iRefWeb: interactive analysis of consolidated protein interaction data and their supporting evidence

Turner, Brian; Razick, Sabry; Turinsky, Andrei L.; Vlasblom, James; Crowdy, Edgard K.; Cho, Emerson; Morrison, Kyle; Donaldson, Ian; Wodak, Shoshana J.

doi:10.1093/database/baq023

Cited by 195 publications

(154 citation statements)

References 59 publications

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“…S1). IRefWeb (90) and IRefIndex (4) are systems allowing the merging and interrogation of protein interactions from more than 10 public databases.…”

mentioning

confidence: 99%

Popular Computational Methods to Assess Multiprotein Complexes Derived From Label-Free Affinity Purification and Mass Spectrometry (AP-MS) Experiments

Armean

Lilley

Trotter³

2013

Molecular & Cellular Proteomics

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Advances in sensitivity, resolution, mass accuracy, and throughput have considerably increased the number of protein identifications made via mass spectrometry. Despite these advances, state-of-the-art experimental methods for the study of protein-protein interactions yield more candidate interactions than may be expected biologically owing to biases and limitations in the experimental methodology. In silico methods, which distinguish between true and false interactions, have been developed and applied successfully to reduce the number of false positive results yielded by physical interaction assays. Such methods may be grouped according to: (1) the type of data used: methods based on experiment-specific measurements (e.g., spectral counts or identification scores) versus methods that extract knowledge encoded in external annotations (e.g., public interaction and functional categorisation databases); (2) the type of algorithm applied: the statistical description and estimation of physical protein properties versus predictive supervised machine learning or text-mining algorithms; (3) the type of protein relation evaluated: direct (binary) interaction of two proteins in a cocomplex versus probability of any functional relationship between two proteins (e.g., cooccurrence in a pathway, sub cellular compartment); and (4) initial motivation: elucidation of experimental data by evaluation versus prediction of novel protein-protein interaction, to be experimentally validated a posteriori. This work reviews several popular computational scoring methods and software platforms for protein-protein interactions evaluation according to their methodology, comparative strengths and weaknesses, data representation, accessibility, and availability.

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“…S1). IRefWeb (90) and IRefIndex (4) are systems allowing the merging and interrogation of protein interactions from more than 10 public databases.…”

mentioning

confidence: 99%

Popular Computational Methods to Assess Multiprotein Complexes Derived From Label-Free Affinity Purification and Mass Spectrometry (AP-MS) Experiments

Armean

Lilley

Trotter³

2013

Molecular & Cellular Proteomics

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“…The PPI was curated using iRefWeb, and genes were limited to those that were included in the gene expression dataset. 63 The start probabilities are defined by normalized gene expression values. First, the gene expression data is z-score normalized by gene, and then it is rescaled so that the values of each gene range from 0 to 1.…”

Section: Network Propagation Viewmentioning

confidence: 99%

Predicting colorectal cancer recurrence by utilizing multiple-view multiple-learner supervised learning.

Castellanos

Liu

Beauchamp

et al. 2017

JCO

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635 Background: Colorectal cancer (CRC) remains a leading cause of cancer-related mortality in the United States. A key therapeutic dilemma in the treatment of CRC is whether patients with stage II and stage III disease require adjuvant chemotherapy after surgical resection. Attempts to improve identification of patients at increased risk of recurrence have yielded many predictive models based on gene expression data, but none are FDA approved and none are used in standard clinical practice. To improve recurrence prediction, we utilize a machine learning approach to predict recurrence status at 3 years after diagnosis. Methods: A dataset was curated from six publically available microarray datasets, and multiple views were generated to include information from non-tumor tissue gene expression patterns, gene set structure, protein-protein interaction network structure, previously curated molecular signatures, and identified tumor suppressor/driver mutations. These views were used to train a diverse pool of base learners using 10x 10-fold cross-validation. Stacked generalization was used to train an ensemble model, also known as a meta-learner, from the predictions of these base learners. Results: The performance of microarray trained models was significantly better compared to models trained on clinical data (Paired Wilcoxon signed rank test, p = 1.49 x 10-8), demonstrating that molecular data predicts recurrence significantly better than basic clinical data. Review of the model training performances revealed that non-linear classifiers often outperform linear classifiers, and that ensemble methods can also enhance performance. We also demonstrate the feasibility of the multiple-view multiple learner (MVML) supervised learning framework to generate and integrate predictions across a diverse set of learners, with the performance of the meta-learner exceeding or matching that of the best base learners across all performance metrics. Conclusions: This work represents the first effort to use ensemble learning to predict CRC recurrence and highlights the promise of ensemble learning to improve the performance of predictive models in order to realize the goals of precision medicine.

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“…In particular, structural data from crystallography or mass spectrometry yield a bias toward stable interactions at the detriment of transient ones. For these reasons, strategies computing confidence scores usually resort to machine learning tools trained on the aforementioned data (11) and also (12).…”

Section: Connectivity Inference From Sets Of Oligomersmentioning

confidence: 99%

Unveiling Contacts within Macromolecular Assemblies by Solving Minimum Weight Connectivity Inference (MWC) Problems*

Agarwal

Caillouet

Coudert

et al. 2015

Molecular & Cellular Proteomics

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Consider a set of oligomers listing the subunits involved in subcomplexes of a macromolecular assembly, obtained e.g. using native mass spectrometry or affinity purification. Given these oligomers, connectivity inference (CI) consists of finding the most plausible contacts between these subunits, and minimum connectivity inference (MCI) is the variant consisting of finding a set of contacts of smallest cardinality. MCI problems avoid speculating on the total number of contacts but yield a subset of all contacts and do not allow exploiting a priori information on the likelihood of individual contacts. In this context, we present two novel algorithms, MILP-W and MILP-W B . The former solves the minimum weight connectivity inference (MWCI), an optimization problem whose criterion mixes the number of contacts and their likelihood. The latter uses the former in a bootstrap fashion to improve the sensitivity and the specificity of solution sets.Experiments on three systems (yeast exosome, yeast proteasome lid, human eIF3), for which reference contacts are known (crystal structure, cryo electron microscopy, cross-linking), show that our algorithms predict contacts with high specificity and sensitivity, yielding a very significant improvement over previous work, typically a twofold increase in sensitivity.The software accompanying this paper is made available and should prove of ubiquitous interest whenever connectivity inference from oligomers is faced. Molecular & Cellular

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iRefWeb: interactive analysis of consolidated protein interaction data and their supporting evidence

Cited by 195 publications

References 59 publications

Popular Computational Methods to Assess Multiprotein Complexes Derived From Label-Free Affinity Purification and Mass Spectrometry (AP-MS) Experiments

Popular Computational Methods to Assess Multiprotein Complexes Derived From Label-Free Affinity Purification and Mass Spectrometry (AP-MS) Experiments

Predicting colorectal cancer recurrence by utilizing multiple-view multiple-learner supervised learning.

Unveiling Contacts within Macromolecular Assemblies by Solving Minimum Weight Connectivity Inference (MWC) Problems*

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