scholarly journals Diagnostic Value of Plasma MicroRNAs for Lung Cancer Using Support Vector Machine Model

2019 ◽  
Vol 10 (21) ◽  
pp. 5090-5098 ◽  
Author(s):  
Wei Wang ◽  
Mingcui Ding ◽  
Xiaoran Duan ◽  
Xiaolei Feng ◽  
Pengpeng Wang ◽  
...  
Keyword(s):  
Lung Cancer ◽  
Support Vector Machine ◽  
Support Vector Machine Model ◽  
Diagnostic Value ◽  
Support Vector ◽  
Machine Model

scholarly journals Establishment of a Lung Cancer Discriminative Model Based on an Optimized Support Vector Machine Algorithm and Study of Key Targets of Wogonin in Lung Cancer

2021 ◽  
Vol 12 ◽  
Author(s):  
Lin Wang ◽  
Jianhua Zhang ◽  
Guoyong Shan ◽  
Junting Liang ◽  
Wenwen Jin ◽  
...  
Keyword(s):  
Lung Cancer ◽  
Support Vector Machine ◽  
Cancer Cells ◽  
Signaling Pathway ◽  
Regulatory Network ◽  
Support Vector Machine Model ◽  
Susceptibility Genes ◽  
Support Vector ◽  
Machine Model ◽  
Diagnosis Model

An optimized support vector machine model was used to construct a lung cancer diagnosis model based on serological indicators, and a molecular regulation model of Wogonin, a component of Scutellaria baicalensis, was established. Serological indexes of patients were collected, the grid search method was used to identify the optimal penalty coefficient C and parameter g of the support vector machine model, and the benign and malignant auxiliary diagnosis model of isolated pulmonary nodules based on serological indicators was established. The regulatory network and key targets of Wogonin in lung cancer were analyzed by network pharmacology, and key targets were detected by western blot. The relationship between serological susceptibility genes and key targets of Wogonin was established, and the signaling pathway of Wogonin regulating lung cancer was constructed. After support vector machine parameter optimization (C = 90.597, g = 32), the accuracy of the model was 90.8333%, with nine false positives and two false negative cases. Ontology functional analysis of 67 common genes between Wogonin targets and lung cancer–related genes showed that the targets were associated with biological processes involved in peptidye-serine modification and regulation of protein kinase B signaling; cell components in the membrane raft and chromosomal region; and molecular function in protein serine/threonine kinase activity and heme binding. Kyoto Encyclopedia of Genes and Genomes analysis showed that the regulation pathways involved the PI3K-Akt signaling pathway, ERBB signaling pathway, and EGFR tyrosine kinase inhibitor resistance. In vitro analyses using lung cancer cells showed that Wogonin led to significantly increased levels of cleaved caspase-3 and Bad and significantly decreased Bcl-2 expression in a concentration-dependent manner. ErbB4 expression also significantly decreased in lung cancer cells after treatment with Wogonin. A regulatory network of Wogonin regulating lung cancer cell apoptosis was constructed, including the participation of serological susceptibility genes. There is a certain regulatory effect between the serological indexes that can be used in the diagnosis of lung cancer and the key targets of Chinese herbal medicine treatment of lung cancer, which provides a new idea for the diagnosis, treatment and prognosis of clinical lung cancer.

scholarly journals An Improved Whale Algorithm for Support Vector Machine Prediction of Photovoltaic Power Generation

Symmetry ◽  
2021 ◽  
Vol 13 (2) ◽  
pp. 212
Author(s):  
Yu-Wei Liu ◽  
Huan Feng ◽  
Heng-Yi Li ◽  
Ling-Ling Li
Keyword(s):  
Support Vector Machine ◽  
Prediction Model ◽  
Output Power ◽  
Input Data ◽  
Support Vector Machine Model ◽  
Weather Conditions ◽  
Clean Energy ◽  
Support Vector ◽  
Machine Model ◽  
Photovoltaic Power

Accurate prediction of photovoltaic power is conducive to the application of clean energy and sustainable development. An improved whale algorithm is proposed to optimize the Support Vector Machine model. The characteristic of the model is that it needs less training data to symmetrically adapt to the prediction conditions of different weather, and has high prediction accuracy in different weather conditions. This study aims to (1) select light intensity, ambient temperature and relative humidity, which are strictly related to photovoltaic output power as the input data; (2) apply wavelet soft threshold denoising to preprocess input data to reduce the noise contained in input data to symmetrically enhance the adaptability of the prediction model in different weather conditions; (3) improve the whale algorithm by using tent chaotic mapping, nonlinear disturbance and differential evolution algorithm; (4) apply the improved whale algorithm to optimize the Support Vector Machine model in order to improve the prediction accuracy of the prediction model. The experiment proves that the short-term prediction model of photovoltaic power based on symmetry concept achieves ideal accuracy in different weather. The systematic method for output power prediction of renewable energy is conductive to reducing the workload of predicting the output power and to promoting the application of clean energy and sustainable development.

Research on SVM Line Loss Rate Prediction Based on Heuristic Algorithm

2013 ◽  
Vol 291-294 ◽  
pp. 2164-2168 ◽  
Author(s):  
Li Tian ◽  
Qiang Qiang Wang ◽  
An Zhao Cao
Keyword(s):  
Support Vector Machine ◽  
Heuristic Algorithm ◽  
Support Vector Machine Model ◽  
Loss Rate ◽  
Heuristic Algorithms ◽  
Support Vector ◽  
Machine Model ◽  
Rate Prediction ◽  
Line Loss

With the characteristic of line loss volatility, a research of line loss rate prediction was imperatively carried out. Considering the optimization ability of heuristic algorithm and the regression ability of support vector machine, a heuristic algorithm-support vector machine model is constructed. Case study shows that, compared with other heuristic algorithms’, the search efficiency and speed of genetic algorithm are good, and the prediction model is with high accuracy.

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