We present a novel partner-specific protein-protein interaction site prediction method called PAIRpred. Unlike most existing machine learning binding site prediction methods, PAIRpred uses information from both proteins in a protein complex to predict pairs of interacting residues from the two proteins. PAIRpred captures sequence and structure information about residue pairs through pairwise kernels that are used for training a support vector machine classifier. As a result, PAIRpred presents a more detailed model of protein binding, and offers state of the art accuracy in predicting binding sites at the protein level as well as inter-protein residue contacts at the complex level. We demonstrate PAIRpred's performance on Docking Benchmark 4.0 and recent CAPRI targets. We present a detailed performance analysis outlining the contribution of different sequence and structure features, together with a comparison to a variety of existing interface prediction techniques. We have also studied the impact of binding-associated conformational change on prediction accuracy and found PAIRpred to be more robust to such structural changes than existing schemes. As an illustration of the potential applications of PAIRpred, we provide a case study in which PAIRpred is used to analyze the nature and specificity of the interface in the interaction of human ISG15 protein with NS1 protein from influenza A virus. Python code for PAIRpred is available at http://combi.cs.colostate.edu/supplements/pairpred/.

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PAIRpred: Partner‐specific prediction of interacting residues from sequence and structure

Learning a distance metric from the given training samples plays a crucial role in many machine learning tasks, and various models and optimization algorithms have been proposed in the past decade. In this paper, we generalize several state-of-the-art metric learning methods, such as large margin nearest neighbor (LMNN) and information theoretic metric learning (ITML), into a kernel classification framework. First, doublets and triplets are constructed from the training samples, and a family of degree-2 polynomial kernel functions is proposed for pairs of doublets or triplets. Then, a kernel classification framework is established to generalize many popular metric learning methods such as LMNN and ITML. The proposed framework can also suggest new metric learning methods, which can be efficiently implemented, interestingly, using the standard support vector machine (SVM) solvers. Two novel metric learning methods, namely, doublet-SVM and triplet-SVM, are then developed under the proposed framework. Experimental results show that doublet-SVM and triplet-SVM achieve competitive classification accuracies with state-of-the-art metric learning methods but with significantly less training time.

A Kernel Classification Framework for Metric Learning

Deep learning-based models have been very successful in achieving state-of-the-art results in many of the computer vision, speech recognition, and natural language processing tasks in the last few years. These models seem a natural fit for handling the ever-increasing scale of biometric recognition problems, from cellphone authentication to airport security systems. Deep learning-based models have increasingly been leveraged to improve the accuracy of different biometric recognition systems in recent years. In this work, we provide a comprehensive survey of more than 120 promising works on biometric recognition (including face, fingerprint, iris, palmprint, ear, voice, signature, and gait recognition), which deploy deep learning models, and show their strengths and potentials in different applications. For each biometric, we first introduce the available datasets that are widely used in the literature and their characteristics. We will then talk about several promising deep learning works developed for that biometric, and show their performance on popular public benchmarks. We will also discuss some of the main challenges while using these models for biometric recognition, and possible future directions to which research in this area is headed.

Biometric Recognition Using Deep Learning: A Survey

Learning a distance metric from the given training samples plays a crucial role in many machine learning tasks, and various models and optimization algorithms have been proposed in the past decade. In this paper, we generalize several state-of-the-art metric learning methods, such as large margin nearest neighbor (LMNN) and information theoretic metric learning (ITML), into a kernel classification framework. First, doublets and triplets are constructed from the training samples, and a family of degree-2 polynomial kernel functions are proposed for pairs of doublets or triplets. Then, a kernel classification framework is established, which can not only generalize many popular metric learning methods such as LMNN and ITML, but also suggest new metric learning methods, which can be efficiently implemented, interestingly, by using the standard support vector machine (SVM) solvers. Two novel metric learning methods, namely doublet-SVM and triplet-SVM, are then developed under the proposed framework. Experimental results show that doublet-SVM and triplet-SVM achieve competitive classification accuracies with state-of-the-art metric learning methods such as ITML and LMNN but with significantly less training time.

Networks provide a natural representation of molecular biology knowledge, in particular to model relationships between biological entities such as genes, proteins, drugs, or diseases. Because of the effort, the cost, or the lack of the experiments necessary for the elucidation of these networks, computational approaches for network inference have been frequently investigated in the literature. In this paper, we examine the assessment of supervised network inference. Supervised inference is based on machine learning techniques that infer the network from a training sample of known interacting and possibly non-interacting entities and additional measurement data. While these methods are very effective, their reliable validation in silico poses a challenge, since both prediction and validation need to be performed on the basis of the same partially known network. Cross-validation techniques need to be specifically adapted to classification problems on pairs of objects. We perform a critical review and assessment of protocols and measures proposed in the literature and derive specific guidelines how to best exploit and evaluate machine learning techniques for network inference. Through theoretical considerations and in silico experiments, we analyze in depth how important factors influence the outcome of performance estimation. These factors include the amount of information available for the interacting entities, the sparsity and topology of biological networks, and the lack of experimentally verified non-interacting pairs.

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On protocols and measures for the validation of supervised methods for the inference of biological networks

Pairwise classification is the task to predict whether the examples a,b of a pair (a,b) belong to the same class or to different classes. In particular, interclass generalization problems can be treated in this way. In pairwise classification, the order of the two input examples should not affect the classification result. To achieve this, particular kernels as well as the use of symmetric training sets in the framework of support vector machines were suggested. The paper discusses both approaches in a general way and establishes a strong connection between them. In addition, an efficient implementation is discussed which allows the training of several millions of pairs. The value of these contributions is confirmed by excellent results on the labeled faces in the wild benchmark.

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Pairwise support vector machines and their application to large scale problems

Support vector regression SVR comprises techniques for estimating functions based on a set of labelled training data vectors. The generalization performance of a regression function generated by an SVR method can highly depend on the choice of certain hyper-parameters like kernel parameters. A common approach to determine suitable values for the hyper-parameters is to use cross validation and a grid search method. In recent works the problem of optimizing the hyper-parameters has been modelled as bilevel optimization problem. For the case of least squares SVR we reformulate the bilevel problem into a single-level nonlinear programme with box constraints that only depends on the hyper-parameters. Moreover, we show how the objective function and its derivatives can be calculated efficiently so that fast standard methods for the optimization of a large number of hyper-parameters become applicable. Reliability and computational efficiency of the approach is demonstrated by means of several test problems.

Efficient optimization of hyper-parameters for least squares support vector regression

Support Vector Machines have been applied to multiclass problems in several ways. Here, the task of predicting whether the examples u; v of a pair (u; v) belong to the same class or to different classes is considered. This task can be tackled by means of kernels that take two pairs of examples as arguments. The article provides a systematic treatment of such pairwise kernels. This enables applications with large numbers of examples and classes and an increased classification performance. Moreover, we present a new methodology for parameter tuning. The strength of these contributions is confirmed by excellent results on the labeled faces in the wild benchmark.

Thorsten Thies

Papers

Pairwise support vector machines and their application to large scale problems

Efficient optimization of hyper-parameters for least squares support vector regression

Pairwise Kernels, Support Vector Machines, and the Application to Large Scale Problems