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The recent advancements in machine learning and the new availability of large chemical datasets made the development of tools and protocols for computational chemistry a topic of high interest. In this chapter a standard procedure to develop Quantitative Structure-Activity Relationship (QSAR) models was presented and implemented in two freely available and easy-to-use workflows. The first workflow helps the user retrieving chemical data (SMILES) from the web, checking their correctness and curating them to produce consistent and ready-to-use datasets for cheminformatic. The second workflow implements six machine learning methods to develop classification QSAR models. Models can be additionally used to predict external chemicals. Calculation and selection of chemical descriptors, tuning of models' hyperparameters, and methods to handle data unbalancing are also incorporated in the workflow. Both the workflows are implemented in KNIME and represent a useful tool for computational scientists, as well as an intuitive and straightforward introduction to QSAR., (© 2025. The Author(s), under exclusive license to Springer Science+Business Media, LLC, part of Springer Nature.)

Automated Machine Learning (AutoML) enhances productivity and efficiency by automating the entire process of machine learning model development, from data preprocessing to model deployment. These tools are accessible to users with varying levels of expertise and enable efficient, scalable, and accurate classification across different applications. This paper evaluates two popular AutoML tools, the Tree-Based Pipeline Optimization Tool (TPOT) version 0.10.2 and Konstanz Information Miner (KNIME) version 5.2.5, comparing their performance in a classification task. Specifically, this work analyzes autism spectrum disorder (ASD) detection in toddlers as a use case. The dataset for ASD detection was collected from various rehabilitation centers in Pakistan. TPOT and KNIME were applied to the ASD dataset, with TPOT achieving an accuracy of 85.23% and KNIME achieving 83.89%. Evaluation metrics such as precision, recall, and F1-score validated the reliability of the models. After selecting the best models with optimal accuracy, the most important features for ASD detection were identified using these AutoML tools. The tools optimized the feature selection process and significantly reduced diagnosis time. This study demonstrates the potential of AutoML tools and feature selection techniques to improve early ASD detection and outcomes for affected children and their families. [ABSTRACT FROM AUTHOR]

The COVID-19 pandemic has caused unprecedented changes in the global economy and society, with many studies attempting to understand the impact of the virus on different countries and industries. This study focuses on the effects of COVID-19 on a consulting company that specializes in technology services. By analyzing the company's sales data for the five-year period before the pandemic, and using machine learning techniques via the KNIME platform, the study aims to predict the sales data for the COVID-19 period. Three different regression models - linear, gradient boosting, and random forest - were used to make these predictions, and the models were compared based on their coefficient of determination (R2) to determine which model performed best. The chosen model was then used to interpret the impact of COVID-19 on the company. The findings of the study provide insights into how COVID-19 has affected the consulting company. The chosen model showed that the pandemic had a significant negative impact on the company's sales, with a sharp decline in the second quarter of 2020. However, the company was able to recover some of its losses by the fourth quarter of the year. The study also highlights the importance of using machine learning techniques to predict future sales data during unpredictable events such as the COVID-19 pandemic. Overall, this study sheds light on the impact of COVID-19 on a technology consulting company and demonstrates the importance of using data analysis and machine learning techniques to make predictions and interpret the effects of significant events on business operations. [ABSTRACT FROM AUTHOR]

Inhibitors of DNA methyltransferase (DNMTs) are now a major family of epigenetic targets with therapeutic interest. However, only two cytosine analogues 5-azacytosine (azacytidine) and 20-deoxy-5-azacytidine (decitabine), have been approved as the most cutting-edge medications for treating epigenetic cancer with some restrictions. In this context, computational methods that rely on quantitative structure-activity relationship (QSAR) play a crucial role allowing us to predict the biological activity of potential molecules based on the theoretically calculated physicochemical properties of these compounds. When coupled with machine learning (ML), QSAR approaches create an ideal platform for discovering potential drug candidates. In this study, three Machine Learning (ML) models; Random Forest, Support Vector Machine, and Artificial Neural Network, were trained using modified TeachOpenCADD KNIME workflows and applied it to the identification of plant molecules that are structurally similar to the active pharmaceuticals of current DNMT inhibitors. Then molecular docking simulations were performed using AutoDock Vina, employing two human DNMT structures (PDB codes: 4WXX and 2QRV) as target proteins and the predicted phytochemicals as ligands. Additionally, we focused on the R882H mutation hotspot in the catalytic domain of DNMT3A, which is associated with aberrant DNA methylation in acute myeloid leukemia (AML). Consequently, the structure of R882H DNMT3A (PDB code: 6W8J) was docked with the identified novel ligands. As a result of our computational analysis, eight phytochemicals were predicted as potential DNMT inhibitors through the ML approaches from KNIME. Subsequently, three of these phytochemicals, namely Herbacetin, Kaempferide, and Morin were identified as virtual hits against DNMTs following the molecular docking simulations. Overall, our study demonstrates the effectiveness of this computational strategy in identifying DNMT inhibitors. These findings hold promise for the discovery of potent and selective anticancer drugs targeting DNMTs. [ABSTRACT FROM AUTHOR]

The rapid growth of the Internet of Things (IoT) and digital industrial devices has significantly impacted various aspects of life, underscoring the importance of the Industrial Internet of Things (IIoT). Given its importance in industrial contexts that affect human life, the IIoT represents a key subset of the broader IoT landscape. Due to the proliferation of sensors in smart devices, which are viewed as points of contact, as the gathering of data and information regarding the IIoT systems and devices operating on the IoT, there is an urgent requirement for developing effective security methods to counter such threats as well as protecting IIoT systems. In this study, we develop and evaluate a well-optimized intrusion detection system (IDS) based on deep learning (DL) and machine learning (ML) techniques to enhance IIoT security. Leveraging the Edge-IIoTset dataset, specifically designed for IIoT cybersecurity evaluations, we focus on detecting and mitigating 14 distinct attack types targeting IIoT and IoT protocols. These attacks are categorized into five threat groups: information collection, malware, DDoS, manin-the-middle attacks, and injection attacks. We conducted experiments using machine learning algorithms (knearest neighbors, decision tree) and a neural network on the KNIME platform, achieving a remarkable 100% accuracy with the decision tree model. This high accuracy demonstrates the effectiveness of our approach in protecting industrial IoT networks. [ABSTRACT FROM AUTHOR]

Vascular endothelial growth factors (VEGFs) are specific cytokines involved in angiogenesis and do so via binding to vascular endothelial growth factor receptors (VEGFRs), a type of receptor tyrosine kinase. VEGFs are reported to facilitate angiogenesis in physiological (embryogenesis) and pathological (tumor) conditions. The overexpression of VEGFs and consequently VEGFRs is reported in tumorigenic conditions. Several VEGFR inhibitors currently used as anticancer drugs to prevent angiogenesis are sunitinib, sorafenib, etc. To identify new potential candidates as VEGFR inhibitors, a classification study using a large and diverse dataset of VEGFR inhibitors from the BindingDB database has been conducted. The KNIME platform was used to calculate molecular and fingerprint‐based descriptors and several classification algorithms viz. linear regression (LR), k‐nearest neighbor (kNN), decision tree (DT), random forest (RF), and gradient boosted tree (GBT) were employed to build the classification model. The model performance was evaluated by accuracy, precision, recall, and F1 score of the test set. The best LR, kNN, DT, RF, and GBT classifiers had the F1 score of 0.81, 0.87, 0.82, 0.87, and 0.87, respectively. The assorted 5120 VEGFR inhibitors were clustered into 10 subsets, and the structural features of each subset were assessed along with the identification of significant fragments in active and inactive compounds. The automated classifier model developed using the KNIME platform could serve as an important platform for screening and designing molecules as VEGFR inhibitors. [ABSTRACT FROM AUTHOR]

Due to the rapid economic growth of the real estate sector, the prediction of housing prices is important for future planning. The aim of this study is to predict the potential price of a house using machine learning methods. As machine learning methods, artificial neural networks and polynomial regression were used and their prediction performances were compared. KNIME data analysis platform was used to apply machine learning methods. R Squared performance metric was used to measure the success of the methods. The application results show that the artificial neural network method predicts house prices with higher accuracy than the Polynomial regression method. It is thought that the study will contribute to the development of applications used for home evaluation and to scientific studies in this field. In future studies, it is aimed to expand the study by using different methods or data sets with home attributes. [ABSTRACT FROM AUTHOR]

The heterogeneity of the Caco-2 cell line and differences in experimental protocols for permeability assessment using this cell-based method have resulted in the high variability of Caco-2 permeability measurements. These problems have limited the generation of large datasets to develop accurate and applicable regression models. This study presents a QSPR approach developed on the KNIME analytical platform and based on a structurally diverse dataset of over 4900 molecules. Interpretable models were obtained using random forest supervised recursive algorithms for data cleaning and feature selection. The development of a conditional consensus model based on regional and global regression random forest produced models with RMSE values between 0.43–0.51 for all validation sets. The potential applicability of the model as a surrogate for the in vitro Caco-2 assay was demonstrated through blind prediction of 32 drugs recommended by the International Council for the Harmonization of Technical Requirements for Pharmaceuticals (ICH) for validation of in vitro permeability methods. The model was validated for the preliminary estimation of the BCS/BDDCS class. The KNIME workflow developed to automate new drug prediction is freely available. The results suggest that this automated prediction platform is a reliable tool for identifying the most promising compounds with high intestinal permeability during the early stages of drug discovery. [ABSTRACT FROM AUTHOR]

Objective: The aim of this study is to identify cancer earlier in life using machine learning methods. Methods: For this purpose, the Wisconsin Diagnostic Breast Cancer dataset was classified using Naive Bayes, decision trees, artificial neural networks algorithms and comparison of these machine learning methods was made. KNIME Analytics Platform was used for applications. Before the classification process, the dataset was preprocessed. After the preprocessing stage, three different classifier methods were applied to the dataset. Accuracy, sensitivity, specificity and confusion matrices were used to measure the success of the methods. Results: The results show that Naive Bayes and artificial neural network methods classify tumors with 96.5% accuracy. The success of the decision tree method in classification was 92.6%. Conclusions: The machine learning algorithms can be used successfully in breast cancer diagnosis to determine whether the tumors are malign or benign. [ABSTRACT FROM AUTHOR]

In our earlier study, we proposed a novel feature selection approach, Recursive Cluster Elimination with Support Vector Machines (SVM-RCE) and implemented this approach in Matlab. Interest in this approach has grown over time and several researchers have incorporated SVM-RCE into their studies, resulting in a substantial number of scientific publications. This increased interest encouraged us to reconsider how feature selection, particularly in biological datasets, can benefit from considering the relationships of those genes in the selection process, this led to our development of SVM-RCE-R. SVM-RCE-R, further enhances the capabilities of SVM-RCE by the addition of a novel user specified ranking function. This ranking function enables the user to stipulate the weights of the accuracy, sensitivity, specificity, f-measure, area under the curve and the precision in the ranking function This flexibility allows the user to select for greater sensitivity or greater specificity as needed for a specific project. The usefulness of SVM-RCE-R is further supported by development of the maTE tool which uses a similar approach to identify microRNA (miRNA) targets. We have also now implemented the SVM-RCE-R algorithm in Knime in order to make it easier to applyThe use of SVM-RCE-R in Knime is simple and intuitive and allows researchers to immediately begin their analysis without having to consult an information technology specialist. The input for the Knime implemented tool is an EXCEL file (or text or CSV) with a simple structure and the output is also an EXCEL file. The Knime version also incorporates new features not available in SVM-RCE. The results show that the inclusion of the ranking function has a significant impact on the performance of SVM-RCE-R. Some of the clusters that achieve high scores for a specified ranking can also have high scores in other metrics.

With the growth of data mining and machine learning approaches in recent years, many efforts have been made to generalize these sciences so that researchers from any field can easily utilize these sciences. One of the most important of these efforts is the development of data mining tools that try to hide the complexities from researchers so that they can achieve a professional output with any level of knowledge. This paper is focused on reviewing and comparing data mining and machine learning tools including WEKA, KNIME, Keel, Orange, Azure, IBM SPSS Modeler, R and Scikit-Learn to show what approach each of these methods has taken in the face of the complexities and problems of different scenarios of generalization of data mining and machine learning. In addition, for a more detailed review, this paper examines the challenge of network intrusion detection in two tools, Knime with graphical interface and Scikit-Learn with coding environment. [ABSTRACT FROM AUTHOR]

In our earlier study, we proposed a novel feature selection approach, Recursive Cluster Elimination with Support Vector Machines (SVM-RCE) and implemented this approach in Matlab. Interest in this approach has grown over time and several researchers have incorporated SVM-RCE into their studies, resulting in a substantial number of scientific publications. This increased interest encouraged us to reconsider how feature selection, particularly in biological datasets, can benefit from considering the relationships of those genes in the selection process, this led to our development of SVM-RCE-R. The usefulness of SVM-RCE-R is further supported by development of maTE tool, which uses a similar approach to identify microRNA (miRNA) targets. We have now implemented the SVM-RCE-R algorithm in Knime in order to make it easier to apply and to make it more accessible to the biomedical community. The use of SVM-RCE-R in Knime is simple and intuitive, allowing researchers to immediately begin their data analysis without having to consult an information technology specialist. The input for the Knime tool is an EXCEL file (or text or CSV) with a simple structure and the output is also an EXCEL file. The Knime version also incorporates new features not available in the previous version. One of these features is a user-specific ranking function that enables the user to provide the weights of the accuracy, sensitivity, specificity, f-measure, area under curve and precision in the ranking function, allowing the user to select for greater sensitivity or greater specificity as needed. The results show that the ranking function has an impact on the performance of SVM-RCE-R. Some of the clusters that achieve high scores for a specified ranking can also have high scores in other metrics. This finding motivates future studies to suggest the optimal ranking function.

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Social media networks are an attractive and hot research area in the big data community because of their numerous active users. One of the most widely studied topics in social networks is the prediction from the public available data. Recently, researchers have successfully predicted many statistical and human properties from social media networks using different machine learning algorithms. In this paper, a new efficient and accurate algorithm is proposed to predict the country location of a Twitter user using his or her public information only. The proposed algorithm employs the public information of the Twitter user and that of his or her followers and friends to predict his or her location without using GPS data. A convenient data set of Twitter users is gathered and used to test our proposed algorithm using KNIME software. The proposed algorithm is compared with other state-of-the-art algorithms, and results showed that our proposed algorithm significantly outperforms other location detection algorithms by using Twitter users from different countries. [ABSTRACT FROM AUTHOR]

Background: In-silico quantitative structure-activity relationship (QSAR) models based tools are widely used to screen huge databases of compounds in order to determine the biological properties of chemical molecules based on their chemical structure. With the passage of time, the exponentially growing amount of synthesized and known chemicals data demands computationally efficient automated QSAR modeling tools, available to researchers that may lack extensive knowledge of machine learning modeling. Thus, a fully automated and advanced modeling platform can be an important addition to the QSAR community. Results: In the presented workflow the process from data preparation to model building and validation has been completely automated. The most critical modeling tasks (data curation, data set characteristics evaluation, variable selection and validation) that largely influence the performance of QSAR models were focused. It is also included the ability to quickly evaluate the feasibility of a given data set to be modeled. The developed framework is tested on data sets of thirty different problems. The best-optimized feature selection methodology in the developed workflow is able to remove 62-99% of all redundant data. On average, about 19% of the prediction error was reduced by using feature selection producing an increase of 49% in the percentage of variance explained (PVE) compared to models without feature selection. Selecting only the models with a modelability score above 0.6, average PVE scores were 0.71. A strong correlation was verified between the modelability scores and the PVE of the models produced with variable selection. Conclusions: We developed an extendable and highly customizable fully automated QSAR modeling framework. This designed workflow does not require any advanced parameterization nor depends on users decisions or expertise in machine learning/programming. With just a given target or problem, the workflow follows an unbiased standard protocol to develop reliable QSAR models by directly accessing online manually curated databases or by using private data sets. The other distinctive features of the workflow include prior estimation of data modelability to avoid time-consuming modeling trials for non modelable data sets, an efficient variable selection procedure and the facility of output availability at each modeling task for the diverse application and reproduction of historical predictions. The results reached on a selection of thirty QSAR problems suggest that the approach is capable of building reliable models even for challenging problems. [ABSTRACT FROM AUTHOR]

Software engineering research in general and software defects' prediction research in particular are facing serious challenges to their reliability and validity. The major reason is that many of the published research outcomes contradict each other. This phenomenon is mainly caused by the lack of research standards as it exists in many well-established scientific and engineering disciplines. The scope of this paper is to focus on fair benchmarking of the defects' prediction models. By experimenting three prediction algorithms, we found that the quality of the resultant predictions would significantly fluctuate as parameters' values changed. Therefore, any published research results not based on optimized prediction algorithms methods can cause inaccurate and misleading benchmarking and recommendations. Thus, we propose optimizing parameters as an essential research standard to conduct reliable and valid benchmarking. We believe if this standard were approved by interested software quality practitioners and research communities, it will present a vital role in soothing the severity of this phenomenon. The three prediction algorithms we used in our analysis were Support Vector Machine SVM, Multilayer Perceptron MLP, and Naïve Bayesian NB. We used KNIME as a data mining platform to design and run all optimization loops on open source Eclipse 2.0 data set. [ABSTRACT FROM AUTHOR]

Dipeptidyl peptidase-4 (DPP-4) inhibitors are becoming an essential drug in the treatment of type 2 diabetes mellitus; however, some classes of these drugs exert side effects, including joint pain and pancreatitis. Studies suggest that these side effects might be related to secondary inhibition of DPP-8 and DPP-9. In this study, we identified DPP-4-inhibitor hit compounds selective against DPP-8 and DPP-9. We built a virtual screening workflow using a quantitative structure–activity relationship (QSAR) strategy based on artificial intelligence to allow faster screening of millions of molecules for the DPP-4 target relative to other screening methods. Five regression machine learning algorithms and four classification machine learning algorithms were applied to build virtual screening workflows, with the QSAR model applied using support vector regression (R2 pred 0.78) and the classification QSAR model using the random forest algorithm with 92.2% accuracy. Virtual screening results of > 10 million molecules obtained 2 716 hits compounds with a pIC 50 value of > 7.5. Additionally, molecular docking results of several potential hit compounds for DPP-4, DPP-8, and DPP-9 identified CH0002 as showing high inhibitory potential against DPP-4 and low inhibitory potential for DPP-8 and DPP-9 enzymes. These results demonstrated the effectiveness of this technique for identifying DPP-4-inhibitor hit compounds selective for DPP-4 and against DPP-8 and DPP-9 and suggest its potential efficacy for applications to discover hit compounds of other targets. [Display omitted] • 5 models of machine learning were used to build a QSAR model of DPP-4 inhibitor: DL, XGBoost, RF, MLR, and SVR. • The SVR model provided the best performance that meets the QSAR parametric standard value for pIC50 prediction of DPP-4 inhibitor. • From the results of virtual screening and molecular docking, CH0002 showed inhibitory selectivity against DPP4. [ABSTRACT FROM AUTHOR]