First Issue of Biological Data Links Six Studies into a Single Thread

• 2026-07-01

The first issue of Biological Data (Volume 1, Issue 1, June 2026) brings together six articles that, read side by side, form a chain of connected questions about how biological data can be turned into better medicine. The issue opens with Hamid Ghaznavi's original research on association rule mining, which reveals relationships between IgG and symptoms in COVID-19 patients and points toward personalized medicine. From there, Elahe Ghiyabi, Houshang Shaker Nigjeh, and Sajjad Mortazavi review integrated computational frameworks for multiscale modeling of cancer prevention, extending the theme from patient-level patterns to prevention. Amirhossein Akhoondi, Mostafa Rafeipour, and Farinaz Behfarjam then offer a brief report reanalyzing substantia nigra transcriptomes in Parkinson's disease, carrying the computational lens into neurodegenerative disease. Hogr Zainaddeen Hassan, Najmadeen Mohammed Saeed, and Sardar Abdullah Husein widen the view with a bibliometric mapping of multidisciplinary COVID-19 research, showing how the field itself is structured. Arash Babaei's original article revisits the sterile tissue paradigm through a systematic reanalysis of microbial signals in germ-free mouse tissues, questioning a long-held assumption. The chain closes with Samad Najjar-Ghabel, Shamim Yousefi, and Soheila Yaghobi Niari's review of hybrid CNN-Transformer models for ECG-based cardiac diagnosis, returning the thread to clinical data and personalized care.

Each link in this chain, from symptom patterns to prevention, from transcriptomes to microbial signals, from research mapping to diagnostic models, speaks to the same underlying question: how can biological data be connected, reexamined, and put to clinical use?