Evolutionary algorithms guided by Erdos-Rényi complex networks.

This article proposes an evolutionary algorithm integrating Erdos-Rényi complex networks to regulate population crossovers, enhancing candidate solution refinement across generations. In this context, the population is conceptualized as a set of interrelated solutions, resembling a complex network. The algorithm enhances solutions by introducing new connections between them, thereby influencing population dynamics and optimizing the problem-solving process. The study conducts experiments comparing four instances of the traditional optimization problem known as the Traveling Salesman Problem (TSP). These experiments employ the traditional evolutionary algorithm, alternative algorithms utilizing different types of complex networks, and the proposed algorithm. The findings suggest that the approach guided by an Erdos-Rényi dynamic network surpasses the performance of the other algorithms. The proposed model exhibits improved convergence rates and shorter execution times. Thus, strategies based on complex networks reveal that network characteristics provide valuable information for solving optimization problems. Therefore, complex networks can regulate the decision-making process, similar to optimizing problems. This work emphasizes that the network structure is crucial in adding value to decision-making.

Early prediction of student performance in CS1 programming courses.

There is a high failure rate and low academic performance observed in programming courses. To address these issues, it is crucial to predict student performance at an early stage. This allows teachers to provide timely support and interventions to help students achieve their learning objectives. The prediction of student performance has gained significant attention, with researchers focusing on machine learning features and algorithms to improve predictions. This article proposes a model for predicting student performance in a 16-week CS1 programming course, specifically in weeks 3, 5, and 7. The model utilizes three key factors: grades, delivery time, and the number of attempts made by students in programming labs and an exam. Eight classification algorithms were employed to train and evaluate the model, with performance assessed using metrics such as accuracy, recall, F1 score, and AUC. In week 3, the gradient boosting classifier (GBC) achieved the best results with an F1 score of 86%, followed closely by the random forest classifier (RFC) with 83%. These findings demonstrate the potential of the proposed model in accurately predicting student performance.

Predicting the final grade using a machine learning regression model: insights from fifty percent of total course grades in CS1 courses.

This article introduces a model for accurately predicting students' final grades in the CS1 course by utilizing their grades from the first half of the course. The methodology includes three phases: training, testing, and validation, employing four regression algorithms: AdaBoost, Random Forest, Support Vector Regression (SVR), and XGBoost. Notably, the SVR algorithm outperformed the others, achieving an impressive R-squared (R2) value ranging from 72% to 91%. The discussion section focuses on four crucial aspects: the selection of data features and the percentage of course grades used for training, the comparison between predicted and actual values to demonstrate reliability, and the model's performance compared to existing literature models, highlighting its effectiveness.

Prediction of virus-host protein-protein interactions mediated by short linear motifs.

BACKGROUND: Short linear motifs in host organisms proteins can be mimicked by viruses to create protein-protein interactions that disable or control metabolic pathways. Given that viral linear motif instances of host motif regular expressions can be found by chance, it is necessary to develop filtering methods of functional linear motifs. We conduct a systematic comparison of linear motifs filtering methods to develop a computational approach for predicting motif-mediated protein-protein interactions between human and the human immunodeficiency virus 1 (HIV-1). RESULTS: We implemented three filtering methods to obtain linear motif sets: 1) conserved in viral proteins (C), 2) located in disordered regions (D) and 3) rare or scarce in a set of randomized viral sequences (R). The sets C,D,R are united and intersected. The resulting sets are compared by the number of protein-protein interactions correctly inferred with them - with experimental validation. The comparison is done with HIV-1 sequences and interactions from the National Institute of Allergy and Infectious Diseases (NIAID). The number of correctly inferred interactions allows to rank the interactions by the sets used to deduce them: D∪R and C. The ordering of the sets is descending on the probability of capturing functional interactions. With respect to HIV-1, the sets C∪R, D∪R, C∪D∪R infer all known interactions between HIV1 and human proteins mediated by linear motifs. We found that the majority of conserved linear motifs in the virus are located in disordered regions. CONCLUSION: We have developed a method for predicting protein-protein interactions mediated by linear motifs between HIV-1 and human proteins. The method only use protein sequences as inputs. We can extend the software developed to any other eukaryotic virus and host in order to find and rank candidate interactions. In future works we will use it to explore possible viral attack mechanisms based on linear motif mimicry.