Feature Representation and Learning Methods With Applications in Large-Scale Biological Sequence Analysis

Submission Deadline: 01 August 2020

Submission Deadline: 01 August 2020

IEEE Access invites manuscript submissions in the area of Feature Representation and Learning Methods With Applications in Large-Scale Biological Sequence Analysis.

In recent years, with the rapid growth of biological data (especially biological sequences in the bioinformatics field) data-driven computational methods are increasingly needed to quickly and accurately analyze large-scale biological data.  Machine learning has recently emerged as an “intelligent” method in many specific bioinformatics areas, such as identification of DNA mutations, and protein post-transcriptional modifications, etc. Methods to find discriminative feature representation for biological sequences is quite important, as it greatly impacts the performance of machine learning methods. A good representation is often the one that captures the discriminative information from the data and supports effective machine learning. As a result, it is fundamentally important for the development of new feature representation approaches in machine learning. Over the last few decades, feature representation engineering in different fields has been well studied; however, most are labor intensive and heavily dependent on the professional knowledge of researchers, highlighting the weakness of current learning algorithms. To expand the scope and ease of the applicability of machine learning, it is highly desirable to make learning algorithms less dependent on handcrafted feature engineering so that novel applications could be constructed faster, and more importantly, to make progress toward artificial intelligence (AI).

This Special Section in IEEE Access will target recent feature representation and learning techniques in bioinformatics applications. Applications on large-scale biological data are strongly encouraged. We also encourage authors to contribute their codes and experimental data so they are available to the public, which would make our Special Section more infusive and attractive.

The topics of interest include, but are not limited to:

  • Supervised learning algorithms with applications in large-scale sequence analysis
  • Unsupervised learning algorithms with applications in large-scale sequence analysis
  • Imbalanced learning algorithms for biological sequence data
  • Multi-view feature learning for DNA or Protein classification
  • Deep learning-based feature learning strategies for biological data
  • Feature representation optimization algorithms with applications on bioinformatics.
  • Handcrafted feature representation algorithms for bioinformatic application


We also highly recommend the submission of multimedia with each article as it significantly increases the visibility, downloads, and citations of articles.


Associate Editor:  Quan Zou, University of Electronic Science and Technology of China, China

Guest Editors:

    1. Leyi Wei, University of Tokyo, Japan
    2. Qin Ma, Ohio State University, USA
    3. Jijun Tang, University of South Carolina, USA
    4. Dariusz Mrozek, Silesian University of Technology, Poland
    5. Guobao Xiao, Minjiang University, China


Relevant IEEE Access Special Sections:

  1. Scalable Deep Learning for Big Data
  2. Deep Learning Algorithms for Internet of Medical Things
  3. Smart Health Sensing and Computational Intelligence: From Big Data to Big Impacts

IEEE Access Editor-in-Chief:
  Prof. Derek Abbott, University of Adelaide

Article submission: Contact Associate Editor and submit manuscript to:

For inquiries regarding this Special Section, please contact: zouquan@tju.edu.cn.