scholarly journals Drug-Target Interaction Prediction Based on Multisource Information Weighted Fusion

2021 ◽  
Vol 2021 ◽  
pp. 1-10
Author(s):  
Shuaiqi Liu ◽  
Jingjie An ◽  
Jie Zhao ◽  
Shuhuan Zhao ◽  
Hui Lv ◽  
...  
Keyword(s):  
Drug Target ◽  
Operating Characteristic ◽  
State Of The Art ◽  
Characteristic Curve ◽  
Relationship Matrix ◽  
Target Interaction ◽  
Interaction Prediction ◽  
Weighted Fusion ◽  
Precision Recall Curve ◽  
The Relationship

Recently, in most existing studies, it is assumed that there are no interaction relationships between drugs and targets with unknown interactions. However, unknown interactions mean the relationships between drugs and targets have just not been confirmed. In this paper, samples for which the relationship between drugs and targets has not been determined are considered unlabeled. A weighted fusion method of multisource information is proposed to screen drug-target interactions. Firstly, some drug-target pairs which may have interactions are selected. Secondly, the selected drug-target pairs are added to the positive samples, which are regarded as known to have interaction relationships, and the original interaction relationship matrix is revised. Finally, the revised datasets are used to predict the interaction derived from the bipartite local model with neighbor-based interaction profile inferring (BLM-NII). Experiments demonstrate that the proposed method has greatly improved specificity, sensitivity, precision, and accuracy compared with the BLM-NII method. In addition, compared with several state-of-the-art methods, the area under the receiver operating characteristic curve (AUC) and the area under the precision-recall curve (AUPR) of the proposed method are excellent.

scholarly journals DeepPurpose: a deep learning library for drug–target interaction prediction

2020 ◽  
Author(s):  
Kexin Huang ◽  
Tianfan Fu ◽  
Lucas M Glass ◽  
Marinka Zitnik ◽  
Cao Xiao ◽  
...  
Keyword(s):  
Deep Learning ◽  
Drug Target ◽  
Prediction Models ◽  
State Of The Art ◽  
Supplementary Information ◽  
Target Interaction ◽  
Interaction Prediction ◽  
Computer Scientists ◽  
Benchmark Datasets ◽  
Biomedical Field

Abstract Summary Accurate prediction of drug–target interactions (DTI) is crucial for drug discovery. Recently, deep learning (DL) models for show promising performance for DTI prediction. However, these models can be difficult to use for both computer scientists entering the biomedical field and bioinformaticians with limited DL experience. We present DeepPurpose, a comprehensive and easy-to-use DL library for DTI prediction. DeepPurpose supports training of customized DTI prediction models by implementing 15 compound and protein encoders and over 50 neural architectures, along with providing many other useful features. We demonstrate state-of-the-art performance of DeepPurpose on several benchmark datasets. Availability and implementation https://github.com/kexinhuang12345/DeepPurpose. Contact [email protected] Supplementary information Supplementary data are available at Bioinformatics online.

scholarly journals Cascade Deep Forest With Heterogeneous Similarity Measures for Drug–Target Interaction Prediction

2021 ◽  
Vol 12 ◽  
Author(s):  
Ying Zheng ◽  
Zheng Wu
Keyword(s):  
Drug Target ◽  
Drug Repositioning ◽  
Similarity Measures ◽  
Data Sets ◽  
Target Interaction ◽  
Interaction Prediction ◽  
Deep Forest ◽  
Network Properties ◽  
Precision Recall Curve ◽  
Fold Cross Validation

Drug repositioning is a method of systematically identifying potential molecular targets that known drugs may act on. Compared with traditional methods, drug repositioning has been extensively studied due to the development of multi-omics technology and system biology methods. Because of its biological network properties, it is possible to apply machine learning related algorithms for prediction. Based on various heterogeneous network model, this paper proposes a method named THNCDF for predicting drug–target interactions. Various heterogeneous networks are integrated to build a tripartite network, and similarity calculation methods are used to obtain similarity matrix. Then, the cascade deep forest method is used to make prediction. Results indicate that THNCDF outperforms the previously reported methods based on the 10-fold cross-validation on the benchmark data sets proposed by Y. Yamanishi. The area under Precision Recall curve (AUPR) value on the Enzyme, GPCR, Ion Channel, and Nuclear Receptor data sets is 0.988, 0.980, 0.938, and 0.906 separately. The experimental results well illustrate the feasibility of this method.

The Computational Models of Drug-target Interaction Prediction

2020 ◽  
Vol 27 (5) ◽  
pp. 348-358 ◽  
Author(s):  
Yijie Ding ◽  
Jijun Tang ◽  
Fei Guo
Keyword(s):  
Machine Learning ◽  
Computational Methods ◽  
Drug Target ◽  
Computational Models ◽  
Data Sets ◽  
Target Interaction ◽  
Interaction Prediction ◽  
The Past ◽  
Benchmark Data ◽  
Precision Recall Curve

:The identification of Drug-Target Interactions (DTIs) is an important process in drug discovery and medical research. However, the tradition experimental methods for DTIs identification are still time consuming, extremely expensive and challenging. In the past ten years, various computational methods have been developed to identify potential DTIs. In this paper, the identification methods of DTIs are summarized. What's more, several state-of-the-art computational methods are mainly introduced, containing network-based method and machine learning-based method. In particular, for machine learning-based methods, including the supervised and semisupervised models, have essential differences in the approach of negative samples. Although these effective computational models in identification of DTIs have achieved significant improvements, network-based and machine learning-based methods have their disadvantages, respectively. These computational methods are evaluated on four benchmark data sets via values of Area Under the Precision Recall curve (AUPR).

Drug-Target Interaction Prediction with Graph Regularized Matrix Factorization

2017 ◽  
Vol 14 (3) ◽  
pp. 646-656 ◽  
Author(s):  
Ali Ezzat ◽  
Peilin Zhao ◽  
Min Wu ◽  
Xiao-Li Li ◽  
Chee-Keong Kwoh
Keyword(s):  
Matrix Factorization ◽  
Drug Target ◽  
Target Interaction ◽  
Interaction Prediction

Can Computers Outperform Humans in Detecting User Zone-Outs? Implications for Intelligent Interfaces

2022 ◽  
Vol 29 (2) ◽  
pp. 1-33
Author(s):  
Nigel Bosch ◽  
Sidney K. D'Mello
Keyword(s):  
Operating Characteristic ◽  
State Of The Art ◽  
Characteristic Curve ◽  
Ground Truth ◽  
Mind Wandering ◽  
High Stakes ◽  
Machine Learning Model ◽  
Improve Accuracy ◽  
Comparable Accuracy ◽  
Operating Characteristic Curve

The ability to identify whether a user is “zoning out” (mind wandering) from video has many HCI (e.g., distance learning, high-stakes vigilance tasks). However, it remains unknown how well humans can perform this task, how they compare to automatic computerized approaches, and how a fusion of the two might improve accuracy. We analyzed videos of users’ faces and upper bodies recorded 10s prior to self-reported mind wandering (i.e., ground truth) while they engaged in a computerized reading task. We found that a state-of-the-art machine learning model had comparable accuracy to aggregated judgments of nine untrained human observers (area under receiver operating characteristic curve [AUC] = .598 versus .589). A fusion of the two (AUC = .644) outperformed each, presumably because each focused on complementary cues. Furthermore, adding more humans beyond 3–4 observers yielded diminishing returns. We discuss implications of human–computer fusion as a means to improve accuracy in complex tasks.

scholarly journals The serum homocysteine level in patients with acute ischemic stroke (AIS) after thrombolysis and its relationship with clinical outcomes

2018 ◽  
Vol 64 (5) ◽  
pp. 438-442 ◽  
Author(s):  
Ling-Cong ◽  
Hong-Zhao ◽  
Yu-Wang ◽  
Yu-Li ◽  
Xin-Sui
Keyword(s):  
Clinical Outcomes ◽  
Operating Characteristic ◽  
Characteristic Curve ◽  
Thrombolytic Treatment ◽  
Neurological Outcomes ◽  
High Level Group ◽  
Serum Homocysteine ◽  
High Level ◽  
The Relationship ◽  
Level Group

SUMMARY OBJECTIVE The present study aims to investigate whether hyperhomocysteinemia (HHcy) affects the outcomes of the thrombolytic treatment for patients with AIS. METHODS A sample of 120 AIS patients were recruited and grouped according to their serum homocysteine (Hcy) levels. The National Institute of Health Stroke Scale (NIHSS) was obtained before treatment and 7 days after it to evaluate neurological outcomes; modified Rankin Scale (mRS) was obtained 12 weeks later to assess functional outcomes. Receiver operating characteristic curve (ROC) was used to demonstrate the relationship between serum Hcy level and the outcomes after tPA treatment. RESULTS The serum Hcy level of 120 patients was of 27.57±20.17μmol/L. The NIHSS scores of the patients in the low Hcy level group were remarkably lower compared to those in the high-level group (p<0.05), after 7 days of treatment. In addition, the mRS scores of the patients in the low Hcy level group, after 12 weeks, were remarkably lower compared to those in the high-level group (p<0.01). ROC demonstrated that the serum Hcy level is related to the clinical outcomes of thrombolytic treatment with moderate specificity (80.3%) and sensitivity (58.2%). CONCLUSION In conclusion, higher serum Hcy levels can indicate poorer clinical outcomes of thrombolytic treatment in patients with AIS.

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