Cancer research in the field of Computational Systems Biology attempts to address questions that will advance current knowledge in the mechanisms of cancer progression or treatment resistance. By analyzing multi-omics data and developing a predictive mathematical and/ or computational model of an unknown biological system, we can systematically understand (a) the mechanisms that tie altered gene expression and downstream molecular mechanisms to functional cancer phenotypes, (b) and/or the mechanisms that tie tumor morphology to functional cancer phenotypes, (c) and/or the mechanisms that tie treatment sequence and combination to evolving functional cancer phenotypes. Currently, systems biology still faces some challenges, including model calibration, model validation and generalization, computational efficiency, and the feasibility of clinical transition. Recent developments in artificial intelligence technologies, e.g. deep learning (DL), allow us to model the hierarchical structure of real biological systems, efficiently converting gene-level data to pathway-level information with an ultimate impact on cell phenotype. Furthermore, such computational models could require fewer training samples, are more generalizable across diverse biological contexts, and can make predictions that are more consistent with the current understanding on the inner-workings of biological systems.
This Research Topic intends to provide an international forum for researchers to exchange up-to-date outcomes on AI and DL to address the concerns in systems biology of cancer. This collection will focus on (but is not limited to) the following topics:
1) AI solutions on the inference of intracellular/intercellular pathway network of cancer cells.
2) AI solutions on the simulation or optimization of 3D cancer progression and immune response.
3) AI solutions on the new strategies for large-scale parameter estimation of computational models.
4) AI solutions on the new optimal strategies for the calibration of computational models.
5) Deep learning for the segmentation, quantification and annotation of cancer imaging data.
6) AI solutions on the cancer progression modelling and prediction by using untimed transcriptome.
7) AI-enhanced approaches for cancer evolution trajectory by using single cell RNA-seq data.
8) New intelligent methods for patient classification or diagnosis based on multi-level clinical data.
Cancer research in the field of Computational Systems Biology attempts to address questions that will advance current knowledge in the mechanisms of cancer progression or treatment resistance. By analyzing multi-omics data and developing a predictive mathematical and/ or computational model of an unknown biological system, we can systematically understand (a) the mechanisms that tie altered gene expression and downstream molecular mechanisms to functional cancer phenotypes, (b) and/or the mechanisms that tie tumor morphology to functional cancer phenotypes, (c) and/or the mechanisms that tie treatment sequence and combination to evolving functional cancer phenotypes. Currently, systems biology still faces some challenges, including model calibration, model validation and generalization, computational efficiency, and the feasibility of clinical transition. Recent developments in artificial intelligence technologies, e.g. deep learning (DL), allow us to model the hierarchical structure of real biological systems, efficiently converting gene-level data to pathway-level information with an ultimate impact on cell phenotype. Furthermore, such computational models could require fewer training samples, are more generalizable across diverse biological contexts, and can make predictions that are more consistent with the current understanding on the inner-workings of biological systems.
This Research Topic intends to provide an international forum for researchers to exchange up-to-date outcomes on AI and DL to address the concerns in systems biology of cancer. This collection will focus on (but is not limited to) the following topics:
1) AI solutions on the inference of intracellular/intercellular pathway network of cancer cells.
2) AI solutions on the simulation or optimization of 3D cancer progression and immune response.
3) AI solutions on the new strategies for large-scale parameter estimation of computational models.
4) AI solutions on the new optimal strategies for the calibration of computational models.
5) Deep learning for the segmentation, quantification and annotation of cancer imaging data.
6) AI solutions on the cancer progression modelling and prediction by using untimed transcriptome.
7) AI-enhanced approaches for cancer evolution trajectory by using single cell RNA-seq data.
8) New intelligent methods for patient classification or diagnosis based on multi-level clinical data.