hosted online by the University of Kansas Medical Center's Department of Biostatistics and Data Science. The supporting materials for the application are publicly accessible on GitHub.

The Automated Systematic Search Deduplicator (ASySD): a rapid, open-source, interoperable tool to remove duplicate citations in biomedical systematic reviews

BackgroundResearchers performing high-quality systematic reviews search across multiple databases to identify relevant evidence. However, the same publication is often retrieved from several databases. Identifying and removing such duplicates (“deduplication”) can be extremely time-consuming, but failure to remove these citations can lead to the wrongful inclusion of duplicate data. Many existing tools are not sensitive enough, lack interoperability with other tools, are not freely accessible, or are difficult to use without programming knowledge. Here, we report the performance of our Automated Systematic Search Deduplicator (ASySD), a novel tool to perform automated deduplication of systematic searches for biomedical reviews.MethodsWe evaluated ASySD’s performance on 5 unseen biomedical systematic search datasets of various sizes (1845–79,880 citations). We compared the performance of ASySD with EndNote’s automated deduplication option and with the Systematic Review Assistant Deduplication Module (SRA-DM).ResultsASySD identified more duplicates than either SRA-DM or EndNote, with a sensitivity in different datasets of 0.95 to 0.99. The false-positive rate was comparable to human performance, with a specificity of > 0.99. The tool took less than 1 h to identify and remove duplicates within each dataset.ConclusionsFor duplicate removal in biomedical systematic reviews, ASySD is a highly sensitive, reliable, and time-saving tool. It is open source and freely available online as both an R package and a user-friendly web application.

E-WASTE MANAGEMENT USING MACHINE LEARNING

-This study introduces an intelligent e- waste management system, leveraging Convolutional Neural Networks (CNNs) integrated into a user-friendly web application. The system employs CNNs for precise image recognition and classification of e-waste items, including organic, metal, glass, clothes, paper, and electronic materials. Users can effortlessly submit e-waste information through the web interface, receiving tailored recommendations for eco-friendly disposal methods. This technology-driven approach aims to revolutionize e-waste management, providing an efficient and sustainable solution for the burgeoning e-waste crisis. Key Words: E-Waste, CNN (Convolutional Neural Network), Image Recognition, Sustainable Disposal and waste management

DiseaseGPS: auxiliary diagnostic system for genetic disorders based on genotype and phenotype.

The next-generation sequencing brought opportunities for the diagnosis of genetic disorders due to its high-throughput capabilities. However, the majority of existing methods were limited to only sequencing candidate variants, and the process of linking these variants to a diagnosis of genetic disorders still required medical professionals to consult databases. Therefore, we introduce diseaseGPS, an integrated platform for the diagnosis of genetic disorders that combines both phenotype and genotype data for analysis. It offers not only a user-friendly GUI web application for those without a programming background but also scripts that can be executed in batch mode for bioinformatics professionals. The genetic and phenotypic data are integrated using the ACMG-Bayes method and a novel phenotypic similarity method, to prioritize the results of genetic disorders. diseaseGPS was evaluated on 6085 cases from Deciphering Developmental Disorders project and 187 cases from Shanghai Children's hospital. The results demonstrated that diseaseGPS performed better than other commonly used methods. diseaseGPS is available to freely accessed at https://diseasegps.sjtu.edu.cn with source code at https://github.com/BioHuangDY/diseaseGPS.

EasySSR: a user-friendly web application with full command-line features for large-scale batch microsatellite mining and samples comparison.

Microsatellites, also known as SSRs or STRs, are polymorphic DNA regions with tandem repetitions of a nucleotide motif of size 1-6 base pairs with a broad range of applications in many fields, such as comparative genomics, molecular biology, and forensics. However, the majority of researchers do not have computational training and struggle while running command-line tools or very limited web tools for their SSR research, spending a considerable amount of time learning how to execute the software and conducting the post-processing data tabulation in other tools or manually-time that could be used directly in data analysis. We present EasySSR, a user-friendly web tool with command-line full functionality, designed for practical use in batch identifying and comparing SSRs in sequences, draft, or complete genomes, not requiring previous bioinformatic skills to run. EasySSR requires only a FASTA and an optional GENBANK file of one or more genomes to identify and compare STRs. The tool can automatically analyze and compare SSRs in whole genomes, convert GenBank to PTT files, identify perfect and imperfect SSRs and coding and non-coding regions, compare their frequencies, abundancy, motifs, flanking sequences, and iterations, producing many outputs ready for download such as PTT files, interactive charts, and Excel tables, giving the user the data ready for further analysis in minutes. EasySSR was implemented as a web application, which can be executed from any browser and is available for free at https://computationalbiology.ufpa.br/easyssr/. Tutorials, usage notes, and download links to the source code can be found at https://github.com/engbiopct/EasySSR.

Precision medicine for traumatic cervical spinal cord injuries: accessible and interpretable machine learning models to predict individualized in-hospital outcomes

BACKGROUND CONTEXTA traumatic spinal cord injury (SCI) can cause temporary or permanent motor and sensory impairment, leading to serious short and long-term consequences that can result in significant morbidity and mortality. The cervical spine is the most commonly affected area, accounting for about 60% of all traumatic SCI cases. PURPOSEThis study aims to employ machine learning (ML) algorithms to predict various outcomes, such as in-hospital mortality, nonhome discharges, extended length of stay (LOS), extended length of intensive care unit stay (ICU-LOS), and major complications in patients diagnosed with cervical SCI (cSCI). STUDY DESIGNOur study was a retrospective machine learning classification study aiming to predict the outcomes of interest, which were binary categorical variables, in patients diagnosed with cSCI. PATIENT SAMPLEThe data for this study were obtained from the American College of Surgeons (ACS) Trauma Quality Program (TQP) database, which was queried to identify patients who suffered from cSCI between 2019 and 2021. OUTCOME MEASURESThe outcomes of interest of our study were in-hospital mortality, nonhome discharges, prolonged LOS, prolonged ICU-LOS, and major complications. The study evaluated the models' performance using both graphical and numerical methods. The receiver operating characteristic (ROC) and precision-recall curves (PRC) were used to assess model performance graphically. Numerical evaluation metrics included AUROC, balanced accuracy, weighted area under PRC (AUPRC), weighted precision, and weighted recall. METHODSThe study employed data from the American College of Surgeons (ACS) Trauma Quality Program (TQP) database to identify patients with cSCI. Four ML algorithms, namely XGBoost, LightGBM, CatBoost, and Random Forest, were utilized to develop predictive models. The most effective models were then incorporated into a publicly available web application designed to forecast the outcomes of interest. RESULTSThere were 71,661 patients included in the analysis for the outcome mortality, 67,331 for the outcome nonhome discharges, 76,782 for the outcome prolonged LOS, 26,615 for the outcome prolonged ICU-LOS, and 72,132 for the outcome major complications. The algorithms exhibited an AUROC value range of 0.78 to 0.839 for in-hospital mortality, 0.806 to 0.815 for nonhome discharges, 0.679 to 0.742 for prolonged LOS, 0.666 to 0.682 for prolonged ICU-LOS, and 0.637 to 0.704 for major complications. An open access web application was developed as part of the study, which can generate predictions for individual patients based on their characteristics. CONCLUSIONSOur study suggests that ML models can be valuable in assessing risk for patients with cervical cSCI and may have considerable potential for predicting outcomes during hospitalization. ML models demonstrated good predictive ability for in-hospital mortality and nonhome discharges, fair predictive ability for prolonged LOS, but poor predictive ability for prolonged ICU-LOS and major complications. Along with these promising results, the development of a user-friendly web application that facilitates the integration of these models into clinical practice is a significant contribution of this study. The product of this study may have significant implications in clinical settings to personalize care, anticipate outcomes, facilitate shared decision making and informed consent processes for cSCI patients.

WebNORM: a web application for calculating normative mineralogy

Normative mineralogy is a valuable tool for interpreting the mineralogical composition of rocks based on their bulk rock geochemical data. Despite the development of various computational tools for normative mineralogy calculations, the accessibility and continued usage of many existing tools is limited by the use of older languages, licensing constraints, and restricted access to the source code. In this paper, we introduce a Python-based implementation of the normative mineralogy algorithm, which is accessible through a user-friendly web application webNORM, or as part of the pyrolite Python package. The algorithm uses major, minor, and trace element oxides as input and returns up to 31 normative minerals. It offers two methods for estimating the Fe2O3:FeO ratio. The web application offers a convenient interface for users without programming experience, while the pyrolite integration allows more advanced users to leverage the Python ecosystem for end-to-end geochemical data analysis. Comparison between our implementation of the normative calculation and previous implementations shows that our version is accurate and reliable. webNORM offers the geoscience community an accessible solution for calculating normative mineralogy. The algorithm and web application are open-source, and we encourage contributions and adaptations by the community for specific needs and improvements.

MAGI-ACMG: Algorithm for the Classification of Variants According to ACMG and ACGS Recommendations.

We have developed MAGI-ACMG, a classification algorithm that allows the classification of sequencing variants (single nucleotide or small indels) according to the recommendations of the American College of Medical Genetics (ACMG) and the Association for Clinical Genomic Science (ACGS). The MAGI-ACMG classification algorithm uses information retrieved through the VarSome Application Programming Interface (API), integrates the AutoPVS1 tool in order to evaluate more precisely the attribution of the PVS1 criterion, and performs the customized assignment of specific criteria. In addition, we propose a sub-classification scheme for variants of uncertain significance (VUS) according to their proximity either towards the "likely pathogenic" or "likely benign" classes. We also conceived a pathogenicity potential criterion (P_POT) as a proxy for segregation criteria that might be added to a VUS after posterior testing, thus allowing it to upgrade its clinical significance in a diagnostic reporting setting. Finally, we have developed a user-friendly web application based on the MAGI-ACMG algorithm, available to geneticists for variant interpretation.

Visualizing Google Sheets Data in Web Applications with Laravel

The study presents the design implementation of visualizing Google Sheets data in web applications using the Rapid Application Development (RAD) approach. It focuses on integrating Google Sheets data seamlessly into a web application using the Laravel PHP framework and employs effective data visualization techniques. Real-time data access is achieved through the integration of Google Sheets API, while the Google Maps API and High charts Library enabled interactive and informative data visualizations. The result is a user-friendly web application, empowering users to explore data insights and make well-informed decisions with ease

Prognosis at Your Fingertips: A Machine Learning-Based Web Application for Outcome Prediction in Acute Traumatic Epidural Hematoma.

Traumatic brain injury (TBI) affects 69 million people worldwide each year, and acute traumatic epidural hematoma (atEDH) is a frequent and severe consequence of TBI. The aim of the study is to use machine learning (ML) algorithms to predict in-hospital death, non-home discharges, prolonged length of stay (LOS), prolonged length of intensive care unit stay (ICU-LOS), and major complications in patients with atEDH and incorporate the resulting ML models into a user-friendly web application for use in the clinical settings. The American College of Surgeons (ACS) Trauma Quality Program (TQP) database was used to identify patients with atEDH. Four ML algorithms (XGBoost, LightGBM, CatBoost, and Random Forest) were utilized, and the best performing models were incorporated into an open-access web application to predict the outcomes of interest. The study found that the ML algorithms had high area under the receiver operating characteristic curve (AUROC) values in predicting outcomes for patients with atEDH. In particular, the algorithms had an AUROC value range of between 0.874 to 0.956 for in-hospital mortality, 0.776 to 0.798 for non-home discharges, 0.737 to 0.758 for prolonged LOS, 0.712 to 0.774 for prolonged ICU-LOS, and 0.674 to 0.733 for major complications. The following link will take users to the open-access web application designed to generate predictions for individual patients based on their characteristics: huggingface.co/spaces/MSHS-Neurosurgery-Research/TQP-atEDH. This study aimed to improve the prognostication of patients with atEDH using ML algorithms and developed a web application for easy integration in clinical practice. It found that ML algorithms can aid in risk stratification and have significant potential for predicting in-hospital outcomes. Results demonstrated excellent performance for predicting in-hospital death and fair performance for non-home discharges, prolonged LOS and ICU-LOS, and poor performance for major complications.

EZYDeep: A Deep Learning Tool for Enzyme Function Prediction based on Sequence Information

Introduction: Enzymes play a crucial role in numerous chemical processes that are essential for life. Accurate prediction and classification of enzymes are crucial for bioindustrial and biomedical applications. Methods: In this study, we present EZYDeep, a deep learning tool based on convolutional neural networks, for classifying enzymes based on their sequence information. The tool was evaluated against two existing methods, HECNet and DEEPre, on the HECNet July 2019 dataset, and showed exceptional performance with accuracy rates over 95% at all four levels of prediction. Results: Additionally, our tool was compared to state-of-the-art enzyme function prediction tools and demonstrated superior performance at all levels of prediction. We also developed a user-friendly web application for the tool, making it easily accessible to researchers and practitioners. Conclusion: Our work demonstrates the potential of using machine learning techniques for accurate and efficient enzyme classification, highlighting the significance of sequence information in predicting enzyme function.

Stock Market Price Prediction Using Machine Learning and Deep Learning

This project aims to address the long-standing challenge of stock market prediction by leveraging the power of machine learning. The stock market is influenced by a multitude of factors, making it difficult for investors to accurately predict trends and make informed decisions. To mitigate this risk, we have chosen to analyse NSE real-time series data using the ARIMA model, a popular forecasting method. Our ultimate goal is to develop a user-friendly web application using Flask, which will allow investors to select a specific firm and receive a forecast for its future stock price. This application will utilize web scraping techniques to retrieve real-time data from the NSE, and the ARIMA model will be applied to predict future outcomes. By combining these powerful tools, we hope to significantly reduce the potential danger associated with stock market prediction and enable investors to make more informed decisions.