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首页> 外文期刊>Journal of Bioinformatics and Computational Biology >MINING DISEASE STATE CONVERTERS FOR MEDICAL INTERVENTION OF DISEASES
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MINING DISEASE STATE CONVERTERS FOR MEDICAL INTERVENTION OF DISEASES

机译：采矿疾病状态转换器用于疾病的医学干预

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In applications such as gene therapy and drug design, a key goal is to convert thendisease state of diseased objects from an undesirable state into a desirable one. Suchnconversions may be achieved by changing the values of some attributes of the objects. Fornexample, in gene therapy one may convert cancerous cells to normal ones by changingnsome genes’ expression level from low to high or from high to low. In this paper, wendefine the disease state conversion problem as the discovery of disease state converters;na disease state converter is a small set of attribute value changes that may change annobject’s disease state from undesirable into desirable. We consider two variants of thisnproblem: personalized disease state converter mining mines disease state converters forna given individual patient with a given disease, and universal disease state converternmining mines disease state converters for all samples with a given disease. We proposena DSCMiner algorithm to discover small and highly effective disease state converters.nSince real-life medical experiments on living diseased instances are expensive and timenconsuming, we use classifiers trained from the datasets of given diseases to evaluatenthe quality of discovered converter sets. The effectiveness of a disease state converter isnmeasured by the percentage of objects that are successfully converted from undesirablenstate into desirable state as deemed by state-of-the-art classifiers. We use experimentsnto evaluate the effectiveness of our algorithm and to show its effectiveness. We alsondiscuss possible research directions for extensions and improvements. We note that thendisease state conversion problem also has applications in customer retention, criminalnrehabilitation, and company turn-around, where the goal is to convert class membershipnof objects whose class is an undesirable class
机译：在诸如基因疗法和药物设计的应用中，关键目标是将患病物体的病态从不期望的状态转变成期望的状态。可以通过更改对象某些属性的值来实现这种转换。例如，在基因疗法中，可以通过将某些基因的表达水平从低变高或从高变低来将癌细胞转化为正常细胞。在本文中，将疾病状态转换问题定义为疾病状态转换器的发现；疾病状态转换器是一小组属性值更改，可以将对象的疾病状态从不希望的状态更改为希望的状态。我们考虑此问题的两个变体：针对特定疾病的个体患者的个性化疾病状态转换器挖掘矿山疾病状态转换器，针对具有特定疾病的所有样本，通用疾病状态转换器挖掘矿山疾病状态转换器。我们提出了一种DSCMiner算法来发现小型高效的疾病状态转换器。n由于在活的患病实例上进行的现实医学实验既昂贵又费时，因此我们使用从给定疾病数据集中训练的分类器来评估发现的转换器集的质量。疾病状态转换器的有效性是由最新分类器认为成功地从不良状态转换为理想状态的对象的百分比来衡量的。我们使用实验来评估算法的有效性并显示其有效性。我们还讨论了可能的研究方向，以进行扩展和改进。我们注意到，疾病状态转换问题在客户保留，犯罪康复和公司周转方面也有应用，其目标是转换类别为不良类别的对象的类别成员资格

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