Enhanced analytical power of SDS‐PAGE using machine learning algorithms

Supek, Fran; Peharec, Petra; Krsnik-Rasol, Marijana; Šmuc, Tomislav

doi:10.1002/pmic.200700555

Cited by 22 publications

(12 citation statements)

References 14 publications

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“…To date, SVMs have been applied successfully to a broad range of regression problems in proteomics, such as identification of protein cleavage sites, amino acid retention time and isoelectric point prediction [10][11][12][13]. In the case of applying SVMs to the prediction of isoelectric point, a concise and meaningful encodings of the peptide properties are essential.…”

mentioning

confidence: 99%

Isoelectric point optimization using peptide descriptors and support vector machines

Perez‐Riverol

Audain

Millan

et al. 2012

Journal of Proteomics

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confidence: 99%

Isoelectric point optimization using peptide descriptors and support vector machines

Perez‐Riverol

Audain

Millan

et al. 2012

Journal of Proteomics

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“…This feature selection algorithm is able to effectively provide quality estimates for each voxel in the white matter skeleton. It has previously been used successfully in a number of different domains including genetics [82] and proteomics [83], and has recently begun to be used in MRI [74]. In the current study we see that it filters out irrelevant voxels and selects the most useful voxels which can be used by an SVM for training and classification.…”

Section: Discussionmentioning

confidence: 71%

White Matter Differences between Healthy Young ApoE4 Carriers and Non-Carriers Identified with Tractography and Support Vector Machines

et al. 2012

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The apolipoprotein E4 (ApoE4) is an established risk factor for Alzheimer's disease (AD). Previous work has shown that this allele is associated with functional (fMRI) changes as well structural grey matter (GM) changes in healthy young, middle-aged and older subjects. Here, we assess the diffusion characteristics and the white matter (WM) tracts of healthy young (20–38 years) ApoE4 carriers and non-carriers. No significant differences in diffusion indices were found between young carriers (ApoE4+) and non-carriers (ApoE4−). There were also no significant differences between the groups in terms of normalised GM or WM volume. A feature selection algorithm (ReliefF) was used to select the most salient voxels from the diffusion data for subsequent classification with support vector machines (SVMs). SVMs were capable of classifying ApoE4 carrier and non-carrier groups with an extremely high level of accuracy. The top 500 voxels selected by ReliefF were then used as seeds for tractography which identified a WM network that included regions of the parietal lobe, the cingulum bundle and the dorsolateral frontal lobe. There was a non-significant decrease in volume of this WM network in the ApoE4 carrier group. Our results indicate that there are subtle WM differences between healthy young ApoE4 carriers and non-carriers and that the WM network identified may be particularly vulnerable to further degeneration in ApoE4 carriers as they enter middle and old age.

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“…The traditional approaches are gel-based such as SDS-PAGE, which is useful for protein ‘fingerprinting’ of complex extracts for protein quantities and post-translational modifications [19,21]. Advances in mass spectrometry (MS) measurements have enabled protein quantification from complex samples.…”

Section: Omics Technology Development: Transcriptomics Proteomics Anmentioning

confidence: 99%

Integrative systems biology: an attempt to describe a simple weed

Liberman

Sozzani

Benfey

2012

Current Opinion in Plant Biology

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Genome-scale studies hold great promise for revealing novel plant biology. Because of the complexity of these techniques, numerous considerations need to be made before embarking on a study. Here we focus on the Arabidopsis model system because of the wealth of available genome-scale data. Many approaches are available that provide genome-scale information regarding the state of a given organism (e.g. genomics, epigenomics, transcriptomics, proteomics, metabolomics interactomics, ionomics, phenomics, etc.). Integration of all of these types of data will be necessary for a comprehensive description of Arabidopsis. In this review we propose that ‘triangulation’ among transcriptomics, proteomics and metabolomics is a meaningful approach for beginning this integrative analysis and uncovering a systems level perspective of Arabidopsis biology.

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Enhanced analytical power of SDS‐PAGE using machine learning algorithms

Cited by 22 publications

References 14 publications

Isoelectric point optimization using peptide descriptors and support vector machines

Isoelectric point optimization using peptide descriptors and support vector machines

White Matter Differences between Healthy Young ApoE4 Carriers and Non-Carriers Identified with Tractography and Support Vector Machines

Integrative systems biology: an attempt to describe a simple weed

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