Designing a Novel Approach Using a Greedy and Information-Theoretic Clustering-Based Algorithm for Anonymizing Microdata Sets.

Data anonymization is a technique that safeguards individuals' privacy by modifying attribute values in published data. However, increased modifications enhance privacy but diminish the utility of published data, necessitating a balance between privacy and utility levels. K-Anonymity is a crucial anonymization technique that generates k-anonymous clusters, where the probability of disclosing a record is 1/k. However, k-anonymity fails to protect against attribute disclosure when the diversity of sensitive values within the anonymous cluster is insufficient. Several techniques have been proposed to address this issue, among which t-closeness is considered one of the most robust privacy techniques. In this paper, we propose a novel approach employing a greedy and information-theoretic clustering-based algorithm to achieve strict privacy protection. The proposed anonymization algorithm commences by clustering the data based on both the similarity of quasi-identifier values and the diversity of sensitive attribute values. In the subsequent adjustment phase, the algorithm splits and merges the clusters to ensure that they each possess at least k members and adhere to the t-closeness requirements. Finally, the algorithm replaces the quasi-identifier values of the records in each cluster with the values of the cluster center to attain k-anonymity and t-closeness. Experimental results on three microdata sets from Facebook, Twitter, and Google+ demonstrate the proposed algorithm's ability to preserve the utility of released data by minimizing the modifications of attribute values while satisfying the k-anonymity and t-closeness constraints.

A novel approach to design a multiepitope peptide as a vaccine candidate for Bordetella pertussis.

Bordetella pertussis is a very contagious pathogen in humans, causing pertussis disease. Pertussis is one of the 10 leading causes of death due to infectious diseases, especially among infants and children. Antibiotic-resistant strains have recently emerged in this bacterium, and despite the high vaccination coverage, the prevalence of this disease has been increasing recently in both developed and developing countries. The objective of this study is to introduce a novel in silico vaccine candidate aimed at countering B. pertussis effectively. Differing from other comparable studies, this research employed a computational screening methodology to assess the genome of 'Bordetella pertussis 18323.' The purpose was to identify an innovative antigen for the development of a vaccine against B. pertussis. Notably, our investigation introduces an innovative antigen distinguished by its elevated immunogenicity score. Importantly, this antigen lacks toxicity and allergenicity, making it recognizable to the immune system and thus capable of inducing a robust immune response. In the subsequent phase, our antigen was utilized to identify potential epitopes conducive to the construction of a B. pertussis vaccine. These epitopes, alongside linkers, his-tag and adjuvants, were amalgamated to form the vaccine candidate. Subsequently, a comprehensive evaluation of the vaccine was conducted, encompassing various computational tests such as secondary and tertiary structure analysis, physicochemical examination, and structural analysis involving docking and molecular dynamics simulations. Importantly, our vaccine successfully passed all in silico tests.Communicated by Ramaswamy H. Sarma.

A novel efficient drug repurposing framework through drug-disease association data integration using convolutional neural networks.

Drug repurposing is an exciting field of research toward recognizing a new FDA-approved drug target for the treatment of a specific disease. It has received extensive attention regarding the tedious, time-consuming, and highly expensive procedure with a high risk of failure of new drug discovery. Data-driven approaches are an important class of methods that have been introduced for identifying a candidate drug against a target disease. In the present study, a model is proposed illustrating the integration of drug-disease association data for drug repurposing using a deep neural network. The model, so-called IDDI-DNN, primarily constructs similarity matrices for drug-related properties (three matrices), disease-related properties (two matrices), and drug-disease associations (one matrix). Then, these matrices are integrated into a unique matrix through a two-step procedure benefiting from the similarity network fusion method. The model uses a constructed matrix for the prediction of novel and unknown drug-disease associations through a convolutional neural network. The proposed model was evaluated comparatively using two different datasets including the gold standard dataset and DNdataset. Comparing the results of evaluations indicates that IDDI-DNN outperforms other state-of-the-art methods concerning prediction accuracy.

Design of a multi-epitope-based peptide vaccine against the SARS-CoV-2 Omicron variant using bioinformatics approach.

SARS-CoV-2, a member of the coronavirus family, is an RNA virus characterized by a single-stranded genome and is responsible for the development of COVID-19. The emergence of the Omicron variant of SARS-CoV-2 in 2021 marked a significant variation recognized by the World Health Organization. The primary objective of this study is to investigate the spike glycoprotein of the Omicron variant of SARS-CoV-2 and identify potential immunogenic epitopes in order to design multi-epitope vaccine constructs. Among the other major structural proteins of the coronavirus, the spike glycoprotein stands out as the largest. Importantly, individuals who have recovered from SARS-CoV-2 and COVID-19 were found to possess antibodies that target the spike glycoprotein. This article asserts that the vaccine presented in this study has the potential to elicit immune responses against previous variants, including the Omicron variant, as well as future variations. This is attributed to the utilization of a Java-based tool, which facilitated the identification of conserved epitopes with high immunogenicity scores, ensuring their non-toxic and non-allergenic properties. Our analysis provides strong evidence for the conservation of these epitopes across all coronavirus sequences detected in various countries since the beginning of the pandemic. The vaccine was subsequently constructed by integrating the identified conserved epitopes with linkers and adjuvants. The vaccine was subsequently evaluated through computational tests to assess their efficacy and performance.Communicated by Ramaswamy H. Sarma.

A New Deep-Learning-Based Model for Breast Cancer Diagnosis from Medical Images.

Breast cancer is one of the most prevalent cancers among women worldwide, and early detection of the disease can be lifesaving. Detecting breast cancer early allows for treatment to begin faster, increasing the chances of a successful outcome. Machine learning helps in the early detection of breast cancer even in places where there is no access to a specialist doctor. The rapid advancement of machine learning, and particularly deep learning, leads to an increase in the medical imaging community's interest in applying these techniques to improve the accuracy of cancer screening. Most of the data related to diseases is scarce. On the other hand, deep-learning models need much data to learn well. For this reason, the existing deep-learning models on medical images cannot work as well as other images. To overcome this limitation and improve breast cancer classification detection, inspired by two state-of-the-art deep networks, GoogLeNet and residual block, and developing several new features, this paper proposes a new deep model to classify breast cancer. Utilizing adopted granular computing, shortcut connection, two learnable activation functions instead of traditional activation functions, and an attention mechanism is expected to improve the accuracy of diagnosis and consequently decrease the load on doctors. Granular computing can improve diagnosis accuracy by capturing more detailed and fine-grained information about cancer images. The proposed model's superiority is demonstrated by comparing it to several state-of-the-art deep models and existing works using two case studies. The proposed model achieved an accuracy of 93% and 95% on ultrasound images and breast histopathology images, respectively.