The Annals of Proteomics and Bioinformatics (APB) aims to advance scientific understanding of complex biological systems by integrating high-throughput proteomics data with computational and bioinformatics analyses. The journal welcomes interdisciplinary research that connects experimental proteomics with in silico modeling, machine learning, and systems biology approaches to decode the molecular mechanisms of life.

APB strives to bridge the gap between data generation and biological interpretation by publishing research that drives innovation in proteomic technologies, bioinformatics algorithms, and systems integration.

Scope of the Journal

The journal covers a broad spectrum of topics spanning molecular biology, computational sciences, and translational research. APB focuses on studies that employ proteomics and bioinformatics to understand the structure, function, and dynamics of proteins within biological networks and disease models. The journal equally values methodological, theoretical, and applied studies that contribute to reproducibility and transparency in life sciences.

Core Areas	Research Topics
Experimental Proteomics	Mass spectrometry-based proteomics, label-free quantification, protein post-translational modifications, sub-proteome mapping, and structural proteomics.
Computational Bioinformatics	Algorithm design, data mining, AI and machine learning in proteomics, sequence analysis, and high-dimensional data visualization.
Systems and Network Biology	Protein-protein interaction networks, metabolic and signaling pathway modeling, and integrative multi-omics approaches.
Clinical and Translational Proteomics	Biomarker discovery, proteogenomics, drug discovery pipelines, and personalized medicine applications.
Data Standards & FAIR Practices	Data sharing, open databases, and reproducibility frameworks following FAIR (Findable, Accessible, Interoperable, Reusable) principles.

Objectives

To publish original research articles that advance proteomics and bioinformatics sciences.
To foster cross-disciplinary collaboration among molecular biologists, chemists, data scientists, and clinicians.
To promote best practices in research transparency, data availability, and computational reproducibility.
To provide an international platform for the discussion of novel hypotheses, data interpretation methods, and integrative omics frameworks.

APB aims to make proteomics and bioinformatics accessible to the wider scientific community by providing methodological clarity, reproducible workflows, and practical implementation examples.

Article Types Published

Original Research Articles: Comprehensive reports of innovative findings or large-scale data-driven studies.
Reviews: Critical and systematic syntheses of emerging topics in proteomics and computational biology.
Short Communications: Concise descriptions of novel tools, databases, or methods.
Technical Notes: Reports focusing on the development of new protocols, algorithms, or mass spectrometry improvements.
Case Studies and Data Reports: Applications of proteomic or bioinformatic techniques to clinical or environmental problems.
Perspectives and Commentaries: Expert insights discussing evolving trends, ethical challenges, or conceptual advances.

Interdisciplinary Relevance

APB encourages manuscripts that link proteomics and bioinformatics to diverse fields including structural biology, computational chemistry, genomics, metabolomics, and precision medicine. Studies that employ artificial intelligence or cloud-based analytics to handle large proteomic datasets are particularly welcome.

The journal also welcomes interdisciplinary collaborations across computer science, statistics, and biomedical engineering — providing a scientific home for cross-domain research teams working to decode the complexity of biological systems.

Ethical and Scientific Standards

All submissions must adhere to the highest ethical standards in research conduct, including compliance with COPE and ICMJE guidelines, proper citation of data sources, avoidance of plagiarism, and declaration of conflicts of interest.

Authors are expected to deposit datasets in publicly accessible repositories such as PRIDE, ProteomeXchange, or UniProt, with persistent identifiers for reproducibility. Manuscripts that include software development must provide open-source access or adequate documentation to facilitate peer validation.

Audience

The journal’s readership includes academic scientists, pharmaceutical researchers, computational biologists, and bioinformatics software developers. APB provides an academic platform to share breakthroughs that are shaping the next generation of molecular and computational life sciences.

Relevance to Society

Proteomics and bioinformatics together drive the discovery of new diagnostics, personalized therapies, and environmental biomarkers. APB’s mission is to translate data-driven molecular understanding into practical solutions for human health, agriculture, and environmental sustainability.

By publishing high-quality open access research, APB contributes to global scientific literacy and facilitates collaboration among researchers regardless of geographic or economic barriers.

Commitment to Open Science

APB endorses open access as a means to accelerate knowledge transfer. All articles are freely available under the Creative Commons Attribution 4.0 International License (CC BY 4.0), allowing authors to retain copyright and readers to reuse content with proper citation.

The journal actively participates in indexing initiatives and cross-repository integration to ensure long-term accessibility and discoverability of published articles.

Keywords Summary

Proteomics
Bioinformatics
Computational Biology
Systems Biology
Protein Networks
Machine Learning in Omics
Mass Spectrometry
Translational Proteomics
Biomarker Discovery
Data Reproducibility

Aims and Scope