A support vector machine approach to identification of proteins relevant to learning in a mouse model of Down Syndrome

Eicher, Tara; Sinha, Kalyan B.

doi:10.1109/ijcnn.2017.7966282

Cited by 8 publications

(4 citation statements)

References 65 publications

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“…As for the obtained classification scores, these are slightly below what have been reported by other studies (e.g. 86% [56] and 75-90% [57]), in spite of the fact that a very small subset of the data is here considered.…”

Section: Mice Protein Expression Datasetcontrasting

confidence: 73%

Understanding diseases as increased heterogeneity: a complex network computational framework

Zanin

Tuñas

Menasalvas

2018

J. R. Soc. Interface.

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Owing to the complexity of the human body, most diseases present a high interpersonal variability in the way they manifest, i.e. in their phenotype, which has important clinical repercussions-for instance, the difficulty in defining objective diagnostic rules. Here we explore the hypothesis that signs and symptoms used to define a disease should be understood in terms of the dispersion (as opposed to the average) of physical observables. To that end, we propose a computational framework, based on complex networks theory, to map groups of subjects to a network structure, based on their pairwise phenotypical similarity. We demonstrate that the resulting structure can be used to improve the performance of classification algorithms, especially in the case of a limited number of instances, with both synthetic and real datasets. Beyond providing an alternative conceptual understanding of diseases, the proposed framework could be of special relevance in the growing field of personalized, or -to-1, medicine.

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Section: Mice Protein Expression Datasetcontrasting

confidence: 73%

Understanding diseases as increased heterogeneity: a complex network computational framework

Zanin

Tuñas

Menasalvas

2018

J. R. Soc. Interface.

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show abstract

“…Similar to [37], the objective is defined as the identification of the most relevant proteins for the prediction of class labels instead of proteins differentiating classes. In [18], a supervised learning approach, support vector machines, is adopted for the identification of differentially expressed proteins. However, in this study, the classification implementations are limited to the tasks reported in the original research, [23].…”

Section: Discussionmentioning

confidence: 99%

Modularity Analysis of Bipartite Networks and Multivariate ANOVA for Identification of Differentially Expressed Proteins in a Mouse Model of Down Syndrome

Jazayeri

Pajouhanfar

Saba

et al. 2020

Proceedings of the 11th ACM International Conference on Bioinformatics, Computational Biology and Health Informatics

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“…SVMs can also be extended to learn non-linear separators using the kernel trick [76], yet we note that these are more difficult to interpret than linear hyperplanes [171]. From SVM models, one can also evaluate the contribution of each analyte to separating the optimal hyperplane, which is calculated as the magnitude of the linear weights of the hyperplane [172][173][174]. Another approach to decipher the analytes most relevant to the model is to consider both the weight and margin between the hyperplane [175].…”

Section: Machine Learning Methods For Predicting Phenotypementioning

confidence: 99%

Metabolomics and Multi-Omics Integration: A Survey of Computational Methods and Resources

et al. 2020

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As researchers are increasingly able to collect data on a large scale from multiple clinical and omics modalities, multi-omics integration is becoming a critical component of metabolomics research. This introduces a need for increased understanding by the metabolomics researcher of computational and statistical analysis methods relevant to multi-omics studies. In this review, we discuss common types of analyses performed in multi-omics studies and the computational and statistical methods that can be used for each type of analysis. We pinpoint the caveats and considerations for analysis methods, including required parameters, sample size and data distribution requirements, sources of a priori knowledge, and techniques for the evaluation of model accuracy. Finally, for the types of analyses discussed, we provide examples of the applications of corresponding methods to clinical and basic research. We intend that our review may be used as a guide for metabolomics researchers to choose effective techniques for multi-omics analyses relevant to their field of study.

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A support vector machine approach to identification of proteins relevant to learning in a mouse model of Down Syndrome

Cited by 8 publications

References 65 publications

Understanding diseases as increased heterogeneity: a complex network computational framework

Understanding diseases as increased heterogeneity: a complex network computational framework

Modularity Analysis of Bipartite Networks and Multivariate ANOVA for Identification of Differentially Expressed Proteins in a Mouse Model of Down Syndrome

Metabolomics and Multi-Omics Integration: A Survey of Computational Methods and Resources

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