alexa Data Mining Methods for OMICs Applications in Anticancer Drug Design and Discovery

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Data Mining Methods for OMICs Applications in Anticancer Drug Design and Discovery

Cancer forces a serious burden on the public health system and has created a challenge to the medical science researchers. Though the century-long drift of cancer mortality in the world was reversed in the middle of 19th century and now cancer remains the second leading cause of death. Annually, more than 10 million new case of cancer are diagnosed based on the World Health Organization (WHO) report [1]. By 2020, the world population is expected to have risen to 7.5 billion; of this number, around 15 million new cancer cases will be diagnosed, and 12 million cancer patients will die. The pointed undesirable data demonstrate that cancer is described as a serious challenge in human healthcare and survival. Although we have witnessed the development of many drugs against cancer, the death rate for the most prevalent cancers has not decreased [2]. The design of novel drugs to treat cancer is an extended and hard process with a very high level of abrasion. Many steps in this lengthy procedure use data generated from various molecular studies and chemical species. One key challenge is to successfully translate the basic findings of target validation further into safety studies in clinical trial stage.

Citation: Sardari S (2012) Data Mining Methods for ‘OMICs’ Applications in Anticancer Drug Design and Discovery. Drug Design 1:e102. doi: 10.4172/2169-0138.1000e102

 
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