Abstract:Hepatocellular carcinoma (HCC) is one of the leading causes of cancer deaths worldwide. Recently, microRNAs (miRNAs) are reported to be altered and act as potential biomarkers in various cancers. However, miRNA biomarkers for predicting the stage of HCC are limitedly discovered. Hence, we sought to identify a novel miRNA signature associated with cancer stage in HCC. We proposed a support vector machine (SVM)-based cancer stage prediction method, SVM-HCC, which uses an inheritable bi-objective combinatorial ge… Show more
“…Various machine learning methods have been developed for cancer typing and diagnosis, including subtyping of liver cancer [ 24 ], breast cancer [ 25 ], lung cancer [ 26 ], and ovarian cancer [ 27 ]. These studies have used different machine learning models, such as support vector machines (SVMs) and artificial neural networks, along with various validation methods.…”
“…Various machine learning methods have been developed for cancer typing and diagnosis, including subtyping of liver cancer [ 24 ], breast cancer [ 25 ], lung cancer [ 26 ], and ovarian cancer [ 27 ]. These studies have used different machine learning models, such as support vector machines (SVMs) and artificial neural networks, along with various validation methods.…”
“…In this study, we showed an efficient strategy to identify miRNA signatures that can stratify high risk and low risk patients as well as classify early and late tumor stages. Several computational and machine leering algorithms have been developed to explore miRNA-associated diseases [ 17 , 29 ]. Previous studies also used miRNA profiles to identify biomarkers for risk stratification [ 11 , 30 ]; however, only a few research works have been conducted to explore miRNA signatures for early tumor stage of ccRCC.…”
Section: Discussionmentioning
confidence: 99%
“…Ng and Taguchi employed the tensor decomposition method to identify miRNA signature in ccRCC [ 15 ]. Previously, studies were used miRNA expression profiles of liver and breast cancer patients, followed by a support vector machine (SVM) with genetic algorithm, to predict the early and advanced stages [ 16 , 17 ]. Recently, miRNA profiles were used to detect lung cancer subtypes [ 18 ].…”
Background
Clear cell renal cell carcinoma (ccRCC) is the most common subtype of renal carcinoma and patients at advanced stage showed poor survival rate. Despite microRNAs (miRNAs) are used as potential biomarkers in many cancers, miRNA biomarkers for predicting the tumor stage of ccRCC are still limitedly identified. Therefore, we proposed a new integrated machine learning (ML) strategy to identify a novel miRNA signature related to tumor stage and prognosis of ccRCC patients using miRNA expression profiles. A multivariate Cox regression model with three hybrid penalties including Least absolute shrinkage and selection operator (Lasso), Adaptive lasso and Elastic net algorithms was used to screen relevant prognostic related miRNAs. The best subset regression (BSR) model was used to identify optimal prognostic model. Five ML algorithms were used to develop stage classification models. The biological significance of the miRNA signature was analyzed by utilizing DIANA-mirPath.
Results
A four-miRNA signature associated with survival was identified and the expression of this signature was strongly correlated with high risk patients. The high risk patients had unfavorable overall survival compared with the low risk group (HR = 4.523, P-value = 2.86e−08). Univariate and multivariate analyses confirmed independent and translational value of this predictive model. A combined ML algorithm identified six miRNA signatures for cancer staging prediction. After using the data balancing algorithm SMOTE, the Support Vector Machine (SVM) algorithm achieved the best classification performance (accuracy = 0.923, sensitivity = 0.927, specificity = 0.919, MCC = 0.843) when compared with other classifiers. Furthermore, enrichment analysis indicated that the identified miRNA signature involved in cancer-associated pathways.
Conclusions
A novel miRNA classification model using the identified prognostic and tumor stage associated miRNA signature will be useful for risk and stage stratification for clinical practice, and the identified miRNA signature can provide promising insight to understand the progression mechanism of ccRCC.
“…The IBCGA uses an intelligent evolutionary algorithm ( Ho et al, 2004a ), which is good at deriving an optimized SVM with feature selection. The IBCGA has been successfully applied in solving several biological problems ( Yerukala Sathipati et al, 2016 , 2019 ; Yerukala Sathipati and Ho, 2017 , 2018 , 2020 , 2021 ).…”
Autism spectrum disorder (ASD) refers to a wide spectrum of neurodevelopmental disorders that emerge during infancy and continue throughout a lifespan. Although substantial efforts have been made to develop therapeutic approaches, core symptoms persist lifelong in ASD patients. Identifying the brain temporospatial regions where the risk genes are expressed in ASD patients may help to improve the therapeutic strategies. Accordingly, this work aims to predict the risk genes of ASD and identify the temporospatial regions of the brain structures at different developmental time points for exploring the specificity of ASD gene expression in the brain that would help in possible ASD detection in the future. A dataset consisting of 13 developmental stages ranging from 8 weeks post-conception to 8 years from 26 brain structures was retrieved from the BrainSpan atlas. This work proposes a support vector machine–based risk gene prediction method ASD-Risk to distinguish the risk genes of ASD and non-ASD genes. ASD-Risk used an optimal feature selection algorithm called inheritable bi-objective combinatorial genetic algorithm to identify the brain temporospatial regions for prediction of the risk genes of ASD. ASD-Risk achieved a 10-fold cross-validation accuracy, sensitivity, specificity, area under a receiver operating characteristic curve, and a test accuracy of 81.83%, 0.84, 0.79, 0.84, and 72.27%, respectively. We prioritized the temporospatial features according to their contribution to the prediction accuracy. The top identified temporospatial regions of the brain for risk gene prediction included the posteroventral parietal cortex at 13 post-conception weeks feature. The identified temporospatial features would help to explore the risk genes that are specifically expressed in different brain regions of ASD patients.
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