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首页> 外文会议>IEEE Conference on Computational Intelligence in Bioinformatics and Computational Biology >Drug target interaction predictions using PU- Leaming under different experimental setting for four formulations namely known drug target pair prediction, drug prediction, target prediction and unknown drug target pair prediction
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Drug target interaction predictions using PU- Leaming under different experimental setting for four formulations namely known drug target pair prediction, drug prediction, target prediction and unknown drug target pair prediction

机译：使用PU-Leaming在不同实验设置下针对四种制剂的药物靶相互作用预测，即已知药物靶对预测，药物预测，靶预测和未知药物靶对预测

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Predicting new drug target interactions experimentally through wet lab experiments is time as well as resource intensive. In general, drug-target interaction prediction problem leads to drug discovery, drug repositioning and uncovers interesting patterns in chemogenomics research. Drug and target represent heterogeneous nodes within a network of interactions. Presence of an edge between the nodes indicates a positive interaction whereas an absence suggests an unknown interaction. Classification based machine learning algorithms are heavily applied in this area of research. Classification algorithms need positive as well as negative data to yield optimized results. The major problem in this field is lack of negative data because the data that are found in the public databases are positive interaction samples. Considering unknown drug target pairs as negative data may cause severe consequences for the prediction performance. Thereby, we propose a positive un-labelled (PU) learning- based approach that uses one class support vector machine technique as the learning algorithm. The algorithm learns the positive distribution from the unified feature vector space of drugs and targets and regards unknown pairs as unlabeled instead of labelling them as negative pairs. Additionally, we use 4860 Klekota Roth fingerprint + 881 PubChem fingerprint as a high dimensional and highly discriminative feature vector representation for drugs. To represent protein features, we create a protein-motif matrix based on the sliding window score that records the probability of a motif pattern occurring within a given protein sequence. Also, we separately evaluate the prediction performance using 5-fold nested cross- validation under different experimental setting for each of the four formulations: 1) Known drug-target pair,2) Drug prediction, 3) Target prediction and 4) Unknown drug target pair. We show that our approach yields the best AUC score over previous benchmark techniques and outperforms most of the recent works based on one class classifiers and PU-based learning.
机译：通过湿实验室实验以实验方式预测新药靶标的相互作用既耗时又耗费资源。通常，药物-靶标相互作用的预测问题会导致药物发现，药物重新定位并发现化学基因组学研究中的有趣模式。药物和靶标代表相互作用网络中的异构节点。节点之间的边缘的存在指示相互作用是正的，而节点之间不存在则表明相互作用是未知的。基于分类的机器学习算法在该研究领域中得到了广泛应用。分类算法需要使用正负数据来产生优化结果。该领域的主要问题是缺乏负面数据，因为在公共数据库中找到的数据是正面互动样本。将未知的药物靶标对视为阴性数据可能会对预测性能造成严重后果。因此，我们提出了一种基于积极的未标记（PU）学习的方法，该方法使用一类支持向量机技术作为学习算法。该算法从药物和靶标的统一特征向量空间中学习正向分布，并将未知对视为未标记，而不是将它们标记为负对。此外，我们使用4860 Klekota Roth指纹+ 881 PubChem指纹作为药物的高维和高判别特征向量表示。为了表示蛋白质特征，我们基于滑动窗口得分创建了蛋白质基序矩阵，该矩阵记录了在给定蛋白质序列中出现基序模式的可能性。此外，我们针对四种配方分别在不同的实验设置下使用5倍嵌套交叉验证分别评估了预测效果：1）已知药物-靶标对，2）药物预测，3）靶标预测和4）未知药物靶标一对。我们表明，我们的方法比以前的基准技术获得了最佳的AUC评分，并且优于基于类分类器和基于PU的学习的大多数最新作品。

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