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. -Anonymity is a crucial anonymization technique that generates -anonymous clusters, where the probability of disclosing a record is 1/k.

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.

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

is a very contagious pathogen in humans, causing pertussis disease. 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.

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

, a member of the coronavirus family, is an RNA virus characterized by a single-stranded genome and is responsible for the development of . The emergence of the Omicron variant of 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 and identify potential immunogenic epitopes in order to design multi-epitope vaccine constructs.

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.