arXiv subjects

Weikaixin Kong

Publications and source records attributed to Weikaixin Kong.

3 recordsLinked to original sources

arXiv2020

Prediction and optimization of NaV1.7 inhibitors based on machine learning methods

We used machine learning methods to predict NaV1.7 inhibitors and found the model RF-CDK that performed best on the imbalanced dataset. Using the RF-CDK model for screening drugs, we got effective compounds K1. We use the cell patch clamp method to verify K1. However, because the model evaluation method in this article is not comprehensive enough, there is still a lot of research work to be performed, such as comparison with other existing methods. The target protein has multiple active sites and requires our further research. We need more detailed models to consider this biological process and compare it with the current results, which is an error in this article. So we want to withdraw this article.

q-bio.QM↗

arXiv2019

Identification of key genes related to the mechanism and prognosis of lung squamous cell carcinoma using bioinformatics analysis

Objectives Lung squamous cell carcinoma (LUSC) often diagnosed as advanced with poor prognosis. The mechanisms of its pathogenesis and prognosis require urgent elucidation. This study was performed to screen potential biomarkers related to the occurrence, development and prognosis of LUSC to reveal unknown physiological and pathological processes. Materials and Methods Using bioinformatics analysis, the lung squamous cell carcinoma microarray datasets from the GEO and TCGA databases were analyzed to identify differentially expressed genes(DEGs). Furthermore, PPI and WGCNA network analysis were integrated to identify the key genes closely related to the process of LUSC development. In addition, survival analysis was performed to achieve a prognostic model that accomplished a high level of prediction accuracy. Results and Conclusion Eighty-five up-regulated and 39 down-regulated genes were identified, on which functional and pathway enrichment analysis was conducted. GO analysis demonstrated that up-regulated genes were principally enriched in epidermal development and DNA unwinding in DNA replication. Down-regulated genes were mainly involved in cell adhesion, signal transduction and positive regulation of inflammatory response. After PPI and WGCNA network analysis, eight genes, including AURKA, RAD51, TTK, AURKB, CCNA2, TPX2, KPNA2 and KIF23, have been found to play a vital role in LUSC development. The prognostic model contained 20 genes, 18 of which were detrimental to prognosis. The AUC of the established prognostic model for predicting the survival of patients at 1, 3, and 5 years was 0.828, 0.826 and 0.824, respectively. To conclude, this study identified a number of biomarkers of significant interest for additional investigation of the therapies and methods of prognosis of lung squamous cell carcinoma.

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arXiv2019

Prediction of 5-hydroxytryptamine Transporter Inhibitor based on Machine Learning

In patients with depression, the use of 5-HT reuptake inhibitors can improve the condition. Topological fingerprints, ECFP4, and molecular descriptors were used. Some SERT and small molecules combined prediction models were established by using 5 machine learning methods. We selected the higher accuracy models(RF, SVM, LR) in five-fold cross-validation of training set to establish an integrated model (VOL_CLF). The training set is from Chembl database and oversampled by SMOTE algorithm to eliminate data imbalance. The unbalanced data from same sources (Chembl) was used as Test set 1; the unbalanced data with different sources(Drugbank) was used as Test set 2 . The prediction accuracy of SERT inhibitors in Test set 1 was 90.7%~93.3%(VOL_CLF method was the highest); the inhibitory recall rate was 84.6%-90.1%(RF method was the highest); the non-inhibitor prediction accuracy rate was 76.1%~80.2%(RF method is the highest); the non-inhibitor predictive recall rate is 81.2%~87.5% (SVM and VOL_CLF methods were the highest) The RF model in Test Set 2 performed better than the other models. The SERT inhibitor predicted accuracy rate, recall rate, non-inhibitor predicted accuracy rate, recall rate were 42.9%, 85.7%, 95.7%, 73.3%.This study demonstrates that machine learning methods effectively predict inhibitors of serotonin transporters and accelerate drug screening.

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