计算机科学
机器学习
判别式
人工智能
贝叶斯概率
推论
仿形(计算机编程)
数据挖掘
概率逻辑
生物标志物发现
启发式
贝叶斯定理
贝叶斯网络
贝叶斯推理
基于规则的系统
规则归纳法
操作系统
基因
生物化学
化学
蛋白质组学
作者
Vanathi Gopalakrishnan,Jonathan L. Lustgarten,Shyam Visweswaran,Gregory F. Cooper
出处
期刊:Bioinformatics
[Oxford University Press]
日期:2010-01-14
卷期号:26 (5): 668-675
被引量:39
标识
DOI:10.1093/bioinformatics/btq005
摘要
Abstract Motivation: Disease state prediction from biomarker profiling studies is an important problem because more accurate classification models will potentially lead to the discovery of better, more discriminative markers. Data mining methods are routinely applied to such analyses of biomedical datasets generated from high-throughput ‘omic’ technologies applied to clinical samples from tissues or bodily fluids. Past work has demonstrated that rule models can be successfully applied to this problem, since they can produce understandable models that facilitate review of discriminative biomarkers by biomedical scientists. While many rule-based methods produce rules that make predictions under uncertainty, they typically do not quantify the uncertainty in the validity of the rule itself. This article describes an approach that uses a Bayesian score to evaluate rule models. Results: We have combined the expressiveness of rules with the mathematical rigor of Bayesian networks (BNs) to develop and evaluate a Bayesian rule learning (BRL) system. This system utilizes a novel variant of the K2 algorithm for building BNs from the training data to provide probabilistic scores for IF-antecedent-THEN-consequent rules using heuristic best-first search. We then apply rule-based inference to evaluate the learned models during 10-fold cross-validation performed two times. The BRL system is evaluated on 24 published ‘omic’ datasets, and on average it performs on par or better than other readily available rule learning methods. Moreover, BRL produces models that contain on average 70% fewer variables, which means that the biomarker panels for disease prediction contain fewer markers for further verification and validation by bench scientists. Contact: vanathi@pitt.edu Supplementary information: Supplementary data are available at Bioinformatics online.
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