Protein function prediction using guilty by association from interaction networks

Piovesan, Damiano; Giollo, Manuel; Ferrari, Carlo; Tosatto, Silvio C. E.

doi:10.1007/s00726-015-2049-3

Cited by 31 publications

(25 citation statements)

References 25 publications

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“…where the direct neighbours of a gene of interest are assumed to be implicated in similar processes [14], to machine learning (ML) algorithms designed to learn from the features of the network to make more useful biological predictions (e.g. [15]).…”

Section: Introductionmentioning

confidence: 99%

Benchmarking network propagation methods for disease gene identification

Picart‐Armada

Barrett

Willé

et al. 2018

Preprint

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BackgroundIn-silico identification of potential disease genes has become an essential aspect of drug target discovery. Recent studies suggest that one powerful way to identify successful targets is through the use of genetic and genomic information. Given a known disease gene, leveraging intermolecular connections via networks and pathways seems a natural way to identify other genes and proteins that are involved in similar biological processes, and that can therefore be analysed as additional targets.ResultsHere, we systematically tested the ability of 12 varied network-based algorithms to identify target genes and cross-validated these using gene-disease data from Open Targets on 22 common diseases. We considered two biological networks, six performance metrics and compared two types of input gene-disease association scores. We also compared several cross-validation schemes and showed that different choices had a remarkable impact on the performance estimates. When seeding biological networks with known drug targets, we found that machine learning and diffusion-based methods are able to find novel targets, showing around 2-4 true hits in the top 20 suggestions. Seeding the networks with genes associated to disease by genetics resulted in poorer performance, below 1 true hit on average. We also observed that the use of a larger network, although noisier, improved overall performance.ConclusionsWe conclude that machine learning and diffusion-based prioritisers are suited for drug discovery in practice and improve over simpler neighbour-voting methods. We also demonstrate the large effect of several factors on prediction performance, especially the validation strategy, input biological network, and definition of seed disease genes.

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Section: Introductionmentioning

confidence: 99%

Benchmarking network propagation methods for disease gene identification

Picart‐Armada

Barrett

Willé

et al. 2018

Preprint

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“…Using these constructed attribute features and annotated proteins, a classifier can be trained first and then be used to predict function annotations for unannotated proteins. On the other hand, graph-based approaches [6][7][8] assume that the closely related proteins (or genes) share similar functional annotations on the basis of network structure information. In [9], protein interactions were measured by several computational approaches.…”

Section: Introductionmentioning

confidence: 99%

Predicting protein function via multi-label supervised topic model on gene ontology

Liu

Tang

et al. 2017

Biotechnology & Biotechnological Equipment

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As the biological datasets accumulate rapidly, computational methods designed to automate protein function prediction are critically needed. The problem of protein function prediction can be considered as a multi-label classification problem resulting in protein functional annotations. Nevertheless, biologists prefer to discover the correlations between protein attributes and functions. We introduce a multi-label supervised topic model into protein function prediction and investigate the advantages of this approach. This topic model can not only work out the function probability distributions over protein instances effectively, but also directly provide the words probability distributions over functions. To the best of our knowledge, this is the first effort to apply a multi-label supervised topic model to the protein function prediction. In this paper, we model a protein as a document and a function label as a topic. First, a set of protein sequences is formalized into a bag of words. Then, we perform inference and estimate the model parameters to predict protein functions. Experimental results on yeast and human datasets demonstrate the effectiveness of this multi-label supervised topic model on protein function prediction. Meanwhile, the experiments also show that this multi-label supervised topic model delivers superior results over the compared algorithms. In summary, the method discussed in this paper provides a new efficient approach to protein function prediction and reveals more information about functions.

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“…In CAFA2, GO-FDR [12], one of the best 47 performing systems on all three GO ontologies, calculates the probability of a protein 48 being associated with a target GO term, using predictions from the PSI-BLAST tool. 49 Recently, You et al [13] suggested an approach based on an ensemble of logistic 50 regression models which, resulted in the best overall performance among the 51 participating teams in the CAFA3 challenge. For each GO term, a set of three logistic 52 regression models are independently trained based on structural information from 53 InterPro [14], biophysical attributes from ProFET [15] and amino acid n-gram features.…”

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confidence: 99%

“…Firstly, features derived 386 from sequences related in other ways than solely through homology, e.g. through 387 co-expression or binding, can be potentially beneficial especially for the prediction of 388 biological processes, as demonstrated for instance by Piovesan et al [51] and Kulmanov 389 et al [18]. Of the three GO ontologies, biological process currently exhibits the lowest 390 absolute performance, and therefore is the most impactful target for further 391 development.…”

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confidence: 99%

Neural network and random forest models in protein function prediction

Hakala

Kaewphan

Björne

et al. 2019

Preprint

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Over the past decade, the demand for automated protein function prediction has increased due to the volume of newly sequenced proteins. In this paper, we address the function prediction task by developing an ensemble system automatically assigning Gene Ontology (GO) terms to the given input protein sequence.We develop an ensemble system which combines the GO predictions made by random forest (RF) and neural network (NN) classifiers. Both RF and NN models rely on features derived from BLAST sequence alignments, taxonomy and protein signature analysis tools. In addition, we report on experiments with a NN model that directly analyzes the amino acid sequence as its sole input, using a convolutional layer. The Swiss-Prot database is used as the training and evaluation data.In the CAFA3 evaluation, which relies on experimental verification of the functional predictions, our submitted ensemble model demonstrates competitive performance ranking among top-10 best-performing systems out of over 100 submitted systems. In this paper, we evaluate and further improve the CAFA3-submitted system. Our machine learning models together with the data pre-processing and feature generation tools are publicly available as an open source software at https://github.com/TurkuNLP/CAFA3

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Protein function prediction using guilty by association from interaction networks

Cited by 31 publications

References 25 publications

Benchmarking network propagation methods for disease gene identification

Benchmarking network propagation methods for disease gene identification

Predicting protein function via multi-label supervised topic model on gene ontology

Neural network and random forest models in protein function prediction

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