Identification of a 12-Gene Signature for Lung Cancer Prognosis through Machine Learning

Bard, E.; Hu, Wei

doi:10.4236/jct.2011.22017

Cited by 8 publications

(2 citation statements)

References 13 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The capabilities of DNA chips enabled the concurrent observation of expression levels to an enormous amount of genes [8], as well as the rise of computer evaluation approaches such as machine learning. These approaches are very much beneficial for cancer prediction [9,10]. They are also used for prognosis [11] and extracting figures from gene expression data classification models.…”

Section: Introductionmentioning

confidence: 99%

CoySvM-(GeD): Coyote Optimization-Based Support Vector Machine Classifier for Cancer Classification Using Gene Expression Data

Reddy¹,

Kumar²,

Ghafoor

et al. 2022

Journal of Sensors

View full text Add to dashboard Cite

Cancer, by any means, is a significant cause of death worldwide. In the analysis of cancer disease, the classification of different tumor types is very important. This test initiates an attitude to the classification of cancer through the data in gene expression by modeling the support vector machine. Genetic material expression data of individual tumor types is designed by the SVM classifier, which tends to increase the potential of genetic data. Feature selection has long been considered a practical standard since its introduction in the field, and numerous feature selection methods have been used in an effort to reduce the input dimension while enhancing the classification performance. The proposed optimization has pertained to the gene expression data that selects the fusion factors for the hybrid kernel function in the SVM classifier and the genes as informative for cancer classification. The analysis of cancer classification is performed using colon cancer and breast cancer, and the performance of CoySVM is tested by taking the measures as precision, recall, and F-measure, and it achieves 87.598%, 95.669%, and 98.088% for colon cancer in addition to 93.647%, 92.984%, and 95% for breast cancer. It shows the best performance due to its highest classification in selected measures than the conventional methods.

show abstract

Section: Introductionmentioning

confidence: 99%

CoySvM-(GeD): Coyote Optimization-Based Support Vector Machine Classifier for Cancer Classification Using Gene Expression Data

Reddy¹,

Kumar²,

Ghafoor

et al. 2022

Journal of Sensors

View full text Add to dashboard Cite

show abstract

“…In addition to biomarker discovery, classification methods also have been used to predict patient survivability, cancer recurrence, and prognosis [7, 8, 9, 57, 58, 59, 60]. The prediction of patient survivability or cancer recurrence indicates whether an event (e.g., death or recurrence of a disease) will occur within a specific time.…”

Section: Application Of Classification For Cancer Treatment and Prmentioning

confidence: 99%

A Classification Framework Applied to Cancer Gene Expression Profiles

Hijazi

Chan

2000

Journal of Healthcare Engineering

View full text Add to dashboard Cite

Classification of cancer based on gene expression has provided insight into possible treatment strategies. Thus, developing machine learning methods that can successfully distinguish among cancer subtypes or normal versus cancer samples is important. This work discusses supervised learning techniques that have been employed to classify cancers. Furthermore, a two-step feature selection method based on an attribute estimation method (e.g., ReliefF) and a genetic algorithm was employed to find a set of genes that can best differentiate between cancer subtypes or normal versus cancer samples. The application of different classification methods (e.g., decision tree, k-nearest neighbor, support vector machine (SVM), bagging, and random forest) on 5 cancer datasets shows that no classification method universally outperforms all the others. However, k-nearest neighbor and linear SVM generally improve the classification performance over other classifiers. Finally, incorporating diverse types of genomic data (e.g., protein-protein interaction data and gene expression) increase the prediction accuracy as compared to using gene expression alone.

show abstract