Identification of gene expression signatures across different types of neural stem cells with the Monte‐Carlo feature selection method

Lei, Chen; Li, JiaRui; Zhang, Yuhang; Kong, Feng; Wang, Shaopeng; Zhang, Yunhua; Huang, Tao; Kong, Xiangyin; Cai, Yi

doi:10.1002/jcb.26507

Cited by 54 publications

(52 citation statements)

References 52 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…To evaluate the prediction performance of mRMR genes, IFS method 20 – 26 was applied to select the genes with greatest prediction power. The IFS method is a wrapped feature selection method that combines the feature selection with classifier construction.…”

Section: Methodsmentioning

confidence: 99%

Identification of the predictive genes for the response of colorectal cancer patients to FOLFOX therapy

Lin¹,

Qiu²,

Zhang³

et al. 2018

OTT

View full text Add to dashboard Cite

BackgroundColorectal cancer is a malignant tumor with high death rate. Chemotherapy, radiotherapy and surgery are the three common treatments of colorectal cancer. For early colorectal cancer patients, postoperative adjuvant chemotherapy can reduce the risk of recurrence. For advanced colorectal cancer patients, palliative chemotherapy can significantly improve the life quality of patients and prolong survival. FOLFOX is one of the mainstream chemotherapies in colorectal cancer, however, its response rate is only about 50%.MethodsTo systematically investigate why some of the colorectal cancer patients have response to FOLFOX therapy while others do not, we searched all publicly available database and combined three gene expression datasets of colorectal cancer patients with FOLFOX therapy. With advanced minimal redundancy maximal relevance and incremental feature selection method, we identified the biomarker genes.ResultsA Support Vector Machine-based classifier was constructed to predict the response of colorectal cancer patients to FOLFOX therapy. Its accuracy, sensitivity and specificity were 0.854, 0.845 and 0.863, respectively.ConclusionThe biological analysis of representative biomarker genes suggested that apoptosis and inflammation signaling pathways were essential for the response of colorectal cancer patients to FOLFOX chemotherapy.

show abstract

Section: Methodsmentioning

confidence: 99%

Identification of the predictive genes for the response of colorectal cancer patients to FOLFOX therapy

Lin¹,

Qiu²,

Zhang³

et al. 2018

OTT

View full text Add to dashboard Cite

show abstract

“…Identifying the phenotype-associated features is one of the basic problems in bioinformatics, and for different problems, there are different solutions (Huang et al, 2008; Cai et al, 2010; Zhang et al, 2012, 2015, 2016, 2017; Li et al, 2014; Chen et al, 2018a; Wang et al, 2018). For identifying differentially expressed genes (DEG), the most widely used methods are the t- test, significance analysis of microarrays (SAM; Tusher et al, 2001), and linear regression as performed by Ramos et al (2014).…”

Section: Methodsmentioning

confidence: 99%

“…Therefore, we applied a greedy method called incremental feature selection (IFS) (Jiang et al, 2013; Li et al, 2014; Shu et al, 2014; Zhang N. et al, 2014a; Huang et al, 2015; Zhang et al, 2015; Chen et al, 2018a) to optimize the number of signature genes. In this method, too few genes may miss the important information and too many genes may introduce noise.…”

Section: Methodsmentioning

confidence: 99%

Identification and Analysis of Blood Gene Expression Signature for Osteoarthritis With Advanced Feature Selection Methods

Lan

Kong

et al. 2018

Front. Genet.

Self Cite

View full text Add to dashboard Cite

Osteoarthritis (OA) is a complex disease that affects articular joints and may cause disability. The incidence of OA is extremely high. Most elderly people have the symptoms of osteoarthritis. The physiotherapy of OA is time consuming, and the chances of full recovery from OA are very minimal. The most effective way of fighting OA is early diagnosis and early intervention. Liquid biopsy has become a popular noninvasive test. To find the blood gene expression signature for OA, we reanalyzed the publicly available blood gene expression profiles of 106 patients with OA and 33 control samples using an automatic computational pipeline based on advanced feature selection methods. Finally, a compact 23-gene set was identified. On the basis of these 23 genes, we constructed a Support Vector Machine (SVM) classifier and evaluated it with leave-one-out cross-validation. Its sensitivity (Sn), specificity (Sp), accuracy (ACC), and Mathew's correlation coefficient (MCC) were 0.991, 0.909, 0.971, and 0.920, respectively. Obviously, the performance needed to be validated in an independent large dataset, but the in-depth biological analysis of the 23 biomarkers showed great promise and suggested that mRNA surveillance pathway and multicellular organism growth played important roles in OA. Our results shed light on OA diagnosis through liquid biopsy.

show abstract

“…where TP, TN, FP, and FN stand for the number of true positive samples, true negative samples, false positive samples, and false negative samples, respectively. Since the sizes of KRAS mutation + samples and KRAS mutation -samples were imbalance and MCC can trade-off sensitivity and specificity (Chen et al, 2018a;Pan et al, 2018;Pan et al, 2019a;Pan et al, 2019b), MCC was used as the main performance metric.…”

Section: Prediction Performance Evaluation Of the Classifiermentioning

confidence: 99%

The Functional Effects of Key Driver KRAS Mutations on Gene Expression in Lung Cancer

Zhang

et al. 2020

Front. Genet.

View full text Add to dashboard Cite

Lung cancer is a common malignant cancer. Kirsten rat sarcoma oncogene (KRAS) mutations have been considered as a key driver for lung cancers. KRAS p.G12C mutations were most predominant in NSCLC which was comprised about 11-16% of lung adenocarcinomas (p.G12C accounts for 45-50% of mutant KRAS). But it is still not clear how the KRAS mutation triggers lung cancers. To study the molecular mechanisms of KRAS mutation in lung cancer. We analyzed the gene expression profiles of 156 KRAS mutation samples and other negative samples with two stage feature selection approach: (1) minimal Redundancy Maximal Relevance (mRMR) and (2) Incremental Feature Selection (IFS). At last, 41 predictive genes for KRAS mutation were identified and a KRAS mutation predictor was constructed. Its leave one out cross validation MCC was 0.879. Our results were helpful for understanding the roles of KRAS mutation in lung cancer.

show abstract

Identification of gene expression signatures across different types of neural stem cells with the Monte‐Carlo feature selection method

Cited by 54 publications

References 52 publications

Identification of the predictive genes for the response of colorectal cancer patients to FOLFOX therapy

Identification of the predictive genes for the response of colorectal cancer patients to FOLFOX therapy

Identification and Analysis of Blood Gene Expression Signature for Osteoarthritis With Advanced Feature Selection Methods

The Functional Effects of Key Driver KRAS Mutations on Gene Expression in Lung Cancer

Contact Info

Product

Resources

About