Welcome To
Current Proteomics

About the Journal

Aims & Scope

Research in the emerging field of proteomics is growing at an extremely rapid rate. The principal aim of Current Proteomics is to publish well-timed review articles in this fast-expanding area on topics relevant and significant to the development of proteomics. Current Proteomics is an essential journal for everyone involved in proteomics and related fields in both academia and industry.

Current Proteomics publishes review articles in all aspects of the fast-expanding field of proteomics. All areas of proteomics are covered together with the methodology, softwares, databases, technological advances and applications of proteomics, including functional proteomics. Diverse technologies covered include but are not limited to:

Ranking and Category

Endorsement(s)

"The wide areas covered by these articles gives an idea of how diverse the field is."

Deb Chakravarti
Quoted in Advances In: Proteomics - Preparing proteins' published in march 25, 2005 issue of science

Announcements

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Welcome To
Current Proteomics

About the Journal

Aims & Scope

Ranking and Category

Meet Our Editorial Board Member

Editorial: The Novel Methods of Computational Proteomics and their Applications to Bio-medicine and Bio-pharmacy

An Overview on Protein Fold Classification via Machine Learning Approach

A Hybrid Discrete Imperialist Competition Algorithm for Gene Selection for Microarray Data

Identifying the Characteristics of the Hypusination Sites Using SMOTE and SVM Algorithm with Feature Selection

Complex Detection in PPI Network Using Genes Expression Information

A Method for Lymph Node Segmentation with Scaling Features in a Random Forest Model

A Homology and Pseudo Amino Acid Composition-based Multi-label Model for Predicting Human Membrane Protein Types

Pathway Crosstalk Analysis based on Signaling Pathway Impact Analysis in Alzheimer's Disease

Prediction of Sphingomonas Protein Coding Regions Based on 3-Base Periodicity Analysis Method

Human Disease-Protein Network

Incorporating Link Information in Feature Selection for Identifying Tumor Biomarkers by Using miRNA-mRNA Paired Expression Data