A Decision Tree Based Classifier to Analyze Human Ovarian Cancer cDNA Microarray Datasets

Tsai, Meng‐Hsiun; Wang, Hsin-Chieh; Lee, Guan-Wei; Lin, Ying-Zu; Chiu, Sheng-Hsiung

doi:10.1007/s10916-015-0361-9

Cited by 16 publications

(5 citation statements)

References 27 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…[ 58 59 ] Furthermore, this method is widely proposed in predicting cancer, predicting specific target of peptides, and analyzing microarray dataset. [ 60 61 62 63 ]…”

Section: Methodsmentioning

confidence: 99%

An optimized framework for cancer prediction using immunosignature

et al. 2018

View full text Add to dashboard Cite

Background:Cancer is a complex disease which can engages the immune system of the patient. In this regard, determination of distinct immunosignatures for various cancers has received increasing interest recently. However, prediction accuracy and reproducibility of the computational methods are limited. In this article, we introduce a robust method for predicting eight types of cancers including astrocytoma, breast cancer, multiple myeloma, lung cancer, oligodendroglia, ovarian cancer, advanced pancreatic cancer, and Ewing sarcoma.Methods:In the proposed scheme, at first, the database is normalized with a dictionary of normalization methods that are combined with particle swarm optimization (PSO) for selecting the best normalization method for each feature. Then, statistical feature selection methods are used to separate discriminative features and they were further improved by PSO with appropriate weights as the inputs of the classification system. Finally, the support vector machines, decision tree, and multilayer perceptron neural network were used as classifiers.Results:The performance of the hybrid predictor was assessed using the holdout method. According to this method, the minimum sensitivity, specificity, precision, and accuracy of the proposed algorithm were 92.4 ± 1.1, 99.1 ± 1.1, 90.6 ± 2.1, and 98.3 ± 1.0, respectively, among the three types of classification that are used in our algorithm.Conclusion:The proposed algorithm considers all the circumstances and works with each feature in its special way. Thus, the proposed algorithm can be used as a promising framework for cancer prediction with immunosignature.

show abstract

“…[ 58 59 ] Furthermore, this method is widely proposed in predicting cancer, predicting specific target of peptides, and analyzing microarray dataset. [ 60 61 62 63 ]…”

Section: Methodsmentioning

confidence: 99%

An optimized framework for cancer prediction using immunosignature

et al. 2018

View full text Add to dashboard Cite

show abstract

“…Naive Bayes predictive machine model works on the principles of conditional probability to predict the class label. Naive Bayes has been used for ovarian cancer classification using features such as micro array gene expression data or clinical variables [17]. Ensemble predictive machine learning works by deriving a new classifier from a set of base classifiers that works better than the constituent base classifiers.…”

Section: International Journal On Recent and Innovation Trends In Com...mentioning

confidence: 99%

Explainable Artificial Intelligence based Ensemble Machine Learning for Ovarian Cancer Stratification using Electronic Health Records

Aelgani,

Vadlakonda

2023

IJRITCC

View full text Add to dashboard Cite

The purpose of this study is to show how ensemble learning-driven machine learning algorithms outperform individual machine learning algorithms at predicting ovarian cancer on a biomarker dataset. Additionally, this study provides model explanations using explainable Artificial Intelligence methods, The method involved gathering and combining 49 risk factors from 349 patients. We hypothesize that ensemble machine learning systems are superior to individual Machine Learning systems in predicting ovarian cancer. The Machine Learning system consists of five individual Machine Learning and five ensemble Machine Learning systems were trained using K-10 cross validation protocols. These training models were then used to predict the development of benign ovarian tumors and ovarian cancer tumors patients. The AUC and Accuracy metrics for ensemble machine learning increased by 19% and 16%. The MCC and Kappa scores for ensemble Machine Learning also increased over individual machine learning by 29% and 33%, respectively. As a result, we draw the conclusion that ensembled-based algorithms outperform individual machine learning in terms of ovarian carcinoma prediction.

show abstract

“…After the oligodeoxynucleotide-based microarray screening assay, the resultant gene lists were used with a decision-tree-based classifier method to analyze the microarray data (26). The selected genes were used to confirm the relationship with BCC cells.…”

Section: Ingenuity Pathway Analysis ® (Ipa)mentioning

confidence: 99%

EGR‑1 plays a protective role in AMPK inhibitor compound C‑induced apoptosis through ROS‑induced ERK activation in skin cancer cells

et al. 2021

Self Cite

View full text Add to dashboard Cite

Skin cancer is caused by abnormal proliferation, gene regulation and mutation of epidermis cells. Compound C is commonly used as an inhibitor of AMP-activated protein kinase (AMPK), which serves as an energy sensor in cells. Recently, compound C has been reported to induce apoptotic and autophagic death in various skin cancer cell lines via an AMPK-independent pathway. However, the signaling pathways activated in compound C-treated cancer cells remain unclear. The present oligodeoxynucleotide-based microarray screening assay showed that the mRNA expression of the zinc-finger transcription factor early growth response-1 (EGR-1), which helps regulate cell cycle progression and cell survival, was significantly upregulated in compound C-treated skin cancer cells. Compound C was demonstrated to induce EGR-1 mRNA and protein expression in a time and dose-dependent manner. Confocal imaging showed that compound C-induced EGR-1 protein expression was localized in the nucleus. Compound C was demonstrated to activate extracellular signal-regulated kinase (ERK) phosphorylation. Inhibition of this compound C-induced ERK phosphorylation downregulated the mRNA and protein expression of EGR-1. In addition, removal of compound C-induced reactive oxygen species (ROS) not only decreased ERK phosphorylation, but also inhibited compound C-induced EGR-1 expression. A functional assay showed that knock down of EGR-1 expression in cancer cells decreased the survival rate while also increasing caspase-3 activity and apoptotic marker expression after compound C treatment. However, no difference in autophagy marker light chain 3-II protein expression was observed between compound C-treated control cells and EGR-1-knockdown cells. Thus, it was concluded that that EGR-1 may antagonize compound C-induced apoptosis but not compound C-induced autophagy through the ROS-mediated ERK activation pathway.

show abstract

A Decision Tree Based Classifier to Analyze Human Ovarian Cancer cDNA Microarray Datasets

Cited by 16 publications

References 27 publications

An optimized framework for cancer prediction using immunosignature

An optimized framework for cancer prediction using immunosignature

Explainable Artificial Intelligence based Ensemble Machine Learning for Ovarian Cancer Stratification using Electronic Health Records

EGR‑1 plays a protective role in AMPK inhibitor compound C‑induced apoptosis through ROS‑induced ERK activation in skin cancer cells

Contact Info

Product

Resources

About