Feature selection and survival modeling in The Cancer Genome Atlas

Kim, Hyunsoo; Bredel, Markus

doi:10.2147/ijn.s40733

Cited by 13 publications

(14 citation statements)

References 14 publications

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“…For the 1NN model, the correlation coefficient r 1 using the full set of iron genes was 0.23. This produces higher correlation values than those found in Kim and Bredel [20], who found a correlation of 0.18 between observed and predicted survival of GBM survival times when employing 1NN over a collection of 12,042 genes. For the Coxnet algorithm, we performed 10-fold cross validation to determine L 1 regularization parameters.…”

Section: Resultsmentioning

confidence: 53%

“…The correlation coefficient r 2 for the Coxnet model was -0.30 (as we are comparing risk with survival times, we should expect a negative correlation). While this correlation was slightly weaker than the 1NN model, the removal of 53 of 61 genes produced stronger correlations when comparing with Kim and Bredel [20], who found a correlation of -0.22 using Coxnet applied to a collection of 12,042 genes, and a correlation of -0.24 when restricting to genes associated with cancer pathways. The Coxnet algorithm minimizes a penalized likelihood, and can assign multiple regression coefficients to exactly zero.…”

Section: Resultsmentioning

confidence: 74%

“…Following Kim and Bredel [20], we used a 1-nearest neighbor (1NN) algorithm and a Coxnet model, both of which are compared against a random selection baseline predictor.…”

Section: Experimental Designmentioning

confidence: 99%

“…Simon and colleagues created the R package Coxnet, which applies this algorithm to determining relative risks in a regularized Cox regression model [21]. As shown by Kim and Bredel [20], Coxnet can be applied for feature selection when considering spaces of several thousand genes. However, as coordinate descent ultimately finds only local minima, it is not surprising that predictive power increases when restricted to sets of genes known to be correlated with cancer mortality.…”

Section: Experimental Designmentioning

confidence: 99%

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Aberrations in the Iron Regulatory Gene Signature Are Associated with Decreased Survival in Diffuse Infiltrating Gliomas

et al. 2016

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Iron is a tightly regulated micronutrient with no physiologic means of elimination and is necessary for cell division in normal tissue. Recent evidence suggests that dysregulation of iron regulatory proteins may play a role in cancer pathophysiology. We use public data from The Cancer Genome Atlas (TCGA) to study the association between survival and expression levels of 61 genes coding for iron regulatory proteins in patients with World Health Organization Grade II-III gliomas. Using a feature selection algorithm we identified a novel, optimized subset of eight iron regulatory genes (STEAP3, HFE, TMPRSS6, SFXN1, TFRC, UROS, SLC11A2, and STEAP4) whose differential expression defines two phenotypic groups with median survival differences of 52.3 months for patients with grade II gliomas (25.9 vs. 78.2 months, p< 10−3), 43.5 months for patients with grade III gliomas (43.9 vs. 87.4 months, p = 0.025), and 54.0 months when considering both grade II and III gliomas (79.9 vs. 25.9 months, p < 10−5).

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Section: Resultsmentioning

confidence: 53%

Section: Resultsmentioning

confidence: 74%

“…Following Kim and Bredel [20], we used a 1-nearest neighbor (1NN) algorithm and a Coxnet model, both of which are compared against a random selection baseline predictor.…”

Section: Experimental Designmentioning

confidence: 99%

Section: Experimental Designmentioning

confidence: 99%

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Aberrations in the Iron Regulatory Gene Signature Are Associated with Decreased Survival in Diffuse Infiltrating Gliomas

et al. 2016

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“…K-NN is a popularly used classifier due to its simplicity, straightforwardness and high efficiency even with noisy data [29]. Even with its straightforwardness, it is capable of achieving high accuracy rates in medical applications [30,31]. K-NN assigns a class to each data point in the test set according to the class of it amongst k-nearest neighbors inside the training set [32].…”

mentioning

confidence: 99%

Breast Cancer Diagnosis Using an Efficient CAD System Based on Multiple Classifiers

2019

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Breast cancer is one of the major health issues across the world. In this study, a new computer-aided detection (CAD) system is introduced. First, the mammogram images were enhanced to increase the contrast. Second, the pectoral muscle was eliminated and the breast was suppressed from the mammogram. Afterward, some statistical features were extracted. Next, k-nearest neighbor (k-NN) and decision trees classifiers were used to classify the normal and abnormal lesions. Moreover, multiple classifier systems (MCS) was constructed as it usually improves the classification results. The MCS has two structures, cascaded and parallel structures. Finally, two wrapper feature selection (FS) approaches were applied to identify those features, which influence classification accuracy. The two data sets (1) the mammographic image analysis society digital mammogram database (MIAS) and (2) the digital mammography dream challenge were combined together to test the CAD system proposed. The highest accuracy achieved with the proposed CAD system before FS was 99.7% using the Adaboosting of the J48 decision tree classifiers. The highest accuracy after FS was 100 %, which was achieved with k‐NN classifier. Moreover, the area under the curve (AUC) of the receiver operating characteristic (ROC) curve was equal to 1.0. The results showed that the proposed CAD system was able to accurately classify normal and abnormal lesions in mammogram samples.

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A two-step feature selection procedure for relevant markers of Squamous Cell Lung Carcinoma using different survival models

Bhattacharjee¹,

Basak²,

Kumari³

2023

Healthcare Analytics

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Feature selection and survival modeling in The Cancer Genome Atlas

Cited by 13 publications

References 14 publications

Aberrations in the Iron Regulatory Gene Signature Are Associated with Decreased Survival in Diffuse Infiltrating Gliomas

Aberrations in the Iron Regulatory Gene Signature Are Associated with Decreased Survival in Diffuse Infiltrating Gliomas

Breast Cancer Diagnosis Using an Efficient CAD System Based on Multiple Classifiers

A two-step feature selection procedure for relevant markers of Squamous Cell Lung Carcinoma using different survival models

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