Research on Drug Response Prediction Model Based on Big Data

2021 ◽  
pp. 524-537
Author(s):  
Guijin Li ◽  
Minzhu Xie
Keyword(s):  
Big Data ◽  
Prediction Model ◽  
Drug Response ◽  
Response Prediction ◽  
Model Based

scholarly journals Establishment and Evaluation of Immune Checkpoint Inhibitor Treatment Response Model Based on Gene Expression Value

2021 ◽  
Author(s):  
Xiaoyan Li ◽  
Guoyin Li ◽  
Xiyang Tang ◽  
Yongsheng Zhou ◽  
Kaifu Zheng ◽  
...  
Keyword(s):  
Prediction Model ◽  
Treatment Response ◽  
Immune Checkpoint ◽  
Predictive Ability ◽  
Response Prediction ◽  
Immune Checkpoints ◽  
Model Based ◽  
Checkpoint Genes ◽  
Mutant Genes ◽  
Response Score

Abstract Background: In the past 10 years, the identification of new mutant genes involved in the pathogenesis of melanoma and the discovery of key immune checkpoints have promoted the development of targeted therapy and immunotherapy. There is no doubt that an important breakthrough has been made in the treatment of advanced or metastatic melanoma. However, the treatment of melanoma also faces many challenges. In addition to resistance to existing targeted therapies or immunotherapy, most patients do not respond to immunotherapy or have serious adverse reactions. At present, the value of existing biomarkers to predict treatment response and toxicity is still limited. Therefore, there is an urgent need to establish a convenient and reliable immunotherapy response prediction model in order to preliminarily clarify the population benefiting from immunotherapy.Results: We established a predictive model based on the expression values of five genes for patients with melanoma with an anti-PD1 immunotherapy response score. This model showed better predictive ability compared with other common immunotherapy predictors. Differences were found in the number of immune cells and the expression of common immune checkpoint genes between the high- and low-score groups. The model played a pivotal role in predicting renal cell carcinoma anti-PD1 immunotherapy response. Conclusions: The anti-PD1 immunotherapy response score prediction model for patients with melanoma showed good predictive power, thus having far-reaching significance for identifying people who benefited from anti-PD1 immunotherapy and reducing the potential toxicity of insensitive patients.

scholarly journals Radiation Response Prediction Model based on Integrated Clinical and Genomic Data Analysis

2021 ◽  
Author(s):  
Bum-Sup Jang ◽  
Ji-Hyun Chang ◽  
Seung Hyuck Jeon ◽  
Myung Geun Song ◽  
Kyung-Hun Lee ◽  
...  
Keyword(s):  
Data Analysis ◽  
Prediction Model ◽  
Genomic Data ◽  
Response Prediction ◽  
Radiation Response ◽  
Model Based ◽  
Genomic Data Analysis

scholarly journals Developing an Agricultural Product Price Prediction Model using HADT Algorithm

2019 ◽  
Vol 9 (1S4) ◽  
pp. 569-575
Keyword(s):  
Data Mining ◽  
Big Data ◽  
Decision Tree ◽  
Prediction Model ◽  
Predictive Analytics ◽  
Agricultural Product ◽  
Agricultural Crop ◽  
Product Price ◽  
Price Prediction ◽  
Model Based

Big Data Predictive Analytics and Data mining are emerging recent research field to analyse the agricultural crop price. The applications and techniques of data mining as well as Big Data using agriculture data is considered in this paper. In particular, the farmers are more concern about estimating that how much profit they are about to expect for the chosen crop. As with many other sectors the amount of agriculture data are increasing on a daily source. In this work, agriculture crop price dataset of Virudhunagar District, Tamilnadu, India is considered and for the price prediction model based on data mining decision tree techniques. The main goal is to establish the new predictive model based on Hybrid Association rule-based Decision Tree algorithm (HADT). The outcome for the suggested HADT forecast model is heartening and precise to predict agricultural product prices than other current decision tree models.

scholarly journals Enhanced co-expression extrapolation (COXEN) gene selection method for building anticancer drug response prediction models.

2020 ◽  
Vol 38 (15_suppl) ◽  
pp. e14073-e14073
Author(s):  
Yitan Zhu ◽  
Thomas S. Brettin ◽  
Fangfang Xia ◽  
Maulik Shukla ◽  
Alexander Partin ◽  
...  
Keyword(s):  
Prediction Model ◽  
Regression Model ◽  
Drug Response ◽  
Prediction Models ◽  
Gene Selection ◽  
Expression Patterns ◽  
Response Prediction ◽  
Precision Oncology ◽  
General Drug ◽  
Multiple Drugs

e14073 Background: Accurate prediction of tumor response to a drug treatment is of paramount importance for precision oncology. The co-expression extrapolation (COXEN) gene selection approach has been successfully used in multiple studies to select genes for predicting the response of tumor cells to a specific drug. Here, we enhance the original COXEN approach to select genes that are predictive of the efficacies of multiple drugs simultaneously for building general drug response prediction model. Methods: We implemented two methods to select predictive genes. The first method ranks the genes according to their prediction power for each individual drug and then takes a union of top predictive genes of all the drugs. The second method uses a linear regression model to evaluate the prediction power of a gene for all drugs while the drugs are one-hot encoded in the regression model. Among the predictive genes, we further select genes by evaluating the preservation of co-expression patterns between cancer cases with drug response data available and cancer cases for which drug response needs to be predicted, because the preservation of co-expression patterns indicates the similarity of genomic regulations between cancer cases. Results: To test the enhanced COXEN method, we used a lightGBM regression model to predict drug response based on the selected genes on two benchmark in vitro drug screening datasets. The table below compares the performance of prediction models built based on 200 genes selected by the enhanced COXEN method to that of models built on 200 genes randomly picked from the LINCS gene set, which includes 976 “landmark” genes well-representing cellular transcriptomic changes identified in the Library of Integrated Network-Based Cellular Signatures (LINCS) project. The enhanced COXEN approach selects genes better than random LINCS genes as demonstrated by the increased average coefficient of determination (R2) for predicting the area under the dose response curve through cross-validation. Pair-wise t-test indicates the improvement is statistically significant (p-value ≤ 0.05) on both datasets. Conclusions: Our result demonstrates the benefit of using an enhanced COXEN approach to select genes for building general drug response prediction model. [Table: see text]

Energy big data demand prediction model based on fuzzy rough set

2020 ◽  
Vol 39 (4) ◽  
pp. 5291-5300
Author(s):  
Zhimei Duan ◽  
Xiaojin Yuan ◽  
Rongfei Zhu
Keyword(s):  
Big Data ◽  
Prediction Model ◽  
Rough Set ◽  
Energy Demand ◽  
Energy Structure ◽  
Attribute Importance ◽  
Fuzzy Rough Set ◽  
Demand Prediction ◽  
Model Based ◽  
Short Period

Energy is an indispensable material resource for human production and life. It is a powerful engine and an important guarantee for human survival, economic and social sustainable development and world change. The economy is developing rapidly, the demand for energy continues to grow, energy consumption has increased sharply in a short period, and the security of energy supply and demand has also shown a severe trend. Predicting energy demand is especially important. However, due to the many influencing factors and the lack of energy data, the energy demand prediction has great uncertainty in the prediction results. Because of the above problems, this paper proposes an energy big data demand prediction model based on a fuzzy rough set model. Firstly, according to the data, the factors affecting the energy demand are determined, and the fuzzy C-means clustering algorithm is used to discretize the data according to the characteristics of the fuzzy rough set. Then the decision table is established and the attribute importance is calculated, and then the neighborhood rough set is used for attribute reduction. Then extract the correlation rules to establish a prediction model. Compare the prediction model proposed in this paper with the existing gray prediction method and energy elasticity coefficient method. The results show that this method can more scientifically predict the changes in energy big data demand. Finally, based on the experimental results, the corresponding strategies for optimizing the energy structure are proposed to provide reference for the optimization and development of energy demand.

scholarly journals Drug response prediction model using a hierarchical structural component modeling method

2018 ◽  
Vol 19 (S9) ◽  
Author(s):  
Sungtae Kim ◽  
Sungkyoung Choi ◽  
Jung-Hwan Yoon ◽  
Youngsoo Kim ◽  
Seungyeoun Lee ◽  
...  
Keyword(s):  
Prediction Model ◽  
Structural Component ◽  
Drug Response ◽  
Response Prediction ◽  
Modeling Method
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