scholarly journals IHRW: An Improved Hypergraph Random Walk Model for Predicting Three-Drug Therapy

2021 ◽  
Author(s):  
Qi Wang ◽  
Guiying Yan
Keyword(s):  
Random Walk ◽  
Drug Therapy ◽  
Computational Models ◽  
Complex Disease ◽  
Drug Combinations ◽  
Random Walk Model ◽  
Disease Treatment ◽  
Drug Candidates ◽  
The Cost ◽  
Therapy Prediction

AbstractDrug combination therapy is a well-established concept in the treatment of complex diseases due to its fewer side effects, lower toxicity, and better efficacy. However, it is challenging to identify efficacious drug combinations from many drug candidates. Computational models could greatly reduce the cost, but most models did not use data for more than two-drug combinations and could not predict three-drug therapy. However, three-drug combinations account for about 21% of the known combinations, which is a very important type of treatment. Here, we utilized higher-order information and developed an improved hypergraph random walk model (IHRW) for three-drug therapy prediction. This is the first method to explore the combination of three drugs.As a result, the case studies of breast cancer, lung cancer, and colon cancer showed that IHRW had a powerful ability to predict potential efficacious three-drug combinations, which provides new prospects for complex disease treatment. The code of IHRW is freely available at https://github.com/wangqi27/IHRW.

scholarly journals HRWR: Predicting Potential Efficacious Drug Combination Based on Hypergraph Random Walk with Restart

2020 ◽  
Author(s):  
Qi Wang ◽  
Guiying Yan
Keyword(s):  
Random Walk ◽  
Drug Combination ◽  
Computational Models ◽  
Operating Characteristic ◽  
Characteristic Curve ◽  
Random Walk With Restart ◽  
Cancer Breast ◽  
Leave One Out ◽  
The Cost ◽  
Efficacious Drug

AbstractSome studies have shown that efficacious drug combination can increase the therapeutic effect, and decrease drug toxicity and side-effects. Thus, drug combinations have been widely used in the treatment of complex diseases, especially cancer. However, experiment-based methods are extremely costly in time and money. Computational models can greatly reduce the cost, but most of the models do not use the data of more than two drugs and lose a lot of useful information. Here, we used high-order drug combination information and developed a hypergraph random walk with restart model (HRWR) for efficacious drug combination prediction.As a result, compared with the other methods by leave-one-out cross-validation (LOOCV), the Area Under Receiver Operating Characteristic Curve (AUROC) of the HRWR algorithm were higher than others. Moreover, the case studies of lung cancer, breast cancer, and colorectal cancer showed that HRWR had a powerful ability to predict potential efficacious combinations, which provides new prospects for cancer treatment. The code and dataset of HRWR are freely available at https://github.com/wangqi27/HRWR.

scholarly journals Exploratory Compatibility Regularity of Traditional Chinese Medicine on Osteoarthritis Treatment: A Data Mining and Random Walk-Based Identification

2021 ◽  
Vol 2021 ◽  
pp. 1-12
Author(s):  
Qiao Zhou ◽  
Jian Liu ◽  
Ling Xin ◽  
Yanyan Fang ◽  
Lei Wan ◽  
...  
Keyword(s):  
Data Mining ◽  
Random Walk ◽  
Chinese Medicine ◽  
Traditional Chinese Medicine ◽  
Propensity Score ◽  
Similarity Measure ◽  
Drug Combinations ◽  
Random Walk Model ◽  
Propensity Score Methods ◽  
Clinical Records

Osteoarthritis (OA) is a degressive and complex disease which is a growing public health problem on a global scale. On basis of an in-house database consisting of clinical records of 13,083 OA patients, the Traditional Chinese Medicine (TCM) was divided into 4 categories of medicines on the basis of the curative properties of herbs. Due to the lack of depth and internal relationship in the calculation results of TCM compatibility law data mining methods such as statistics and frequency analysis, we use a variety of multidimensional complex network methods that can efficaciously find the compatibility law of TCM, including similarity measure, graphical visualization of network diagram, random walking, and propensity score methods. We summarize common couplet medicines utilized for the treatment of osteoarthritis. The similarity measure method was used to investigate the commonly used drugs for the treatment of osteoarthritis. The method of association rule analysis is used to recognize the compatibility between the components. On basis of the propensity score methods, the evaluation displayed that, compared with single drug, the drug group increased ESR, CRP, C3, C4, IgG, and IgA more efficiently. Concluding, a random walk model was constructed to assess drug efficacy. After applying a random walk model, while revealing the compatibility among different components of TCM, their therapeutic efficacy against OA is analyzed. We obtained four groups of drug combination clusters by similarity measure and 11 pairs of highly connected drugs by association rules, which are cardinal drug combinations in the prescription for the treatment of OA. We also found that different traditional drug pairs were associated with different laboratory indexes, and drug combinations could better optimize laboratory indexes. This study presented that the TCM constituents complement one another. Besides, the therapeutic effects resulting from a variety of combinations of these constituents are quite different.

A Multi-Label Classification Algorithm Based on Random Walk Model

2010 ◽  
Vol 33 (8) ◽  
pp. 1418-1426 ◽  
Author(s):  
Wei ZHENG ◽  
Chao-Kun WANG ◽  
Zhang LIU ◽  
Jian-Min WANG
Keyword(s):  
Random Walk ◽  
Classification Algorithm ◽  
Random Walk Model

Quartile histogram assessment of glioma malignancy using high b ‐value diffusion MRI with a continuous‐time random‐walk model

2021 ◽  
Vol 34 (4) ◽  
Author(s):  
M. Muge Karaman ◽  
Jiaxuan Zhang ◽  
Karen L. Xie ◽  
Wenzhen Zhu ◽  
Xiaohong Joe Zhou
Keyword(s):  
Random Walk ◽  
Continuous Time ◽  
Diffusion Mri ◽  
B Value ◽  
Continuous Time Random Walk ◽  
Random Walk Model ◽  
High B Value

The prediction model of personality in social networks by using data mining deep learning algorithm and random walk model

2020 ◽  
pp. 002072092093683
Author(s):  
Yu Zhu
Keyword(s):  
Data Mining ◽  
Deep Learning ◽  
Social Network ◽  
Random Walk ◽  
Prediction Method ◽  
Random Walk Model ◽  
Personality Characteristics ◽  
User Personality ◽  
Network Platform ◽  
Personality Prediction

The objective is to predict and analyze the behaviors of users in the social network platform by using the personality theory and computational technologies, thereby acquiring the personality characteristics of social network users more effectively. First, social network data are analyzed, which finds that the type of text data marks the majority. By using data mining technology, the raw data of numerous social network users can be obtained. Based on the random walk model, the data information of the text status of social network users is analyzed, and a user personality prediction method integrating multi-label learning is proposed. In addition, the online social network platform Weibo is taken as the research object. The blog information of Weibo users is obtained through crawler technology. Then, the users are labeled in accordance with personality characteristics. The Pearson correlation coefficient is used to evaluate the relation between the user personality characteristics and the user behavior characteristics of the Weibo users. The correlation between the network behaviors and personality characteristics of Weibo users is analyzed, and the scientificity of the prediction method is verified by the Big Five Model of Personality. By applying relevant technologies and algorithms of data mining and deep learning, the learning ability of neural networks on data characteristics can be improved. In terms of performance on analyzing text information of social network users, the user personality prediction method of integrated multi-label learning based on the random walk model has a large advantage. For the problem of personality prediction of social network users, through combining data mining technology and deep neural network technology in deep learning, the data processing results of social network user behaviors are more accurate.

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