BioMed Research International

Analysis and Modeling for Big Data in Cancer Research


Status
Published

Lead Editor

1Shanghai University, Shanghai, China

2Ohio University, Athens, USA

3China Academy of Chinese Medical Sciences, Beijing, China

4University of Western Australia (Go8), Crawley, Australia

5University of Sydney, Sydney, NSW, Australia


Analysis and Modeling for Big Data in Cancer Research

Description

Cancer, a class of diseases characterized by out-of-control cell growth, is the second most common cause of death and greatly threatens people’s health. According to the survey of the World Health Organization in 2012, there were four million new cancer cases and 8.2 million cancer-related deaths worldwide. Over the last few years, there have been huge amounts of data about diagnosis and treatment of cancer, which is generated from the developments of the biomedical technologies and approaches. The opportunities from the big data in healthcare open a new window to improve clinical diagnoses or therapeutics, but there are many challenges in efficient analysis and interpretation of such big and complex data. For instance, how to manage, extract, analyze, integrate, visualize, and communicate the hidden information from the myriad of data representation of cancer evolved into one of the greatest challenges in next-generation biomedicine. Thus, there is a need to fundamentally address all the above-mentioned issues in big data in cancer healthcare.

BioMed Research International seeks original manuscripts for a Special Issue on the theme: Analysis and Modeling for Big Data in Cancer Research, scheduled to appear in an issue of 2017.

Potential topics include but are not limited to the following:

  • Big data analytics for genomics, transcriptomics, and metabolomics in cancer research
  • New computational methods such as machine learning, scalability/parallelization, map-reduce paradigm, and network analytics, used for big data analysis within cancer
  • Analysis of the next-generation sequencing (NGS) and big data (including gene expression profile) for cancer research
  • Analysis of the spectroscopy/mass spectrometry mass of macromolecule
  • Big data in Chinese medicine
  • Standardized data collection to build prediction models in oncology
  • Big data for cancer signaling pathway study
  • Application of big data for cancer biomarker discovery and validation
  • Big data for cancer drug discovery

Articles

  • Special Issue
  • - Volume 2017
  • - Article ID 1972097
  • - Editorial

Analysis and Modeling for Big Data in Cancer Research

Bing Niu | Peter B. Harrington | ... | Simon Poon
  • Special Issue
  • - Volume 2017
  • - Article ID 4649191
  • - Research Article

2D-QSAR and 3D-QSAR Analyses for EGFR Inhibitors

Manman Zhao | Lin Wang | ... | Bing Niu
  • Special Issue
  • - Volume 2017
  • - Article ID 1645619
  • - Research Article

A Cancer Gene Selection Algorithm Based on the K-S Test and CFS

Qiang Su | Yina Wang | ... | Wen-cong Lu
  • Special Issue
  • - Volume 2017
  • - Article ID 4751260
  • - Research Article

Curcumin Analogue CA15 Exhibits Anticancer Effects on HEp-2 Cells via Targeting NF-κB

Jian Chen | Linlin Zhang | ... | Wulan Li
  • Special Issue
  • - Volume 2017
  • - Article ID 3923865
  • - Research Article

Prediction of Radix Astragali Immunomodulatory Effect of CD80 Expression from Chromatograms by Quantitative Pattern-Activity Relationship

Michelle Chun-har Ng | Tsui-yan Lau | ... | Daniel Man-Yuen Sze
  • Special Issue
  • - Volume 2017
  • - Article ID 7653101
  • - Research Article

Prediction and Analysis of Key Genes in Glioblastoma Based on Bioinformatics

Hao Long | Chaofeng Liang | ... | Ye Song
  • Special Issue
  • - Volume 2016
  • - Article ID 4596326
  • - Research Article

Random Subspace Aggregation for Cancer Prediction with Gene Expression Profiles

Liying Yang | Zhimin Liu | ... | Junying Zhang
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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