Journal of Healthcare Engineering

Deep Learning in Bioinformatics and Biomedical Images


Publishing date
01 Apr 2021
Status
Closed
Submission deadline
04 Dec 2020

Lead Editor

1Sichuan University, Chengdu, China

2Beijing Institute of Technology, Beijing, China

3Jilin University, Changchun, China

4Hainan Normal University, Hainan, China

This issue is now closed for submissions.
More articles will be published in the near future.

Deep Learning in Bioinformatics and Biomedical Images

This issue is now closed for submissions.
More articles will be published in the near future.

Description

Bioinformatics and Biomedical Imaging are frontiers and interdisciplinary subjects derived from the theories and methodologies of comprehensive computer science, life science and biology, which play integral roles in disease diagnosis and therapy. In recent years, the fields of medical science and health informatics have made great progress and have led to in-depth analytics that is demanded by generation, collection and accumulation of massive data, which are no longer competent through traditional analytical methods. On the other hand, algorithms in bioinformatics and biomedical image analysis have been significantly improved thanks to the rapid development of deep learning (including convolutional neural networks, recurrent neural networks, auto-encoders, generative adversarial networks, and so on). Accordingly, the application of deep learning in bioinformatics and biomedical images to gain insight from data has been emphasised in both academic and medical fields.

At present, due to the rapid development of biotechnology in the historical period, the biomedical data generated in various research and application fields has increased exponentially, ranging from molecular level (gene functions, protein interactions, metabolic pathway, etc.), biological tissue level (brain connectivity map, X-ray images, magnetic resonance images, etc.), clinical level (intensive care unit, electronic medical record, etc.). The unneglectable fact is that growth speed and heterogeneous structure make it much more challenging to handle biomedical data with such properties than conventional data analysis methods as usual. Therefore, it is necessary to establish more powerful theoretical methods and practical tools for analysing and extracting meaningful information from above mentioned complex bio-data. Analysing these complex and heterogeneous data is a typical complex system problem. We need to analyse the dependence, relationship, or interaction between different levels of data and its environment. In this case, due to the non-linear, emergent, spontaneous order, adaptation, and feedback loop characteristics of the raw data, it is difficult for us to model by traditional methods. Only through deep learning can we solve these problems.

This Special Issue seeks to highlight the latest developments in applying advanced deep learning techniques in bioinformatics and biomedical image analysis. Both original research papers and review articles related to deep learning in genomics and medical images analysis will be considered for publication.

Potential topics include but are not limited to the following:

  • Deep learning methods in genome sequencing and single cell sequencing
  • Deep learning methods in epigenomics and genomics regulatory analysis
  • Deep learning methods in 3D genomics, functional genomics, and medical genomics
  • Deep learning methods in multi-omics integration
  • Deep learning methods in biomedical signal/image analysis
  • Deep learning methods for the classification of lesions, tissue, and disease in ultrasound/CT/MRI
  • Deep learning methods for computer-aided detection in ultrasound/CT/MRI
  • Deep learning methods in image registration or fusion with ultrasound/CT/MRI
  • Deep learning methods for segmentation, denoising, and super-resolution in ultrasound/CT/MRI
  • Artificial intelligence methods and algorithms in bioinformatics and biomedical images
  • Online database and webserver based on artificial intelligence and parallel acceleration technology in bioinformatics and biomedical images

Articles

  • Special Issue
  • - Volume 2021
  • - Article ID 6698176
  • - Research Article

DiaMole: Mole Detection and Segmentation Software for Mobile Phone Skin Images

Shuai Liu | Zheng Chen | ... | Fengfeng Zhou
  • Special Issue
  • - Volume 2021
  • - Article ID 6657324
  • - Research Article

Association between Single Nucleotide Polymorphism rs9891119 of STAT3 Gene and the Genetic Susceptibility to Type 2 Diabetes in Chinese Han Population from Guangdong

Haibing Yu | Xuyun Xu | ... | Yuanlin Ding
  • Special Issue
  • - Volume 2021
  • - Article ID 6695108
  • - Research Article

A Systematic Approach for MRI Brain Tumor Localization and Segmentation Using Deep Learning and Active Contouring

Shanaka Ramesh Gunasekara | H. N. T. K. Kaldera | Maheshi B. Dissanayake
  • Special Issue
  • - Volume 2021
  • - Article ID 6699996
  • - Research Article

Deep Learning Method for RNA Secondary Structure Prediction with Pseudoknots Based on Large-Scale Data

Bowen Shen | Hao Zhang | ... | Yuanning Liu
  • Special Issue
  • - Volume 2021
  • - Article ID 6656763
  • - Research Article

Research on Transboundary Regulation of Plant-Derived Exogenous MiRNA Based on Biological Big Data

Zhi Li | Xu Wei | ... | Liwan Zhu
  • Special Issue
  • - Volume 2021
  • - Article ID 6676194
  • - Research Article

Gene Sequence Assembly Algorithm Model Based on the DBG Strategy and Its Application

Haihe Shi | Gang Wu
  • Special Issue
  • - Volume 2021
  • - Article ID 6692775
  • - Research Article

A New Implementation of Genome Rearrangement Problem

Xiaoqian Jing | Haihe Shi
  • Special Issue
  • - Volume 2021
  • - Article ID 6636710
  • - Research Article

New Construction of Family of MLCS Algorithms

Haihe Shi | Jun Wang
  • Special Issue
  • - Volume 2021
  • - Article ID 6674695
  • - Research Article

Extreme Learning Machine for Heartbeat Classification with Hybrid Time-Domain and Wavelet Time-Frequency Features

Yuefan Xu | Sen Zhang | ... | Wendong Xiao
Journal of Healthcare Engineering
 Journal metrics
Acceptance rate37%
Submission to final decision99 days
Acceptance to publication48 days
CiteScore4.600
Impact Factor2.682
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Article of the Year Award: Outstanding research contributions of 2020, as selected by our Chief Editors. Read the winning articles.