Explainable machine learning approach for cancer prediction through binarilization of RNA sequencing data.

In recent years, researchers have proven the effectiveness and speediness of machine learning-based cancer diagnosis models. However, it is difficult to explain the results generated by machine learning models, especially ones that utilized complex high-dimensional data like RNA sequencing data. In this study, we propose the binarilization technique as a novel way to treat RNA sequencing data and used it to construct explainable cancer prediction models. We tested our proposed data processing technique on five different models, namely neural network, random forest, xgboost, support vector machine, and decision tree, using four cancer datasets collected from the National Cancer Institute Genomic Data Commons. Since our datasets are imbalanced, we evaluated the performance of all models using metrics designed for imbalance performance like geometric mean, Matthews correlation coefficient, F-Measure, and area under the receiver operating characteristic curve. Our approach showed comparative performance while relying on less features. Additionally, we demonstrated that data binarilization offers higher explainability by revealing how each feature affects the prediction. These results demonstrate the potential of data binarilization technique in improving the performance and explainability of RNA sequencing based cancer prediction models.

A Review of the Potency of Plant Extracts and Compounds from Key Families as an Alternative to Synthetic Nematicides: History, Efficacy, and Current Developments.

The global nematicides market is expected to continue growing. With an increasing demand for synthetic chemical-free organic foods, botanical nematicides are taking the lead as replacements. Consequently, in the recent years, there have been vigorous efforts towards identification of the active secondary metabolites from various plants. These include mostly glucosinolates and their hydrolysis products such as isothiocyanates; flavonoids, alkaloids, limonoids, quassinoids, saponins, and the more recently probed essential oils, among others. And despite their overwhelming potential, variabilities in quality, efficacy, potency and composition continue to persist, and commercialization of new botanical nematicides is still lagging. Herein, we have reviewed the history of botanical nematicides and regional progresses, the potency of the identified phytochemicals from the key important plant families, and deciphered some of the impediments involved in standardization of the active compounds in addition to the concerns over the safety of the purified compounds to non-target microbial communities.

Swarm intelligence-based model for improving prediction performance of low-expectation teams in educational software engineering projects.

Software engineering is one of the most significant areas, which extensively used in educational and industrial fields. Software engineering education plays an essential role in keeping students up to date with software technologies, products, and processes that are commonly applied in the software industry. The software development project is one of the most important parts of the software engineering course, because it covers the practical side of the course. This type of project helps strengthening students' skills to collaborate in a team spirit to work on software projects. Software project involves the composition of software product and process parts. Software product part represents software deliverables at each phase of Software Development Life Cycle (SDLC) while software process part captures team activities and behaviors during SDLC. The low-expectation teams face challenges during different stages of software project. Consequently, predicting performance of such teams is one of the most important tasks for learning process in software engineering education. The early prediction of performance for low-expectation teams would help instructors to address difficulties and challenges related to such teams at earliest possible phases of software project to avoid project failure. Several studies attempted to early predict the performance for low-expectation teams at different phases of SDLC. This study introduces swarm intelligence -based model which essentially aims to improve the prediction performance for low-expectation teams at earliest possible phases of SDLC by implementing Particle Swarm Optimization-K Nearest Neighbours (PSO-KNN), and it attempts to reduce the number of selected software product and process features to reach higher accuracy with identifying less than 40 relevant features. Experiments were conducted on the Software Engineering Team Assessment and Prediction (SETAP) project dataset. The proposed model was compared with the related studies and the state-of-the-art Machine Learning (ML) classifiers: Sequential Minimal Optimization (SMO), Simple Linear Regression (SLR), Naïve Bayes (NB), Multilayer Perceptron (MLP), standard KNN, and J48. The proposed model provides superior results compared to the traditional ML classifiers and state-of-the-art studies in the investigated phases of software product and process development.

Promoting Relational Agent for Health Behavior Change in Low and Middle - Income Countries (LMICs): Issues and Approaches.

The use of contemporary technologies in healthcare systems to improve quality of care and to promote behavioral healthcare outcomes are prevalent in high-income countries. However, low and middle-income countries (LMICs) are not receiving the same advantages of technology, which may be due to inadequate technological infrastructure and financial resources, lack of interest among policy makers and healthcare service providers, lack of skills and capacity among healthcare professionals in using technology based interventions, and resistance of the public to the use of technologies for healthcare or health promotion activities. Technology-based interventions offer considerable promise to develop entirely new models of healthcare both within and outside of formal systems of care and offer the opportunity to have a large public health impact. Such technology-based interventions could be used to address targeted global health problems in LMICs, including the chronic non-communicable diseases (NCDs) - a growing health system burden in LMICs. Major preventable behavioral risk factors of chronic NCDs are increasing in LMICs, and innovative interventions are essential to address these risk factors. Computer-based or mobile-based virtual coaches or Relational Agents (RAs) are increasingly being explored for counseling patients to change their health behavior in high-income countries; however, the use of RAs in LMICs has not been studied. In this paper, we summarize the growing application of RA technology in behavior change interventions in high-income countries and describe the potential of its use in LMICs. Finally, we review the potential barriers and challenges in promoting RAs in LMICs.