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Network-constrained group lasso for high-dimensional multinomial classification with application to cancer subtype prediction.

Tian X, Wang X, Chen J - Cancer Inform (2015)

Bottom Line: Efficient use of the network information is important to improve classification performance as well as the biological interpretability.The proposed model was compared to models with no prior structure information in both simulations and a problem of cancer subtype prediction with real TCGA (the cancer genome atlas) gene expression data.The network-constrained mode outperformed the traditional ones in both cases.

View Article: PubMed Central - PubMed

Affiliation: Department of Applied Mathematics and Statistics, Stony Brook University, Stony Brook, NY, USA.

ABSTRACT
Classic multinomial logit model, commonly used in multiclass regression problem, is restricted to few predictors and does not take into account the relationship among variables. It has limited use for genomic data, where the number of genomic features far exceeds the sample size. Genomic features such as gene expressions are usually related by an underlying biological network. Efficient use of the network information is important to improve classification performance as well as the biological interpretability. We proposed a multinomial logit model that is capable of addressing both the high dimensionality of predictors and the underlying network information. Group lasso was used to induce model sparsity, and a network-constraint was imposed to induce the smoothness of the coefficients with respect to the underlying network structure. To deal with the non-smoothness of the objective function in optimization, we developed a proximal gradient algorithm for efficient computation. The proposed model was compared to models with no prior structure information in both simulations and a problem of cancer subtype prediction with real TCGA (the cancer genome atlas) gene expression data. The network-constrained mode outperformed the traditional ones in both cases.

No MeSH data available.


Related in: MedlinePlus

Comparison of four candidate methods under incorrect network and overlapping network in terms of MSE, accuracy rate, and Brier score.
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f4-cin-suppl.6-2014-025: Comparison of four candidate methods under incorrect network and overlapping network in terms of MSE, accuracy rate, and Brier score.

Mentions: To investigate the impact of structure misspecification, we investigate the scenarios of incorrect network and overlapping network. We simulate a medium-sized data set with 100 predictors, 10 being relevant. Each subnetwork consists of 10 predictors. For the incorrect network setting, the 10 relevant predictors come from the first subnetwork. For the overlapping network setting, the 10 relevant predictors come from two subnetworks. The performance of our models is still satisfactory because of the flexible tuning parameter on the structure-constraint term (Fig. 4). In particular, the prediction accuracy of NGL-MLM is comparable to that of GL-MLM in both situations, whereas, in terms of parameter estimation and Brier score, the structure-constrained models NGL-MLM and NGL-MLMa outperform the other two.


Network-constrained group lasso for high-dimensional multinomial classification with application to cancer subtype prediction.

Tian X, Wang X, Chen J - Cancer Inform (2015)

Comparison of four candidate methods under incorrect network and overlapping network in terms of MSE, accuracy rate, and Brier score.
© Copyright Policy - open-access
Related In: Results  -  Collection

Show All Figures
getmorefigures.php?uid=PMC4295837&req=5

f4-cin-suppl.6-2014-025: Comparison of four candidate methods under incorrect network and overlapping network in terms of MSE, accuracy rate, and Brier score.
Mentions: To investigate the impact of structure misspecification, we investigate the scenarios of incorrect network and overlapping network. We simulate a medium-sized data set with 100 predictors, 10 being relevant. Each subnetwork consists of 10 predictors. For the incorrect network setting, the 10 relevant predictors come from the first subnetwork. For the overlapping network setting, the 10 relevant predictors come from two subnetworks. The performance of our models is still satisfactory because of the flexible tuning parameter on the structure-constraint term (Fig. 4). In particular, the prediction accuracy of NGL-MLM is comparable to that of GL-MLM in both situations, whereas, in terms of parameter estimation and Brier score, the structure-constrained models NGL-MLM and NGL-MLMa outperform the other two.

Bottom Line: Efficient use of the network information is important to improve classification performance as well as the biological interpretability.The proposed model was compared to models with no prior structure information in both simulations and a problem of cancer subtype prediction with real TCGA (the cancer genome atlas) gene expression data.The network-constrained mode outperformed the traditional ones in both cases.

View Article: PubMed Central - PubMed

Affiliation: Department of Applied Mathematics and Statistics, Stony Brook University, Stony Brook, NY, USA.

ABSTRACT
Classic multinomial logit model, commonly used in multiclass regression problem, is restricted to few predictors and does not take into account the relationship among variables. It has limited use for genomic data, where the number of genomic features far exceeds the sample size. Genomic features such as gene expressions are usually related by an underlying biological network. Efficient use of the network information is important to improve classification performance as well as the biological interpretability. We proposed a multinomial logit model that is capable of addressing both the high dimensionality of predictors and the underlying network information. Group lasso was used to induce model sparsity, and a network-constraint was imposed to induce the smoothness of the coefficients with respect to the underlying network structure. To deal with the non-smoothness of the objective function in optimization, we developed a proximal gradient algorithm for efficient computation. The proposed model was compared to models with no prior structure information in both simulations and a problem of cancer subtype prediction with real TCGA (the cancer genome atlas) gene expression data. The network-constrained mode outperformed the traditional ones in both cases.

No MeSH data available.


Related in: MedlinePlus