Cancer is the second leading cause of death worldwide. Since age is one of the risk factors for the development of cancer, the incidence rates for cancers rise as age increases. It is estimated that metastasis is responsible for about 90% of cancer-related deaths. Metastatic colonization of distal organs can occur months to decades after initial diagnosis and treatment. Therefore, a proactive approach to detecting cancer at an early stage can make treatments more effective, with fewer side effects and improved long-term survival.
In recent years, omics approaches (e.g., genomics, transcriptomics, metabolomics, proteomics) have yielded great advances in cancer research and have provided in-depth insights into the processes involved in cancer development and progression. These novel experimental methods have allowed the generation and accumulation of a huge amount of both cancer- and aging-related data. Moreover, practical use of the information contained within this huge amount of data requires computational approaches such as bioinformatics, machine learning and artificial intelligence (AI). Therefore, these computational methods together with omics data from large databases can now be used to develop cancer biomarkers, novel anti-cancer drug targets, and both novel and repurposed treatment options for cancer.
The aim of this Research Topic is to provide a detailed overview of recent progress in the application of different computational methods in cancer research. The second goal is to combine aging-related data and cancer data to understand impact of age on cancer molecular landscape, cancer progression and prognosis. Finally, the goal is to provide the novel discovery of potential cancer drug targets, biomarkers, or therapeutic interventions.
We welcome submissions of Original Research Articles, Reviews, Mini-Reviews, Methods, Protocols, Perspectives, and Technology Reports that address, but are not limited to, the following topics:
• Application of bioinformatics methods to early diagnosis of cancer
• Application of AI methods (e.g., reinforcement learning and generative adversarial networks) to develop de novo cancer drugs
• Using transcriptomics data (or other omics-data) to find drugs that can be repurposed as anti-cancer drugs
• Using computational methods to find novel drug targets for cancer
• Using computational methods to discriminate between early and late stages of cancer
• Using aging-related and cancer omics data to understand impact of age on cancer progression and prognosis
Please note that original research articles utilizing bioinformatic tools should provide some validation for their results.
Cancer is the second leading cause of death worldwide. Since age is one of the risk factors for the development of cancer, the incidence rates for cancers rise as age increases. It is estimated that metastasis is responsible for about 90% of cancer-related deaths. Metastatic colonization of distal organs can occur months to decades after initial diagnosis and treatment. Therefore, a proactive approach to detecting cancer at an early stage can make treatments more effective, with fewer side effects and improved long-term survival.
In recent years, omics approaches (e.g., genomics, transcriptomics, metabolomics, proteomics) have yielded great advances in cancer research and have provided in-depth insights into the processes involved in cancer development and progression. These novel experimental methods have allowed the generation and accumulation of a huge amount of both cancer- and aging-related data. Moreover, practical use of the information contained within this huge amount of data requires computational approaches such as bioinformatics, machine learning and artificial intelligence (AI). Therefore, these computational methods together with omics data from large databases can now be used to develop cancer biomarkers, novel anti-cancer drug targets, and both novel and repurposed treatment options for cancer.
The aim of this Research Topic is to provide a detailed overview of recent progress in the application of different computational methods in cancer research. The second goal is to combine aging-related data and cancer data to understand impact of age on cancer molecular landscape, cancer progression and prognosis. Finally, the goal is to provide the novel discovery of potential cancer drug targets, biomarkers, or therapeutic interventions.
We welcome submissions of Original Research Articles, Reviews, Mini-Reviews, Methods, Protocols, Perspectives, and Technology Reports that address, but are not limited to, the following topics:
• Application of bioinformatics methods to early diagnosis of cancer
• Application of AI methods (e.g., reinforcement learning and generative adversarial networks) to develop de novo cancer drugs
• Using transcriptomics data (or other omics-data) to find drugs that can be repurposed as anti-cancer drugs
• Using computational methods to find novel drug targets for cancer
• Using computational methods to discriminate between early and late stages of cancer
• Using aging-related and cancer omics data to understand impact of age on cancer progression and prognosis
Please note that original research articles utilizing bioinformatic tools should provide some validation for their results.