BioMed Research International

Scalable Machine Learning Algorithms in Computational Biology and Biomedicine 2020


Publishing date
01 Nov 2020
Status
Closed
Submission deadline
03 Jul 2020

Lead Editor

1University of Electronic Science and Technology of China, Chengdu, China

2Silesian University of Technology, Gliwice, Poland

3The Ohio State University, Columbus, USA

4University of Texas Health Science Center at Houston, Houston, USA

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

Scalable Machine Learning Algorithms in Computational Biology and Biomedicine 2020

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

Description

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

Advanced machine learning techniques have also developed quickly in recent years. Several impacted new methods were reported in 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 become to be the next hot topic. Parallel mechanism is also developed by the scholars and industry researchers, such as Mahout. A growing number of computer scientists devote to the advanced large-scale data mining techniques. However, the application in biomedicine has not fully been addressed and fell behind the technique growth.

This Special Issue aims to target the recent large-scale machine learning techniques together with biomedicine applications. Applications in medical and biological scalable data are 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 semi-supervised learning, strategies for multiple kernels learning, etc. Both original research and review articles are welcomed.

Potential topics include but are not limited to the following:

  • Novel computational strategies for clinical or specific diseases research
  • Large scale classification algorithms with application to biomedicine or bioinformatics
  • Large scale clustering algorithms with application to biomedicine or bioinformatics
  • Imbalanced learning algorithms for biomedical or bioinformatics data
  • Multiple views learning from medical image classification
  • Semi-supervised 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 to biomedicine or bioinformatics
  • Multiple labels classification algorithms with application to biomedicine or bioinformatics

Articles

  • Special Issue
  • - Volume 2020
  • - Article ID 3508107
  • - Research Article

SSH: A Tool for Predicting Hydrophobic Interaction of Monoclonal Antibodies Using Sequences

Anthony Mackitz Dzisoo | Juanjuan Kang | ... | Jian Huang
  • Special Issue
  • - Volume 2020
  • - Article ID 4675395
  • - Research Article

A Novel Triple Matrix Factorization Method for Detecting Drug-Side Effect Association Based on Kernel Target Alignment

Xiaoyi Guo | Wei Zhou | ... | Fei Guo
  • Special Issue
  • - Volume 2020
  • - Article ID 2468789
  • - Research Article

Its2vec: Fungal Species Identification Using Sequence Embedding and Random Forest Classification

Chao Wang | Ying Zhang | Shuguang Han
  • Special Issue
  • - Volume 2020
  • - Article ID 9235920
  • - Research Article

Identification of Human Enzymes Using Amino Acid Composition and the Composition of -Spaced Amino Acid Pairs

Lifu Zhang | Benzhi Dong | ... | Liran Juan
  • Special Issue
  • - Volume 2020
  • - Article ID 2654815
  • - Research Article

Development of a Novel Tool for the Retrieval and Analysis of Hormone Receptor Expression Characteristics in Metastatic Breast Cancer via Data Mining on Pathology Reports

Kai-Po Chang | John Wang | ... | Yen-Wei Chu
  • Special Issue
  • - Volume 2020
  • - Article ID 7297631
  • - Research Article

PredDBP-Stack: Prediction of DNA-Binding Proteins from HMM Profiles using a Stacked Ensemble Method

Jun Wang | Huiwen Zheng | ... | Taigang Liu
  • Special Issue
  • - Volume 2020
  • - Article ID 7584968
  • - Research Article

Protein Contact Map Prediction Based on ResNet and DenseNet

Zhong Li | Yuele Lin | ... | Yuhua Yao
  • Special Issue
  • - Volume 2020
  • - Article ID 4569037
  • - Research Article

IMPContact: An Interhelical Residue Contact Prediction Method

Chao Fang | Yajie Jia | ... | Han Wang
BioMed Research International
 Journal metrics
Acceptance rate31%
Submission to final decision67 days
Acceptance to publication30 days
CiteScore3.600
Impact Factor2.276
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