A Convolutional Neural Network System to Discriminate Drug-Target Interactions

Hu, Shanshan; Xia, DeNan; Su, Benyue; Zhang, Youzhi; Wang, Bing; Li, Jinyan

doi:10.1109/tcbb.2019.2940187

Cited by 17 publications

(14 citation statements)

References 38 publications

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“…Protein-protein interaction (PPI) is the main way to realize the regulation of biological information, and it is an important factor to determine the fate of cells [1][2][3]. The study of protein-protein interactions is the basis for understanding life activities and one of the most important topics in the post-genome era.…”

Section: Introductionmentioning

confidence: 99%

Developing Computational Model to Predict Protein-Protein Interaction Sites Based on the XGBoost Algorithm

Deng

Zhang

Wang

et al. 2020

IJMS

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The study of protein-protein interaction is of great biological significance, and the prediction of protein-protein interaction sites can promote the understanding of cell biological activity and will be helpful for drug development. However, uneven distribution between interaction and non-interaction sites is common because only a small number of protein interactions have been confirmed by experimental techniques, which greatly affects the predictive capability of computational methods. In this work, two imbalanced data processing strategies based on XGBoost algorithm were proposed to re-balance the original dataset from inherent relationship between positive and negative samples for the prediction of protein-protein interaction sites. Herein, a feature extraction method was applied to represent the protein interaction sites based on evolutionary conservatism of proteins, and the influence of overlapping regions of positive and negative samples was considered in prediction performance. Our method showed good prediction performance, such as prediction accuracy of 0.807 and MCC of 0.614, on an original dataset with 10,455 surface residues but only 2297 interface residues. Experimental results demonstrated the effectiveness of our XGBoost-based method.

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Section: Introductionmentioning

confidence: 99%

Developing Computational Model to Predict Protein-Protein Interaction Sites Based on the XGBoost Algorithm

Deng

Zhang

Wang

et al. 2020

IJMS

Self Cite

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“…The external validation dataset is also used to prove the effectiveness of the proposed method. Distance-based sampling for negative DTIs was first used in Hu et al, whose training sets were composed of reference [ 1 ] and manually collected datasets [ 35 ]. The replication of the algorithm of Hu et al is difficult without their original dataset.…”

Section: Resultsmentioning

confidence: 99%

“…The commonly used classification models, such as support vector machines (SVM), random forest, random walk with restart, and decision trees can be found both in similarity-based and feature vector-based algorithms [ 8 , 14 , 15 , 32 , 33 , 34 ]. With the expansion of data scale, deep learning is widely used in DTIs [ 11 , 35 ].…”

Section: Introductionmentioning

confidence: 99%

“…Farshid et al proposed FRnet-Encode to extract 4096 features, and FRnet-Predict to predict drug-target interactions based on deep convolutional neural network [ 11 ]. Hu et al introduced a convolutional neural network system to discriminate drug-target interactions [ 35 ]. A large number of hyper-parameters adjustment is the key factor restricting the application of deep learning.…”

Section: Introductionmentioning

confidence: 99%

“…The gold standard datasets introduced by Yamanishi et al is used for training and testing in this work. To solve the imbalance problem of datasets, we calculated the Euclidean distance between unlabeled drug-target pairs and positive ones, which is called distance-based sampling for negative DTIs [ 35 ]. At last, SVM is used for classification with less parameter adjustment [ 33 , 44 ].…”

Section: Introductionmentioning

confidence: 99%

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Predicting Drug-Target Interactions with Electrotopological State Fingerprints and Amphiphilic Pseudo Amino Acid Composition

Wang

Lü

et al. 2020

IJMS

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The task of drug-target interaction (DTI) prediction plays important roles in drug development. The experimental methods in DTIs are time-consuming, expensive and challenging. To solve these problems, machine learning-based methods are introduced, which are restricted by effective feature extraction and negative sampling. In this work, features with electrotopological state (E-state) fingerprints for drugs and amphiphilic pseudo amino acid composition (APAAC) for target proteins are tested. E-state fingerprints are extracted based on both molecular electronic and topological features with the same metric. APAAC is an extension of amino acid composition (AAC), which is calculated based on hydrophilic and hydrophobic characters to construct sequence order information. Using the combination of these feature pairs, the prediction model is established by support vector machines. In order to enhance the effectiveness of features, a distance-based negative sampling is proposed to obtain reliable negative samples. It is shown that the prediction results of area under curve for Receiver Operating Characteristic (AUC) are above 98.5% for all the three datasets in this work. The comparison of state-of-the-art methods demonstrates the effectiveness and efficiency of proposed method, which will be helpful for further drug development.

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