مطالب

Cancer is a complex and dangerous disease in which cells uncontrollably begin to grow. Some cells, with mutated genes, cause abnormalities in the cell. These abnormalities are transferred to other genes through specific interactions between genes, leading to disruptions in the normal function of cells. The result of these cell abnormalities will be the occurrence of cancer. In cancer, modules are considered as clusters of genes and regulatory molecules that play a role in the processes of cancer initiation and progression. These modules usually have a specific gene sequence as a central unit that is important in controlling and regulating cellular processes related to cancer.In this study, a novel network-based method called mdGRN is proposed for identifying modules effective in lung cancer occurrence in the gene regulatory network. In this method, first, using gene expression data and regulatory interactions, a lung cancer regulatory network is constructed. Then, using a greedy modularity optimization approach, communities related to lung cancer are identified. Subsequently, the obtained communities are ranked using influence diffusion metrics in the network. Finally, the top-ranked communities are introduced as effective modules.To assess the efficacy of the proposed method, the standard Cancer Genome Atlas (TCGA) database and four classifiers including a decision tree, k-nearest neighbors, support vector machine, and random forest were utilized. The results obtained demonstrated that the proposed mdGRN method outperforms other methods in identifying cancer modules in terms of the average harmonic mean metric with the support vector machine classifier. Additionally, in terms of the AUC metric, the proposed method achieved a value of 0.997 using the random forest classifier, indicating better performance compared to other previous methods in identifying cancer modules. Furthermore, the number of genes identified by the top module is compared with other previous computational and network methods. The results show that the top-ranked module, besides containing a considerable number of driver genes, contains unique genes that have not been identified by other methods.

Blended learning (BL) is being increasingly utilized in teaching and learning languages, therefore, carrying out this study in view of the lack of studies on BL within the Iranian EFL context is very significant. The study conducted at the English Department of Karaj Islamic Azad University provides valuable insights into how instructors and learners perceive the interplay between blended, online, and face-to-face (F2F) instructions. A mixed methods design has been applied to the study. 410 learners, studying English translation, and 46 university instructors responded to a Likert-type questionnaire and an open-ended one and 10 of them were interviewed. The findings revealed that, by incorporating both traditional classroom instruction and online learning tools, students can benefit from a more personalized and interactive learning experience. This approach can also help educators adapt their teaching methods to better meet the needs of diverse learners. In terms of educational policies, there may be a need for increased support and resources for implementing BL modules in language education. Additionally, language teaching programs may need to be revised to include more digital resources and tools to enhance language learning outcomes. Overall, the integration of BL in language classes can lead to more effective and engaging language education practices.

This study aimed to examine the impact of flipped classroom (FC) integrated with Massive Open Online Courses (MOOCs) on EFL learners’ use of self-regulated learning (SRL) strategies in writing. To this end, 60 intermediate college students, who were homogenized by a placement test, were selected and randomly divided into experimental and control groups. The treatment for the experimental group (flipped classroom integrated with MOOCs), took place in three phases, namely before class, in class, and after class. Before class, the materials and sources were delivered via Moodle application, a MOOC-based educational program. In class, the students participated in group discussions and an interactive feedback session. After class, they received online support. The participants in the control group attended the traditional face-to-face writing course, without the use of any technology-based instruments. They underwent only two phases: in-class and after-class activities. Before the treatment, an SRL strategies questionnaire was administered to all the participants. The same questionnaire was again administered to them at the end of the treatment. The results of ANOVA revealed that flipped classroom integrated with MOOCs had a statistically significant positive effect on the experimental group’s overall use of SRL strategies. Similarly, the method, performance, and social environment SRL dimensions improved significantly in the experimental group. Flipped classroom integrated with MOOCs may provide valuable insights for EFL contexts, particularly by encouraging self-regulated learning and reducing teacher workload, which can support the development of academic writing skills.