BioMed Research International

Scalable Data Mining Algorithms in Computational Biology and Biomedicine


Publishing date
21 Oct 2016
Status
Published
Submission deadline
03 Jun 2016

Lead Editor

1Tianjin University, Tianjin, China

2Silesian University of Technology, Gliwice, Poland

3South Dakota State University, Brookings, USA

4Wake Forest Baptist Medical Center, Winston-Salem, USA


Scalable Data Mining Algorithms in Computational Biology and Biomedicine

Description

Since "Precision Medicine" was initially launched by President Obama, it presents a huge challenge and chance for the computational biology and biomedicine. In the recent years, computational methods appeared vastly in the biomedicine and bioinformatics research, including medical image analysis, healthcare informatics, and cancer genomics. Lots of prediction and mining works were required on the medical data, such as tumor images, electronic medical records, microarray, and GWAS (Genome-Wide Association Study) data. Therefore, a growing number of data mining algorithms were employed in the prediction tasks of computational biology and biomedicine.

Advanced data mining techniques have also been developed quickly in recent years. Several impacted new methods were reported in the top journals and conferences. For example, affinity propagation was published in Science as a novel clustering algorithm. Recently, deep learning seems to be suitable for big data and is becoming to be the next hot topic. Parallel mechanism is also developed by the scholar and industry researchers, such as Mahout. A growing number of computer scientists are devoted to the advanced large scale data mining techniques. However, application in biomedicine has not fully been addressed and fell behind the technique growth.

This special issue will target the recent large scale data mining techniques together with biomedicine application. Application on medical and biology scalable data is encouraged. We especially encourage clinical or specific diseases genomics research with computational methods. We also welcome novel classification and clustering algorithms, such as strategies for large imbalanced learning, strategies for multiple views learning, strategies for various semisupervised learning, and strategies for multiple kernels learning. Only machine learning theory without biomedicine application cannot be accepted. We encourage authors to supply their codes and open their real biology or medical data, which would make our issue more innovative. Please do not test your algorithm just only on some well-known benchmark datasets.

The special issue welcomes a set of recent advances in the related topics, to provide a platform for researchers to exchange their innovative ideas and real biomedical data.

Potential topics include, but are not limited to:

  • Novel computational strategies for clinical or specific diseases research
  • Large scale classification algorithms with application on biomedicine or bioinformatics
  • Large scale clustering algorithms with application on biomedicine or bioinformatics
  • Imbalanced learning algorithms for biomedical or bioinformatics data
  • Multiple views learning for medical image classification
  • Semisupervised learning strategies for biomedical or bioinformatics data
  • Ensemble learning strategies for biomedical or bioinformatics data
  • Parallel learning techniques for ultra large biomedical or bioinformatics data
  • Multiple kernels learning with application on biomedicine or bioinformatics
  • Multiple labels classification algorithms with application on biomedicine or bioinformatics

Articles

  • Special Issue
  • - Volume 2017
  • - Article ID 5652041
  • - Editorial

Scalable Data Mining Algorithms in Computational Biology and Biomedicine

Quan Zou | Dariusz Mrozek | ... | Yungang Xu
  • Special Issue
  • - Volume 2017
  • - Article ID 8690892
  • - Research Article

Objective Ventricle Segmentation in Brain CT with Ischemic Stroke Based on Anatomical Knowledge

Xiaohua Qian | Yuan Lin | ... | Jing Wang
  • Special Issue
  • - Volume 2016
  • - Article ID 2979081
  • - Research Article

Depth Attenuation Degree Based Visualization for Cardiac Ischemic Electrophysiological Feature Exploration

Fei Yang | Lei Zhang | ... | Henggui Zhang
  • Special Issue
  • - Volume 2016
  • - Article ID 7861274
  • - Research Article

Analysis of Important Gene Ontology Terms and Biological Pathways Related to Pancreatic Cancer

Hang Yin | ShaoPeng Wang | ... | Hailin Liu
  • Special Issue
  • - Volume 2016
  • - Article ID 5428737
  • - Research Article

Functional Region Annotation of Liver CT Image Based on Vascular Tree

Yufei Chen | Xiaodong Yue | ... | Gang Wang
  • Special Issue
  • - Volume 2016
  • - Article ID 9406259
  • - Research Article

Convolutional Deep Belief Networks for Single-Cell/Object Tracking in Computational Biology and Computer Vision

Bineng Zhong | Shengnan Pan | ... | Liujuan Cao
  • Special Issue
  • - Volume 2016
  • - Article ID 7639397
  • - Research Article

An Approach for Predicting Essential Genes Using Multiple Homology Mapping and Machine Learning Algorithms

Hong-Li Hua | Fa-Zhan Zhang | ... | Feng-Biao Guo
  • Special Issue
  • - Volume 2016
  • - Article ID 6802832
  • - Research Article

ProFold: Protein Fold Classification with Additional Structural Features and a Novel Ensemble Classifier

Daozheng Chen | Xiaoyu Tian | ... | Jun Gao
  • Special Issue
  • - Volume 2016
  • - Article ID 5313050
  • - Research Article

Statistical Approaches for the Construction and Interpretation of Human Protein-Protein Interaction Network

Yang Hu | Ying Zhang | ... | Jun Zhang
  • Special Issue
  • - Volume 2016
  • - Article ID 8182416
  • - Research Article

Robust Individual-Cell/Object Tracking via PCANet Deep Network in Biomedicine and Computer Vision

Bineng Zhong | Shengnan Pan | ... | Liujuan Cao
BioMed Research International
 Journal metrics
Acceptance rate31%
Submission to final decision67 days
Acceptance to publication30 days
CiteScore3.600
Impact Factor2.276
 Submit

We are committed to sharing findings related to COVID-19 as quickly as possible. We will be providing unlimited waivers of publication charges for accepted research articles as well as case reports and case series related to COVID-19. Review articles are excluded from this waiver policy. Sign up here as a reviewer to help fast-track new submissions.