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A key step in network analysis is to partition a complex network into dense modules. Currently, modularity is one of the most popular benefit functions used to partition network modules. However, recent studies suggested that it has an inherent limitation in detecting dense network modules. In this study, we observed that despite the limitation, modularity(More)
Learning network structure underlying data is an important problem in machine learning. This paper presents a novel degree prior to study the inference of scale-free networks, which are widely used to model social and biological networks. In particular, this paper formulates scale-free network inference using Gaussian Graphical model (GGM) regularized by a(More)
Learning the structure of a graphical model is a fundamental problem and it is used extensively to infer the relationship between random variables. In many real world applications, we usually have some prior knowledge about the underlying graph structure, such as degree distribution and block structure. In this paper, we propose a novel generative model for(More)
Learning the structure of a probabilistic graphical models is a well studied problem in the machine learning community due to its importance in many applications. Current approaches are mainly focused on learning the structure under restrictive parametric assumptions, which limits the applicability of these methods. In this paper, we study the problem of(More)
MOTIVATION Protein contacts contain key information for the understanding of protein structure and function and thus, contact prediction from sequence is an important problem. Recently exciting progress has been made on this problem, but the predicted contacts for proteins without many sequence homologs is still of low quality and not very useful for de(More)
Deep Convolutional Neural Networks (DCNN) has shown excellent performance in a variety of Machine Learning tasks. Here we employ Deep Convolutional Neural Fields (DeepCNF), which is a combination of DCNN with Conditional Random Field (CRF), to address the task of protein sequence labelling for the prediction of solvent accessibility (ACC), order/disorder(More)
Learning the network structure underlying data is an important problem in machine learning. This paper introduces a novel prior to study the inference of scale-free networks, which are widely used to model social and biological networks. The prior not only favors a desirable global node degree distribution, but also takes into consideration the relative(More)
Gaussian graphical models (GGMs) are widely-used to describe the relationship between random variables. In many real-world applications, GGMs have a block structure in the sense that the variables can be clustered into groups so that inter-group correlation is much weaker than intra-group correlation. We present a novel nonparametric Bayesian generative(More)
—Protein contacts contain important information for protein structure and functional study, but contact prediction from sequence information remains very challenging. Recently evolutionary coupling (EC) analysis, which predicts contacts by detecting co-evolved residues (or columns) in a multiple sequence alignment (MSA), has made good progress due to better(More)
Structured high-cardinality data arises in many domains, and poses a major challenge for both modeling and inference. Graphical models are a popular approach to modeling structured data but they are unsuitable for high-cardinality variables. The count-min (CM) sketch is a popular approach to estimating probabilities in high-cardinality data but it does not(More)