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IEEE Transactions on Computational Biology and Bioinformatics is a peer-reviewed journal published by IEEE, covering Bioinformatics and Genomic Networks, Machine Learning in Bioinformatics, Computational Drug Discovery Methods. Over its lifetime it has published 483 papers, which have received 516 citations.
| Publisher | IEEE |
|---|---|
| Reference style | Numbered (IEEE) Numbered — [1], [2] in the text [1] A. Smith, B. Jones, and C. Lee, "A representative article title," IEEE Transactions on Computational Biology and Bioinformatics, vol. 12, no. 3, pp. 45–58, 2023.
Formats any DOI in IEEE Transactions on Computational Biology and Bioinformatics style. No sign-up. |
| Abbreviation (ISO-4) | TCBB |
| Publishes research in | Bioinformatics and Genomic Networks Machine Learning in Bioinformatics Computational Drug Discovery Methods Gene expression and cancer classification Single-cell and spatial transcriptomics |
| ISSN | 2998-4165 |
| Citation impact (2-yr) | 1.07 |
| h-index | 9 |
| i10-index | 9 |
| Top institutions publishing here | Central South University |
| You get | A submission-ready PDF and the editable LaTeX source — ready to submit. |
Citations each year’s papers have accumulated so far — the most recent years are still building up.
Human immunodeficiency virus type 1 (HIV-1) is one of the main causative agents of acquired immunodeficiency syndrome (AIDS), and effectively identifying HIV-1 protease cleavage sites (PCSs) is of great importance for the design of new anti-AIDS inhibitors. Computational prediction of HIV-1 PCSs can be used to discover new cleavable substrates, and further facilitates the understanding…
By generating massive gene transcriptome data and analyzing transcriptomic variations at the cell level, single-cell RNA-sequencing (scRNA-seq) technology has provided new way to explore cellular heterogeneity and functionality. Clustering scRNA-seq data could discover the hidden diversity and complexity of cell populations, which can aid to the identification of the disease mechanisms and biomarkers. In this…
Plant diseases have been detrimental for the agriculture industry, as they cause substantial crop loss globally. To overcome this, IoT and AI-based smart agriculture solutions are being deployed for plant disease detection. However, a diverse range of crops and their diseases pose enormous challenges to these methods. Additionally, limited generalizability and the black-box nature of…
Over the past few years, artificial intelligence (AI) has emerged as a transformative force in drug discovery and development (DDD), revolutionizing many aspects of the process. This survey provides a comprehensive review of recent advancements in AI applications within early drug discovery and post-market drug assessment. It addresses the identification and prioritization of new therapeutic…
Drug-target affinity (DTA) prediction plays a crucial role in accelerating the drug development process. Although deep learning-based models achieve strong performance in benchmark datasets, their predictive accuracy declines sharply in cold-start scenarios, i.e., when encountering drugs or proteins absent from the training set. This limitation arises from the restricted scale of training datasets, leading to…
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