About this Research Topic
This Research Topic is the second volume in the series:Using Physical & Genomics Markers for Smart Therapy via Expert Systems With Computer Learning. The previous volume can be viewed here:
Using Physical & Genomics Markers for Smart Therapy via Expert Systems With Computer Learning
The purpose of Evidence Based Medicine (EBM) is to integrate the experience of clinicians, patients, and the best available scientific information to guide decisions about clinical management. On the other hand, Big data is a very hot topic in recent years, and it is also a long-term trend of future works. A large amount of information and a diversified appearance can be quickly analyzed by big data to generate more valuable information. The biomedical community makes important decisions and applications. For precision medicine, the analysis of medicine, genomics, genetics and other issues through the big data of whole-evidence can be integrated and analyzed in clinical and genetic fields to help understand the relationship between diseases and related genes and the information generated after analysis in order to promote the developing speed for new drugs, and can provide the future direction of biomedicine.
Currently, the big data of whole-evidence used to analyze the genes of different diseases and compare the data will also have a more precise direction for drug treatment and avoid the current situation of drug abuse. Furthermore, the whole evidence of big data can analyze the relationship between drug metabolism and genes. Individual diseases caused by family inheritance may be further understood in this analysis. Future research data analysis of personalized medicine can provide more assistance to the biotechnology industry. In this issue, we look forward to collecting latest articles about the application of computer learning by using whole-evidence with various physical and genomics markers for smart therapy.
Subtopics of interest include, but are not limited to, the following:
• Combinatorial analysis of whole-evidence for personalized medicine
• New or improved tools for the analysis of smart therapy
• Bioinformatics models, methods and algorithms in expert systems
• Big data in genomics markers: analytics, machine learning methods and datasets
• Development and validation of computational learning methods to predict
• Identification of novel drug targets
• Novel computational learning models on clinical decisions
• Data mining and genomics markers knowledge discovery
• Computational and statistical models for cancer data analysis
• Text mining in medical record
Using Physical & Genomics Markers for Smart Therapy via Expert Systems With Computer Learning
The purpose of Evidence Based Medicine (EBM) is to integrate the experience of clinicians, patients, and the best available scientific information to guide decisions about clinical management. On the other hand, Big data is a very hot topic in recent years, and it is also a long-term trend of future works. A large amount of information and a diversified appearance can be quickly analyzed by big data to generate more valuable information. The biomedical community makes important decisions and applications. For precision medicine, the analysis of medicine, genomics, genetics and other issues through the big data of whole-evidence can be integrated and analyzed in clinical and genetic fields to help understand the relationship between diseases and related genes and the information generated after analysis in order to promote the developing speed for new drugs, and can provide the future direction of biomedicine.
Currently, the big data of whole-evidence used to analyze the genes of different diseases and compare the data will also have a more precise direction for drug treatment and avoid the current situation of drug abuse. Furthermore, the whole evidence of big data can analyze the relationship between drug metabolism and genes. Individual diseases caused by family inheritance may be further understood in this analysis. Future research data analysis of personalized medicine can provide more assistance to the biotechnology industry. In this issue, we look forward to collecting latest articles about the application of computer learning by using whole-evidence with various physical and genomics markers for smart therapy.
Subtopics of interest include, but are not limited to, the following:
• Combinatorial analysis of whole-evidence for personalized medicine
• New or improved tools for the analysis of smart therapy
• Bioinformatics models, methods and algorithms in expert systems
• Big data in genomics markers: analytics, machine learning methods and datasets
• Development and validation of computational learning methods to predict
• Identification of novel drug targets
• Novel computational learning models on clinical decisions
• Data mining and genomics markers knowledge discovery
• Computational and statistical models for cancer data analysis
• Text mining in medical record
Keywords: Predictive modeling, Cancer Biomarkers, Machine Learning, Whole-evidence, Cancer stem cells
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