About this Research Topic
Cancer is the greatest threat to human life and health it is still an aggressive killer. It is one of the main causes of death in the world and represents one of the most important medical problems of mankind. In the past 10 years, the new synthetic anticancer agents have not been as successful as the current clinical speculation. At the same time, despite the gradual increase in human and financial resources invested in cancer research, the pathogenesis and clinical treatment of cancer still cannot meet the needs.
Exploring the molecular characteristics of cancer is the basis and core of cancer treatment. However, the funds and time required for traditional clinical methods cannot meet the urgent needs of human treatment. With the reduction of the cost of sequencing and various biomolecular tests, the data of cancer-related genomics, transcriptome, proteome, and metabonomics show exponential growth. With the rapid development of high-throughput sequencing, gene editing, immunotherapy, and other technologies, as well as the discovery of key cancer-related genes or pathways, the research on cancer biology at the genetic and genomic levels has become increasingly in-depth.
Medical big data provides a reliable computing and statistical method for researchers to mine key cancer information, but it also poses a major challenge to existing computing methods. In the information age, an in-depth study of statistics and computing methods is the key to accurately mining the biological knowledge contained in the multi-group data. This brings better targeted and personalized healthcare solutions to patients.
Computational and statistical methods to identify cancer-related mechanisms and biomarkers are becoming increasingly popular. These methods put forward different opinions on cancer from basic research and clinical perspectives. Given the above development of computing methods in cancer research, we propose a research topic, which aims to provide researchers with an excellent opportunity to share their latest research results, introduce new methods, and discuss challenges and opportunities in related fields. We suggest that the author not only focus on computer analysis but also provide corresponding experimental verification, to make the entire scientific research achievements more reliable.
The thesis solicited includes but is not limited to the following topics:
Cancer disease prediction based on artificial intelligence
Tools and databases for the study of cancer and related diseases
Discovery of complex disease-related genes, RNA, proteins, and metabolites
Study on anticancer mechanism of drugs based on network pharmacology, molecular docking, and experimental verification
Machine learning and statistical methods for identifying cancer-related molecules
Analysis of the relationship between cancer-related diseases and cancer
Synergistic Effect of natural products and Immunotherapy in cancer treatment
Mechanism of natural compounds targeting cancer stem cells
Identification and validation of cancer molecular biomarkers
Based on network pharmacology, molecular docking, and experimental verification to understand the anti-cancer mechanism of active food ingredients, plant drugs, and marine drugs
Application of experimental and omic research in cancer treatment
Exploring the molecular characteristics of cancer is the basis and core of cancer treatment. However, the funds and time required for traditional clinical methods cannot meet the urgent needs of human treatment. With the reduction of the cost of sequencing and various biomolecular tests, the data of cancer-related genomics, transcriptome, proteome, and metabonomics show exponential growth. With the rapid development of high-throughput sequencing, gene editing, immunotherapy, and other technologies, as well as the discovery of key cancer-related genes or pathways, the research on cancer biology at the genetic and genomic levels has become increasingly in-depth.
Medical big data provides a reliable computing and statistical method for researchers to mine key cancer information, but it also poses a major challenge to existing computing methods. In the information age, an in-depth study of statistics and computing methods is the key to accurately mining the biological knowledge contained in the multi-group data. This brings better targeted and personalized healthcare solutions to patients.
Computational and statistical methods to identify cancer-related mechanisms and biomarkers are becoming increasingly popular. These methods put forward different opinions on cancer from basic research and clinical perspectives. Given the above development of computing methods in cancer research, we propose a research topic, which aims to provide researchers with an excellent opportunity to share their latest research results, introduce new methods, and discuss challenges and opportunities in related fields. We suggest that the author not only focus on computer analysis but also provide corresponding experimental verification, to make the entire scientific research achievements more reliable.
The thesis solicited includes but is not limited to the following topics:
Cancer disease prediction based on artificial intelligence
Tools and databases for the study of cancer and related diseases
Discovery of complex disease-related genes, RNA, proteins, and metabolites
Study on anticancer mechanism of drugs based on network pharmacology, molecular docking, and experimental verification
Machine learning and statistical methods for identifying cancer-related molecules
Analysis of the relationship between cancer-related diseases and cancer
Synergistic Effect of natural products and Immunotherapy in cancer treatment
Mechanism of natural compounds targeting cancer stem cells
Identification and validation of cancer molecular biomarkers
Based on network pharmacology, molecular docking, and experimental verification to understand the anti-cancer mechanism of active food ingredients, plant drugs, and marine drugs
Application of experimental and omic research in cancer treatment
Keywords: Big Data; Cancer; Artificial Intelligence
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