Soldatova Larisa N, Rzhetsky Andrey, De Grave Kurt, King Ross D
Department of Information Systems and Computing, Brunel University, London, UK.
J Biomed Semantics. 2013 Apr 15;4 Suppl 1(Suppl 1):S7. doi: 10.1186/2041-1480-4-S1-S7.
The theory of probability is widely used in biomedical research for data analysis and modelling. In previous work the probabilities of the research hypotheses have been recorded as experimental metadata. The ontology HELO is designed to support probabilistic reasoning, and provides semantic descriptors for reporting on research that involves operations with probabilities. HELO explicitly links research statements such as hypotheses, models, laws, conclusions, etc. to the associated probabilities of these statements being true. HELO enables the explicit semantic representation and accurate recording of probabilities in hypotheses, as well as the inference methods used to generate and update those hypotheses. We demonstrate the utility of HELO on three worked examples: changes in the probability of the hypothesis that sirtuins regulate human life span; changes in the probability of hypotheses about gene functions in the S. cerevisiae aromatic amino acid pathway; and the use of active learning in drug design (quantitative structure activity relation learning), where a strategy for the selection of compounds with the highest probability of improving on the best known compound was used. HELO is open source and available at https://github.com/larisa-soldatova/HELO.
概率理论在生物医学研究中的数据分析和建模方面有着广泛应用。在先前的工作中，研究假设的概率已被记录为实验元数据。本体HELO旨在支持概率推理，并为涉及概率运算的研究报告提供语义描述符。HELO明确地将诸如假设、模型、定律、结论等研究陈述与这些陈述为真的相关概率联系起来。HELO能够对假设中的概率进行明确的语义表示和准确记录，以及用于生成和更新这些假设的推理方法。我们通过三个实例来展示HELO的效用：关于沉默调节蛋白调控人类寿命的假设概率变化；酿酒酵母芳香族氨基酸途径中基因功能假设的概率变化；以及在药物设计（定量构效关系学习）中使用主动学习，其中采用了一种选择最有可能改进已知最佳化合物的化合物的策略。HELO是开源的，可在https://github.com/larisa-soldatova/HELO获取。
