基于最大值加权隔离核映射的近似贝叶斯计算
Approximate Bayesian Computation Based on Maxima Weighted Isolation Kernel Mapping
AI总结：
本研究提出基于最大值加权隔离核映射的近似贝叶斯计算方法，用于精确评估分支过程模型参数，并通过多维数据和癌症细胞进化实例验证其有效性。
摘要：
动机：分支过程模型产生的数据集在随机分布上不均匀，包含稀疏和密集区域。本研究旨在解决精确评估此类模型参数的问题。将分支过程模型应用于癌症细胞进化等领域面临诸多障碍，包括高维性和感兴趣结果出现的稀有性。我们承担了一项艰巨任务，即基于个体变异等位基因频率数据，获取反映驱动基因突变与癌症标志物关系的模型系数。结果：我们开发了一种基于隔离核的近似贝叶斯计算方法。该方法涉及将原始数据转换到希尔伯特空间（映射），并测量模拟点与观测点相关的最大值加权隔离核映射之间的相似性。我们还设计了一种无需计算且与维度无关的参数估计启发式算法。通过多维测试数据以及聚焦于癌症细胞进化的具体实例，展示了所提出的机器学习方法的优势。
英文摘要：
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.
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