A WCO Based Cancer Survival Prediction Using Statistical Feature Selection

Breast cancer is the most common malignancy in women, and because it has a high mortality rate, it is urgent to develop computational methods to increase the accuracy of breast cancer survival predictive models. Although multi-omics data such as gene expression have been extensively used in recent studies, the accurate prognosis of breast cancer remains a challenge. Somatic mutations are another important and promising data source for studying cancer development, and its effect on the prognosis of breast cancer remains to be further explored. Meanwhile, these omics datasets are high-dimensional and redundant. Therefore, we adopted multiple kernel learning (MKL) to efficiently integrate somatic mutation to currently molecular data including gene expression, copy number variation (CNV), methylation, and protein expression data for the prediction of breast cancer survival. Before integration, the maximum relevance minimum redundancy (mRMR) feature selection method was utilized to select features that present high relevance to survival and low redundancy among themselves for each type of data. The experimental results demonstrated that the proposed method achieved the most optimal performance and there was a remarkable improvement in the prediction performance when somatic mutations were included, indicating that somatic mutations are critical for improving breast cancer survival predictions. Moreover, mRMR was superior to other feature selection methods used in previous studies. Furthermore, MKL outperformed the other traditional classifiers in multi-omics data integration. Our analysis indicated that through employing promising omics data such as somatic mutations and harnessing the power of proper feature selection methods and effective integration frameworks, the breast cancer survival predictive accuracy can be further increased, thereby providing a more optimal clinical diagnosis and more effective treatment for breast cancer patients.

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Identification of 6 gene markers for survival prediction in osteosarcoma cases based on multi-omics analysis

Experimental Biology and Medicine ◽

10.1177/1535370221992015 ◽

2021 ◽

pp. 153537022199201

Author(s):

Runmin Li ◽

Guosheng Wang ◽

ZhouJie Wu ◽

HuaGuang Lu ◽

Gen Li ◽

...

Keyword(s):

Feature Selection ◽

Cox Regression ◽

Cancer Genomics ◽

Predictive Performance ◽

Training Group ◽

Gene Expression Omnibus ◽

Survival Prediction ◽

Screening Process ◽

Gene Markers ◽

Validation Set

Multiple-omics sequencing information with high-throughput has laid a solid foundation to identify genes associated with cancer prognostic process. Multiomics information study is capable of revealing the cancer occurring and developing system according to several aspects. Currently, the prognosis of osteosarcoma is still poor, so a genetic marker is needed for predicting the clinically related overall survival result. First, Office of Cancer Genomics (OCG Target) provided RNASeq, copy amount variations information, and clinically related follow-up data. Genes associated with prognostic process and genes exhibiting copy amount difference were screened in the training group, and the mentioned genes were integrated for feature selection with least absolute shrinkage and selection operator (Lasso). Eventually, effective biomarkers received the screening process. Lastly, this study built and demonstrated one gene-associated prognosis mode according to the set of the test and gene expression omnibus validation set; 512 prognosis-related genes ( P < 0.01), 336 copies of amplified genes ( P < 0.05), and 36 copies of deleted genes ( P < 0.05) were obtained, and those genes of the mentioned genomic variants display close associations with tumor occurring and developing mechanisms. This study generated 10 genes for candidates through the integration of genomic variant genes as well as prognosis-related genes. Six typical genes (i.e. MYC, CHIC2, CCDC152, LYL1, GPR142, and MMP27) were obtained by Lasso feature selection and stepwise multivariate regression study, many of which are reported to show a relationship to tumor progressing process. The authors conducted Cox regression study for building 6-gene sign, i.e. one single prognosis-related element, in terms of cases carrying osteosarcoma. In addition, the samples were able to be risk stratified in the training group, test set, and externally validating set. The AUC of five-year survival according to the training group and validation set reached over 0.85, with superior predictive performance as opposed to the existing researches. Here, 6-gene sign was built to be new prognosis-related marking elements for assessing osteosarcoma cases’ surviving state.

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PD-0496: Multi-centric learning with a federated IT infrastructure: application to 2-year lung-cancer survival prediction

Radiotherapy and Oncology ◽

10.1016/s0167-8140(15)32802-4 ◽

2013 ◽

Vol 106 ◽

pp. S193-S194 ◽

Cited By ~ 3

Author(s):

A. Dekker ◽

G. Nalbantov ◽

C. Oberije ◽

W. Wiessler ◽

M. Eble ◽

...

Keyword(s):

Lung Cancer ◽

Cancer Survival ◽

Survival Prediction ◽

It Infrastructure ◽

Lung Cancer Survival

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Statistical Feature Extraction and Hybrid Feature Selection for Material Removal Rate Prediction in Chemical Mechanical Planarization Process

2021 5th IEEE Electron Devices Technology & Manufacturing Conference (EDTM) ◽

10.1109/edtm50988.2021.9421002 ◽

2021 ◽

Author(s):

Wenlan Jiang ◽

Chunpu Lv ◽

Bing Yang ◽

Fuquan Zhang ◽

Ying Gao ◽

...

Keyword(s):

Feature Extraction ◽

Feature Selection ◽

Material Removal Rate ◽

Material Removal ◽

Removal Rate ◽

Chemical Mechanical Planarization ◽

Statistical Feature ◽

Rate Prediction ◽

Planarization Process ◽

Selection For

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Multiple Linear Regression Analysis of lncRNA–Disease Association Prediction Based on Clinical Prognosis Data

BioMed Research International ◽

10.1155/2018/3823082 ◽

2018 ◽

Vol 2018 ◽

pp. 1-10 ◽

Cited By ~ 2

Author(s):

Bo Wang ◽

Jing Zhang

Keyword(s):

Prostate Cancer ◽

Linear Regression ◽

Multiple Linear Regression ◽

Cancer Survival ◽

Disease Association ◽

The Body ◽

Survival Prediction ◽

Clinical Prognosis ◽

Disease Associations ◽

Auc Value

Long noncoding RNAs (lncRNAs) have an important role in various life processes of the body, especially cancer. The analysis of disease prognosis is ignored in current prediction on lncRNA–disease associations. In this study, a multiple linear regression model was constructed for lncRNA–disease association prediction based on clinical prognosis data (MlrLDAcp), which integrated the cancer data of clinical prognosis and the expression quantity of lncRNA transcript. MlrLDAcp could realize not only cancer survival prediction but also lncRNA–disease association prediction. Ultimately, 60 lncRNAs most closely related to prostate cancer survival were selected from 481 alternative lncRNAs. Then, the multiple linear regression relationship between the prognosis survival of 176 patients with prostate cancer and 60 lncRNAs was also given. Compared with previous studies, MlrLDAcp had a predominant survival predictive ability and could effectively predict lncRNA–disease associations. MlrLDAcp had an area under the curve (AUC) value of 0.875 for survival prediction and an AUC value of 0.872 for lncRNA–disease association prediction. It could be an effective biological method for biomedical research.

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