Computational and Mathematical Methods in Medicine

Applications of Machine Learning in Genomics and Systems Biology


Publishing date
31 Aug 2012
Status
Published
Submission deadline
13 Apr 2012

Lead Editor

1Howard University, Washington, DC 20059, USA

2East Stroudsburg University, East Stroudsburg, PA 18301, USA

3Rochester Institute of Technology, Rochester, NY 14623, USA

4University of Maryland Eastern Shore, Princess Anne, MD 21853, USA


Applications of Machine Learning in Genomics and Systems Biology

Description

At the accomplishment of the human genome project, techniques that can analyze large amounts of data are urgently needed. Advances in computational techniques for analyzing high-throughput data in genomics, proteomics, and visualization have been extensively studied and have played vital roles in understanding biological mechanisms. Machine learning and related techniques such as support vector machines, Markov models, decision trees, and neural networks have been increasingly used to solve problems in genomics and systems biology.

The main focus of this special issue is on new applications and developments of machine learning techniques to address the contemporary problems in genomics and systems biology, especially those computationally hard problems and those which involve randomness and noisy data. This special issue will serve as an international platform for researchers who have an expertise in machine learning, genomics, systems biology, and their applications in medicine. It will also serve as a forum for researchers to discuss recent advancements in machine learning methods in the field. Potential topics include, but are not limited to:

  • Data mining and pattern recognition methods for next-generation sequencing data analysis
  • Data management and data visualization methods and tools
  • Biomarker data integration and information retrieval
  • Identification of structural variations
  • Computational proteomics for clinical applications
  • Prediction of protein structure and protein-protein interactions
  • Large-scale data integration for genomics or proteomics data

Before submission authors should carefully read over the journal's Author Guidelines, which are located at http://www.hindawi.com/journals/cmmm/guidelines/. Prospective authors should submit an electronic copy of their complete manuscript through the journal Manuscript Tracking System at http://mts.hindawi.com/ according to the following timetable:


Articles

  • Special Issue
  • - Volume 2013
  • - Article ID 587492
  • - Editorial

Applications of Machine Learning in Genomics and Systems Biology

Chunmei Liu | Dongsheng Che | ... | Yinglei Song
  • Special Issue
  • - Volume 2013
  • - Article ID 856281
  • - Research Article

Efficient Identification of Transcription Factor Binding Sites with a Graph Theoretic Approach

Jia Song | Li Xu | Hong Sun
  • Special Issue
  • - Volume 2012
  • - Article ID 892098
  • - Research Article

A Dynamic Data-Driven Framework for Biological Data Using 2D Barcodes

Hui Li | Chunmei Liu
  • Special Issue
  • - Volume 2012
  • - Article ID 135780
  • - Research Article

Biomarker Identification Using Text Mining

Hui Li | Chunmei Liu
  • Special Issue
  • - Volume 2012
  • - Article ID 696190
  • - Research Article

Identification of Novel Type III Effectors Using Latent Dirichlet Allocation

Yang Yang
  • Special Issue
  • - Volume 2012
  • - Article ID 127130
  • - Research Article

Prediction of Breeding Values for Dairy Cattle Using Artificial Neural Networks and Neuro-Fuzzy Systems

Saleh Shahinfar | Hassan Mehrabani-Yeganeh | ... | Kent A. Weigel
  • Special Issue
  • - Volume 2012
  • - Article ID 320698
  • - Research Article

A Novel Weighted Support Vector Machine Based on Particle Swarm Optimization for Gene Selection and Tumor Classification

Mohammad Javad Abdi | Seyed Mohammad Hosseini | Mansoor Rezghi
Computational and Mathematical Methods in Medicine
 Journal metrics
Acceptance rate28%
Submission to final decision86 days
Acceptance to publication45 days
CiteScore1.840
Impact Factor1.563
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