File organization for database design
File organization for database design
Towards a theory of emergent functionality
Proceedings of the first international conference on simulation of adaptive behavior on From animals to animats
Introduction to artificial neural systems
Introduction to artificial neural systems
Optimal control drug scheduling of cancer chemotherapy
Automatica (Journal of IFAC)
Emergent cooperative goal-satisfaction in large-scale automated-agent systems
Artificial Intelligence
Classification characteristics of SOM and ART2
SAC '94 Proceedings of the 1994 ACM symposium on Applied computing
Comparison of SOM point densities based on different criteria
Neural Computation
Pattern Classification: Neuro-Fuzzy Methods and Their Comparison
Pattern Classification: Neuro-Fuzzy Methods and Their Comparison
A Modified Fuzzy ART for Image Segmentation
ICIAP '01 Proceedings of the 11th International Conference on Image Analysis and Processing
Large-scale data exploration with the hierarchically growing hyperbolic SOM
Neural Networks - 2006 Special issue: Advances in self-organizing maps--WSOM'05
Challenges of biological realism and validation in simulation-based medical education
Artificial Intelligence in Medicine
Mathematical and Computer Modelling: An International Journal
Theoretical aspects of mapping to multidimensional optimal regions as a multi-classifier
Intelligent Data Analysis
| Hi-index | 0.98 |
Although Multiscale Cancer Modeling has a realistic view in the process of tumor growth, its numerical algorithm is time consuming. Therefore, it is problematic to run and to find the best treatment plan for chemotherapy, even in case of a small size of tissue. Using an artificial neural network, this paper simulates the multiscale cancer model faster than its numerical algorithm. In order to find the best treatment plan, it suggests applying a simpler avascular model called Gompertz. By using these proposed methods, multiscale cancer modeling may be extendable to chemotherapy for a realistic size of tissue. In order to simulate multiscale model, a hierarchical neural network called Nested Hierarchical Self Organizing Map (NHSOM) is used. The basis of the NHSOM is an enhanced version of SOM, with an adaptive vigilance parameter. Corresponding parameter and the overall bottom-up design guarantee the quality of clustering, and the embedded top-down architecture reduces computational complexity. Although by applying NHSOM, the process of simulation runs faster compared with that of the numerical algorithm, it is not possible to check a simple search space. As a result, a set containing the best treatment plans of a simpler model (Gompertz) is used. Additionally, it is assumed in this paper, that the distribution of drug in vessels has a linear relation with the blood flow rate. The technical advantage of this assumption is that by using a simple linear relation, a given diffusion of a drug dosage may be scaled to the desired one. By extracting a proper feature vector from the multiscale model and using NHSOM, applying the scaled-best treatment plans of Gompertz model is done for a small size of tissue. In addition, simulating the effect of stress reduction on normal tissue after chemotherapy is another advantage of using NHSOM, which is a kind of ''emergent''.