FRINK Linked Data Fragments server

Matches in SemOpenAlex for { <https://semopenalex.org/work/W3035708679> ?p ?o ?g. }

• W3035708679 abstract "Abstract Machine learning approaches hold potential for deconstructing complex psychiatric traits and yielding biomarkers which have a large potential for clinical application. Particularly, the advancement in deep learning methods has promoted them as highly promising tools for this purpose due to their capability to handle high-dimensional data and automatically extract high-level latent features. However, current proposed approaches for psychiatric classification or prediction using biological data do not allow direct interpretation of original features, which hinders insights into the biological underpinnings and development of biomarkers. In the present study, we introduce a sparse deep neural network (DNN) approach to identify sparse and interpretable features for schizophrenia (SZ) case-control classification. An L 0 -norm regularization is implemented on the input layer of the network for sparse feature selection, which can later be interpreted based on importance weights. We applied the proposed approach on a large multi-study cohort (N = 1,684) with brain structural MRI (gray matter volume (GMV)) and genetic (single nucleotide polymorphism (SNP)) data for discrimination of patients with SZ vs. controls. A total of 634 individuals served as training samples, and the resulting classification model was evaluated for generalizability on three independent data sets collected at different sites with different scanning protocols (n = 635, 255 and 160, respectively). We examined the classification power of pure GMV features, as well as combined GMV and SNP features. The performance of the proposed approach was compared with that yielded by an independent component analysis + support vector machine (ICA+SVM) framework. Empirical experiments demonstrated that sparse DNN slightly outperformed ICA+SVM and more effectively fused GMV and SNP features for SZ discrimination. With combined GMV and SNP features, sparse DNN yielded an average classification error rate of 28.98% on external data. The importance weights suggested that the DNN model prioritized to select frontal and superior temporal gyrus for SZ classification when a high sparsity was enforced, and parietal regions were further included with a lower sparsity setting, which strongly echoed previous literature. This is the first attempt to apply an interpretable sparse DNN model to imaging and genetic features for SZ classification with generalizability assessed in a large and multi-study cohort. The results validate the application of the proposed approach to SZ classification, and promise extended utility on other data modalities (e.g. functional and diffusion images) and traits (e.g. continuous scores) which ultimately may result in clinically useful tools." @default.
• W3035708679 created "2020-06-19" @default.
• W3035708679 creator A5001798382 @default.
• W3035708679 creator A5002668347 @default.
• W3035708679 creator A5005182829 @default.
• W3035708679 creator A5008815202 @default.
• W3035708679 creator A5016103514 @default.
• W3035708679 creator A5018920191 @default.
• W3035708679 creator A5020215901 @default.
• W3035708679 creator A5032850756 @default.
• W3035708679 creator A5036338045 @default.
• W3035708679 creator A5038635573 @default.
• W3035708679 creator A5048350283 @default.
• W3035708679 creator A5066292657 @default.
• W3035708679 creator A5070184927 @default.
• W3035708679 creator A5073216396 @default.
• W3035708679 creator A5083359076 @default.
• W3035708679 creator A5087017146 @default.
• W3035708679 date "2020-06-12" @default.
• W3035708679 modified "2023-10-18" @default.
• W3035708679 title "Sparse Deep Neural Networks on Imaging Genetics for Schizophrenia Case-Control Classification" @default.
• W3035708679 cites W1590571936 @default.
• W3035708679 cites W1970789124 @default.
• W3035708679 cites W1972498024 @default.
• W3035708679 cites W1977465442 @default.
• W3035708679 cites W1983668233 @default.
• W3035708679 cites W1985327120 @default.
• W3035708679 cites W1997159235 @default.
• W3035708679 cites W2004230500 @default.
• W3035708679 cites W2006931708 @default.
• W3035708679 cites W2020318870 @default.
• W3035708679 cites W2061539393 @default.
• W3035708679 cites W2062648684 @default.
• W3035708679 cites W2079590167 @default.
• W3035708679 cites W2087036932 @default.
• W3035708679 cites W2096791516 @default.
• W3035708679 cites W2101357408 @default.
• W3035708679 cites W2105527159 @default.
• W3035708679 cites W2108384452 @default.
• W3035708679 cites W2117038895 @default.
• W3035708679 cites W2118540296 @default.
• W3035708679 cites W2125576000 @default.
• W3035708679 cites W2137526583 @default.
• W3035708679 cites W2141724543 @default.
• W3035708679 cites W2158282570 @default.
• W3035708679 cites W2161633633 @default.
• W3035708679 cites W2166793287 @default.
• W3035708679 cites W2486515975 @default.
• W3035708679 cites W2553557322 @default.
• W3035708679 cites W2596331123 @default.
• W3035708679 cites W2614464346 @default.
• W3035708679 cites W2615866848 @default.
• W3035708679 cites W2623597509 @default.
• W3035708679 cites W2767050687 @default.
• W3035708679 cites W2789863594 @default.
• W3035708679 cites W2804287612 @default.
• W3035708679 cites W2810277203 @default.
• W3035708679 cites W2944560486 @default.
• W3035708679 cites W2967967941 @default.
• W3035708679 cites W2977330744 @default.
• W3035708679 cites W4213212399 @default.
• W3035708679 cites W4230920194 @default.
• W3035708679 cites W4231665507 @default.
• W3035708679 cites W4234617039 @default.
• W3035708679 cites W4295750005 @default.
• W3035708679 doi "https://doi.org/10.1101/2020.06.11.20128975" @default.
• W3035708679 hasPublicationYear "2020" @default.
• W3035708679 type Work @default.
• W3035708679 sameAs 3035708679 @default.
• W3035708679 citedByCount "0" @default.
• W3035708679 crossrefType "posted-content" @default.
• W3035708679 hasAuthorship W3035708679A5001798382 @default.
• W3035708679 hasAuthorship W3035708679A5002668347 @default.
• W3035708679 hasAuthorship W3035708679A5005182829 @default.
• W3035708679 hasAuthorship W3035708679A5008815202 @default.
• W3035708679 hasAuthorship W3035708679A5016103514 @default.
• W3035708679 hasAuthorship W3035708679A5018920191 @default.
• W3035708679 hasAuthorship W3035708679A5020215901 @default.
• W3035708679 hasAuthorship W3035708679A5032850756 @default.
• W3035708679 hasAuthorship W3035708679A5036338045 @default.
• W3035708679 hasAuthorship W3035708679A5038635573 @default.
• W3035708679 hasAuthorship W3035708679A5048350283 @default.
• W3035708679 hasAuthorship W3035708679A5066292657 @default.
• W3035708679 hasAuthorship W3035708679A5070184927 @default.
• W3035708679 hasAuthorship W3035708679A5073216396 @default.
• W3035708679 hasAuthorship W3035708679A5083359076 @default.
• W3035708679 hasAuthorship W3035708679A5087017146 @default.
• W3035708679 hasBestOaLocation W30357086791 @default.
• W3035708679 hasConcept C105795698 @default.
• W3035708679 hasConcept C119857082 @default.
• W3035708679 hasConcept C12267149 @default.
• W3035708679 hasConcept C148483581 @default.
• W3035708679 hasConcept C153180895 @default.
• W3035708679 hasConcept C154945302 @default.
• W3035708679 hasConcept C169760540 @default.
• W3035708679 hasConcept C18183760 @default.
• W3035708679 hasConcept C27158222 @default.
• W3035708679 hasConcept C33923547 @default.
• W3035708679 hasConcept C41008148 @default.
• W3035708679 hasConcept C50644808 @default.

• next