7,078 research outputs found
Predicting protein-protein interactions as a one-class classification problem
Protein-protein interactions represent a key step in understanding proteins functions. This is due to the fact that proteins usually work in context of other proteins and rarely function alone. Machine learning techniques have been used to predict protein-protein interactions. However, most of these techniques address this problem as a binary classification problem. While it is easy to get a dataset of interacting protein as positive example, there is no experimentally confirmed non-interacting protein to be considered as a negative set. Therefore, in this paper we solve this problem as a one-class classification problem using One-Class SVM (OCSVM). Using only positive examples (interacting protein pairs) for training, the OCSVM achieves accuracy of 80%. These results imply that protein-protein interaction can be predicted using one-class classifier with reliable accuracy
Diffusion Component Analysis: Unraveling Functional Topology in Biological Networks
Complex biological systems have been successfully modeled by biochemical and
genetic interaction networks, typically gathered from high-throughput (HTP)
data. These networks can be used to infer functional relationships between
genes or proteins. Using the intuition that the topological role of a gene in a
network relates to its biological function, local or diffusion based
"guilt-by-association" and graph-theoretic methods have had success in
inferring gene functions. Here we seek to improve function prediction by
integrating diffusion-based methods with a novel dimensionality reduction
technique to overcome the incomplete and noisy nature of network data. In this
paper, we introduce diffusion component analysis (DCA), a framework that plugs
in a diffusion model and learns a low-dimensional vector representation of each
node to encode the topological properties of a network. As a proof of concept,
we demonstrate DCA's substantial improvement over state-of-the-art
diffusion-based approaches in predicting protein function from molecular
interaction networks. Moreover, our DCA framework can integrate multiple
networks from heterogeneous sources, consisting of genomic information,
biochemical experiments and other resources, to even further improve function
prediction. Yet another layer of performance gain is achieved by integrating
the DCA framework with support vector machines that take our node vector
representations as features. Overall, our DCA framework provides a novel
representation of nodes in a network that can be used as a plug-in architecture
to other machine learning algorithms to decipher topological properties of and
obtain novel insights into interactomes.Comment: RECOMB 201
Prediction of protein-protein interactions using one-class classification methods and integrating diverse data
This research addresses the problem of prediction of protein-protein interactions (PPI)
when integrating diverse kinds of biological information. This task has been commonly
viewed as a binary classification problem (whether any two proteins do or do not interact)
and several different machine learning techniques have been employed to solve this
task. However the nature of the data creates two major problems which can affect results.
These are firstly imbalanced class problems due to the number of positive examples (pairs
of proteins which really interact) being much smaller than the number of negative ones.
Secondly the selection of negative examples can be based on some unreliable assumptions
which could introduce some bias in the classification results.
Here we propose the use of one-class classification (OCC) methods to deal with the task of
prediction of PPI. OCC methods utilise examples of just one class to generate a predictive
model which consequently is independent of the kind of negative examples selected; additionally
these approaches are known to cope with imbalanced class problems. We have
designed and carried out a performance evaluation study of several OCC methods for this
task, and have found that the Parzen density estimation approach outperforms the rest. We
also undertook a comparative performance evaluation between the Parzen OCC method
and several conventional learning techniques, considering different scenarios, for example
varying the number of negative examples used for training purposes. We found that the
Parzen OCC method in general performs competitively with traditional approaches and in
many situations outperforms them. Finally we evaluated the ability of the Parzen OCC
approach to predict new potential PPI targets, and validated these results by searching for
biological evidence in the literature
PiRaNhA: A server for the computational prediction of RNA-binding residues in protein sequences
The PiRaNhA web server is a publicly available online resource that automatically predicts the location of RNA-binding residues (RBRs) in protein sequences. The goal of functional annotation of sequences in the field of RNA binding is to provide predictions of high accuracy that require only small numbers of targeted mutations for verification. The PiRaNhA server uses a support vector machine (SVM), with position-specific scoring matrices, residue interface propensity, predicted residue accessibility and residue hydrophobicity as features. The server allows the submission of up to 10 protein sequences, and the predictions for each sequence are provided on a web page and via email. The prediction results are provided in sequence format with predicted RBRs highlighted, in text format with the SVM threshold score indicated and as a graph which enables users to quickly identify those residues above any specific SVM threshold. The graph effectively enables the increase or decrease of the false positive rate. When tested on a non-redundant data set of 42 protein sequences not used in training, the PiRaNhA server achieved an accuracy of 85%, specificity of 90% and a Matthews correlation coefficient of 0.41 and outperformed other publicly available servers. The PiRaNhA prediction server is freely available at http://www.bioinformatics.sussex.ac.uk/PIRANHA. © The Author(s) 2010. Published by Oxford University Press
Exploring the potential of 3D Zernike descriptors and SVM for protein\u2013protein interface prediction
Abstract Background The correct determination of protein–protein interaction interfaces is important for understanding disease mechanisms and for rational drug design. To date, several computational methods for the prediction of protein interfaces have been developed, but the interface prediction problem is still not fully understood. Experimental evidence suggests that the location of binding sites is imprinted in the protein structure, but there are major differences among the interfaces of the various protein types: the characterising properties can vary a lot depending on the interaction type and function. The selection of an optimal set of features characterising the protein interface and the development of an effective method to represent and capture the complex protein recognition patterns are of paramount importance for this task. Results In this work we investigate the potential of a novel local surface descriptor based on 3D Zernike moments for the interface prediction task. Descriptors invariant to roto-translations are extracted from circular patches of the protein surface enriched with physico-chemical properties from the HQI8 amino acid index set, and are used as samples for a binary classification problem. Support Vector Machines are used as a classifier to distinguish interface local surface patches from non-interface ones. The proposed method was validated on 16 classes of proteins extracted from the Protein–Protein Docking Benchmark 5.0 and compared to other state-of-the-art protein interface predictors (SPPIDER, PrISE and NPS-HomPPI). Conclusions The 3D Zernike descriptors are able to capture the similarity among patterns of physico-chemical and biochemical properties mapped on the protein surface arising from the various spatial arrangements of the underlying residues, and their usage can be easily extended to other sets of amino acid properties. The results suggest that the choice of a proper set of features characterising the protein interface is crucial for the interface prediction task, and that optimality strongly depends on the class of proteins whose interface we want to characterise. We postulate that different protein classes should be treated separately and that it is necessary to identify an optimal set of features for each protein class
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