Behnam Neyshabur

NETAL: a new graph-based method for global alignment of protein–protein interaction networks

MOTIVATION The interactions among proteins and the resulting networks of such interactions have a central role in cell biology. Aligning these networks gives us important information, such as conserved complexes and evolutionary relationships. Although there have been several publications on the global alignment of protein networks; however, none of proposed methods are able to produce a highly conserved and meaningful alignment. Moreover, time complexity of current algorithms makes them impossible to use for multiple alignment of several large networks together. RESULTS We present a novel algorithm for the global alignment of protein-protein interaction networks. It uses a greedy method, based on the alignment scoring matrix, which is derived from both biological and topological information of input networks to find the best global network alignment. NETAL outperforms other global alignment methods in terms of several measurements, such as Edge Correctness, Largest Common Connected Subgraphs and the number of common Gene Ontology terms between aligned proteins. As the running time of NETAL is much less than other available methods, NETAL can be easily expanded to multiple alignment algorithm. Furthermore, NETAL overpowers all other existing algorithms in term of performance so that the short running time of NETAL allowed us to implement it as the first server for global alignment of protein-protein interaction networks. AVAILABILITY Binaries supported on linux are freely available for download at http://www.bioinf.cs.ipm.ir/software/netal. SUPPLEMENTARY INFORMATION Supplementary data are available at Bioinformatics online.

Clustering, Hamming Embedding, Generalized LSH and the Max Norm

We study the convex relaxation of clustering and hamming embedding, focusing on the asymmetric case (co-clustering and asymmetric hamming embedding), understanding their relationship to LSH as studied by Charikar (2002) and to the max-norm ball, and the differences between their symmetric and asymmetric versions.

Predicting protein–protein interactions through sequence-based deep learning

Motivation High‐throughput experimental techniques have produced a large amount of protein‐protein interaction (PPI) data, but their coverage is still low and the PPI data is also very noisy. Computational prediction of PPIs can be used to discover new PPIs and identify errors in the experimental PPI data. Results We present a novel deep learning framework, DPPI, to model and predict PPIs from sequence information alone. Our model efficiently applies a deep, Siamese‐like convolutional neural network combined with random projection and data augmentation to predict PPIs, leveraging existing high‐quality experimental PPI data and evolutionary information of a protein pair under prediction. Our experimental results show that DPPI outperforms the state‐of‐the‐art methods on several benchmarks in terms of area under precision‐recall curve (auPR), and computationally is more efficient. We also show that DPPI is able to predict homodimeric interactions where other methods fail to work accurately, and the effectiveness of DPPI in specific applications such as predicting cytokine‐receptor binding affinities. Availability and implementation Predicting protein‐protein interactions through sequence‐based deep learning): https://github.com/hashemifar/DPPI/. Supplementary information Supplementary data are available at Bioinformatics online.

Joint inference of tissue-specific networks with a scale free topology

High-throughput experimental techniques have produced an enormous number of gene expression profiles for various tissues of the human body. Tissue-specificity is a key component in reflecting the potentially different roles of proteins in diverse cell lineages. One way of understanding the tissue specificity is by reconstructing the tissue-specific co-expression networks (CENs) to analyze the correlation between genes. A few methods have been developed for estimating CENs, but it still remains challenging in terms of both accuracy and efficiency. In this paper we propose a new method, JointNet, for predicting tissue-specific co-expression networks. JointNet is exploiting the observation that, functionally related tissues have similar expression patterns and thus, similar networks. It uses different node penalties for hubs and non-hub nodes to accurately estimate the scale-free networks. Our experimental results show that the resulting tissue-specific CENs are accurate and that our method outperforms the current state of the art.