About Computational Cancer Systems Biology
About this Collection
This collection is now closed to submissions.
The use of large-scale omics or imaging-based screens has highlighted significant heterogeneity in the response of both patients and cell lines to anti-cancer therapies. While genetic factors have traditionally been considered the primary cause of treatment resistance and its heterogeneity, recent studies have indicated that other factors, including cancer cell plasticity, microenvironment interactions, and spatial organization, also play crucial roles. Consequently, computational methods that integrate data from multiple sources have become essential for stratifying patient risk, predicting drug sensitivity, and developing combination treatment strategies.
Given the complexities of these challenges, this collection seeks to encourage submissions of articles (namely software tools, methods and research articles) that present novel computational methods and software packages. These methods or tools should be capable of simulating or predicting the evolution, spatial organization, and drug response of cancer cells and interactions between them. The methods could involve models operating at the level of populations of cells or intracellular processes such as signaling, transcriptional regulation, or metabolomic processes. Both machine learning and mathematical modeling approaches or a combination thereof, are welcome.
Keywords: Cancer; plasticity; evolution; drug-resistance; combination therapy; micro-environment; machine learning; mathematical modeling; computational biology; systems biology; network biology
Submission deadline: 30th November 2023
This collection is part of the Bioinformatics, Oncology and Artificial Intelligence and Machine Learning Gateways.
Any questions about this collection? Please get in contact directly with research@f1000.com.
The use of large-scale omics or imaging-based screens has highlighted significant heterogeneity in the response of both patients and cell lines to anti-cancer therapies. While genetic factors have traditionally been considered the primary cause of treatment resistance and its heterogeneity, recent studies have indicated that other factors, including cancer cell plasticity, microenvironment interactions, and spatial organization, also play crucial roles. Consequently, computational methods that integrate data from multiple sources have become essential for stratifying patient risk, predicting drug sensitivity, and developing combination treatment strategies.
Given the complexities of these challenges, this collection seeks to encourage submissions of articles (namely software tools, methods and research articles) that present novel computational methods and software packages. These methods or tools should be capable of simulating or predicting the evolution, spatial organization, and drug response of cancer cells and interactions between them. The methods could involve models operating at the level of populations of cells or intracellular processes such as signaling, transcriptional regulation, or metabolomic processes. Both machine learning and mathematical modeling approaches or a combination thereof, are welcome.
Keywords: Cancer; plasticity; evolution; drug-resistance; combination therapy; micro-environment; machine learning; mathematical modeling; computational biology; systems biology; network biology
Submission deadline: 30th November 2023
This collection is part of the Bioinformatics, Oncology and Artificial Intelligence and Machine Learning Gateways.
Any questions about this collection? Please get in contact directly with research@f1000.com.
Collection Advisor
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Fabian Froehlich
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