Approximate Bayesian Computation Based on Maxima Weighted Isolation Kernel Mapping
This work addresses parameter estimation challenges in cancer cell evolution modeling, but it appears incremental as it builds on existing Isolation Kernel and approximate Bayesian computation techniques.
The authors tackled the problem of precisely evaluating parameters for a branching processes model with unevenly distributed data, such as in cancer cell evolution, by developing an approximate Bayesian computation method based on Isolation Kernel mapping, which they demonstrated on multidimensional test data and a cancer-specific example.
Motivation: A branching processes model yields an unevenly stochastically distributed dataset that consists of sparse and dense regions. This work addresses the problem of precisely evaluating parameters for such a model. Applying a branching processes model to an area such as cancer cell evolution faces a number of obstacles, including high dimensionality and the rare appearance of a result of interest. We take on the ambitious task of obtaining the coefficients of a model that reflects the relationship of driver gene mutations and cancer hallmarks on the basis of personal data regarding variant allele frequencies. Results: An approximate Bayesian computation method based on Isolation Kernel is developed. The method involves the transformation of row data to a Hilbert space (mapping) and the measurement of the similarity between simulated points and maxima weighted Isolation Kernel mapping related to the observation point. We also design a heuristic algorithm for parameter estimation that requires no calculation and is dimension independent. The advantages of the proposed machine learning method are illustrated using multidimensional test data as well as a specific example focused on cancer cell evolution.