Sparse kernel methods for high-dimensional survival data

Evers, Ludger; Messow, Claudia-Martina

doi:10.1093/bioinformatics/btn253

Cited by 65 publications

(61 citation statements)

References 14 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…31) and kernel survival SVMs. 32 The survival analysis paradigm was considered with data in the form (t,v,δ), where δ is a binary vector, where 1 corresponds to a failure and 0 to censoring. 31 In this case, the survival models predict the patient hazard h i (t)…”

Section: C Predictive Modelsmentioning

confidence: 99%

“…At every event time t, a hyperplane is constructed to separate patients with failure from patients with censoring. 32 The model consists of several hyperplanes (one for each event time) that are parallel and therefore using an identical direction vector β. This is an analogy to the Cox model where the same β is used for all events as well.…”

Section: C2 Survival Svmsmentioning

confidence: 99%

“…The survpack  package was used for the implementation survival SVMs. 32 LibSVM was used for the implementation of two-class SVMs. 30 For both two-class and survival SVMs, the cost C of the errors and the variance σ K of the Gaussian kernel were optimized as C ∈ [10 0 ;10 3 ] and σ K ∈ [10 −3 ;10 4 ], respectively.…”

Section: D Experimental Setupmentioning

confidence: 99%

See 2 more Smart Citations

Predicting adenocarcinoma recurrence using computational texture models of nodule components in lung CT

et al. 2015

View full text Add to dashboard Cite

Purpose: To investigate the importance of presurgical computed tomography (CT) intensity and texture information from ground-glass opacities (GGO) and solid nodule components for the prediction of adenocarcinoma recurrence. Methods: For this study, 101 patients with surgically resected stage I adenocarcinoma were selected. During the follow-up period, 17 patients had disease recurrence with six associated cancer-related deaths. GGO and solid tumor components were delineated on presurgical CT scans by a radiologist. Computational texture models of GGO and solid regions were built using linear combinations of steerable Riesz wavelets learned with linear support vector machines (SVMs). Unlike other traditional texture attributes, the proposed texture models are designed to encode local image scales and directions that are specific to GGO and solid tissue. The responses of the locally steered models were used as texture attributes and compared to the responses of unaligned Riesz wavelets. The texture attributes were combined with CT intensities to predict tumor recurrence and patient hazard according to disease-free survival (DFS) time. Two families of predictive models were compared: LASSO and SVMs, and their survival counterparts: Cox-LASSO and survival SVMs.Results: The best-performing predictive model of patient hazard was associated with a concordance index (C-index) of 0.81 ± 0.02 and was based on the combination of the steered models and CT intensities with survival SVMs. The same feature group and the LASSO model yielded the highest area under the receiver operating characteristic curve (AUC) of 0.8 ± 0.01 for predicting tumor recurrence, although no statistically significant difference was found when compared to using intensity features solely. For all models, the performance was found to be significantly higher when image attributes were based on the solid components solely versus using the entire tumors (p < 3.08 × 10 −5 ). Conclusions: This study constitutes a novel perspective on how to interpret imaging information from CT examinations by suggesting that most of the information related to adenocarcinoma aggressiveness is related to the intensity and morphological properties of solid components of the tumor. The prediction of adenocarcinoma relapse was found to have low specificity but very high sensitivity. Our results could be useful in clinical practice to identify patients for which no recurrence is expected with a very high confidence using a presurgical CT scan only. It also provided an accurate estimation of the risk of recurrence after a given duration t from surgical resection (i.e., C-index = 0.81 ± 0.02). C 2015 American Association of Physicists in Medicine. [http://dx

show abstract

Section: C Predictive Modelsmentioning

confidence: 99%

Section: C2 Survival Svmsmentioning

confidence: 99%

See 1 more Smart Citation

Predicting adenocarcinoma recurrence using computational texture models of nodule components in lung CT

et al. 2015

View full text Add to dashboard Cite

show abstract

“…One of the most studied indices is the southern oscillation index (SOI), which is strongly correlated with the El Niño phenomenon [39] and is predictive of climate in many parts of the world; see Ref. 10 for other examples. Thus, ocean dynamics are known to have strong influence over climate processes on land, but the nature of these relationships is not always well understood.…”

Section: Predictive Modelingmentioning

confidence: 99%

“…Efforts in this direction have been successful in developing sparse models, which promote sparsity within the dependencies characterized by the model. These models have been applied successfully in a number of fields, such as signal processing [4], bioinformatics [10], computer vision [26] etc. Incorporating sparsity within a statistical model provides a natural control over the complexity of the model achieved through training.…”

Section: Introductionmentioning

confidence: 99%

Sparse Group Lasso: Consistency and Climate Applications

Chatterjee¹,

Steinhaeuser²,

Banerjee³

et al. 2012

Proceedings of the 2012 SIAM International Conference on Data Mining

View full text Add to dashboard Cite

The design of statistical predictive models for climate data gives rise to some unique challenges due to the high dimensionality and spatio-temporal nature of the datasets, which dictate that models should exhibit parsimony in variable selection. Recently, a class of methods which promote structured sparsity in the model have been developed, which is suitable for this task. In this paper, we prove theoretical statistical consistency of estimators with tree-structured norm regularizers. We consider one particular model, the Sparse Group Lasso (SGL), to construct predictors of land climate using ocean climate variables. Our experimental results demonstrate that the SGL model provides better predictive performance than the current state-of-the-art, remains climatologically interpretable, and is robust in its variable selection.

show abstract

An overview of techniques for linking high‐dimensional molecular data to time‐to‐event endpoints by risk prediction models

2011

View full text Add to dashboard Cite

Analysis of molecular data promises identification of biomarkers for improving prognostic models, thus potentially enabling better patient management. For identifying such biomarkers, risk prediction models can be employed that link high-dimensional molecular covariate data to a clinical endpoint. In low-dimensional settings, a multitude of statistical techniques already exists for building such models, e.g. allowing for variable selection or for quantifying the added value of a new biomarker. We provide an overview of techniques for regularized estimation that transfer this toward high-dimensional settings, with a focus on models for time-to-event endpoints. Techniques for incorporating specific covariate structure are discussed, as well as techniques for dealing with more complex endpoints. Employing gene expression data from patients with diffuse large B-cell lymphoma, some typical modeling issues from low-dimensional settings are illustrated in a high-dimensional application. First, the performance of classical stepwise regression is compared to stage-wise regression, as implemented by a component-wise likelihood-based boosting approach. A second issues arises, when artificially transforming the response into a binary variable. The effects of the resulting loss of efficiency and potential bias in a high-dimensional setting are illustrated, and a link to competing risks models is provided. Finally, we discuss conditions for adequately quantifying the added value of high-dimensional gene expression measurements, both at the stage of model fitting and when performing evaluation.

show abstract

Sparse kernel methods for high-dimensional survival data

Abstract: Software is available under the GNU Public License as an R package and can be obtained from the first author's website http://www.maths.bris.ac.uk/~maxle/software.html.

Cited by 65 publications

References 14 publications

Predicting adenocarcinoma recurrence using computational texture models of nodule components in lung CT

Predicting adenocarcinoma recurrence using computational texture models of nodule components in lung CT

Sparse Group Lasso: Consistency and Climate Applications

An overview of techniques for linking high‐dimensional molecular data to time‐to‐event endpoints by risk prediction models

Contact Info

Product

Resources

About