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Computational analysis of Biological Images

Guest Editor: Professor Ge Yang

As part of the launch of the journal section “Imaging and image analysis”, BMC Bioinformatics is excited to announce that we are now accepting manuscripts for the thematic series Computational analysis of Biological images.

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The purpose of this thematic series is to present latest advances in computational techniques for analyzing and understanding biological images, with their applications to biology and/or medicine.

We welcome manuscripts describing novel or updated computational techniques for analyzing and understanding biological image data, including, but not limited to

• Machine/deep learning based analysis techniques

• Image segmentation techniques

• Image object tracking techniques

• Image registration techniques

• Image classification techniques

• Techniques for analyzing images from new modalities such as light-sheet microscopy, super-resolution microscopy, CryoEM

• Techniques for analyzing and/or mining large scale image data (big image data)

• Techniques for modeling and representing image objects and/or events

• Techniques for performance characterization and comparison of algorithms

We also welcome manuscripts describing novel applications of computational analysis of biological images. Such applications include, but are not limited to

• Computational and systems biology

• Synthetic biology

• Disease diagnosis

• Drug discovery

• High throughput/high content studies

• Translational applications such as disease marker discovery

Please submit directly to BMC Bioinformatics stating in your cover letter that it is for the Computational analysis of Biological Images collection. Alternatively you can email your pre-submission inquiries to the Editor of BMC Bioinformatics at

All manuscripts submitted for inclusion in the series by June 10th 2019 will be considered.

  1. Cryo-electron tomography is an important and powerful technique to explore the structure, abundance, and location of ultrastructure in a near-native state. It contains detailed information of all macromolecula...

    Authors: Sinuo Liu, Xiaojuan Ban, Xiangrui Zeng, Fengnian Zhao, Yuan Gao, Wenjie Wu, Hongpan Zhang, Feiyang Chen, Thomas Hall, Xin Gao and Min Xu

    Citation: BMC Bioinformatics 2020 21:399

    Content type: Methodology article

    Published on:

  2. Currently the combination of molecular tools, imaging techniques and analysis software offer the possibility of studying gene activity through the use of fluorescent reporters and infer its distribution within...

    Authors: David R. Espeso, Elena Algar, Esteban Martínez-García and Víctor de Lorenzo

    Citation: BMC Bioinformatics 2020 21:224

    Content type: Methodology article

    Published on:

  3. The protein ki67 (pki67) is a marker of tumor aggressiveness, and its expression has been proven to be useful in the prognostic and predictive evaluation of several types of tumors. To numerically quantify the...

    Authors: Barbara Rita Barricelli, Elena Casiraghi, Jessica Gliozzo, Veronica Huber, Biagio Eugenio Leone, Alessandro Rizzi and Barbara Vergani

    Citation: BMC Bioinformatics 2019 20:733

    Content type: Methodology article

    Published on: