Hybrid Approach of Relation Network and Localized Graph Convolutional Filtering for Breast Cancer Subtype Classification

Network biology has been successfully used to help reveal complex mechanisms of disease, especially cancer. On the other hand, network biology requires in-depth knowledge to construct disease-specific networks, but our current knowledge is very limited even with the recent advances in human cancer biology. Deep learning has shown a great potential to address the difficult situation like this. However, deep learning technologies conventionally use grid-like structured data, thus application of deep learning technologies to the classification of human disease subtypes is yet to be explored. Recently, graph based deep learning techniques have emerged, which becomes an opportunity to leverage analyses in network biology. In this paper, we proposed a hybrid model, which integrates two key components 1) graph convolution neural network (graph CNN) and 2) relation network (RN). We utilize graph CNN as a component to learn expression patterns of cooperative gene community, and RN as a component to learn associations between learned patterns. The proposed model is applied to the PAM50 breast cancer subtype classification task, the standard breast cancer subtype classification of clinical utility. In experiments of both subtype classification and patient survival analysis, our proposed method achieved significantly better performances than existing methods. We believe that this work is an important starting point to realize the upcoming personalized medicine.Comment: 8 pages, To be published in proceeding of IJCAI 201

Differential network biology

Protein and genetic interaction maps have typically been generated under a single condition, providing a static view of the interactome. Recent studies employing differential analysis, however, have revealed that widespread re-wiring of the interactome underlies key biological responses

Elucidating Signal Transduction Modulatory Drug Target Network of Colon Cancer: A Network Biology Approach

Latest evaluation and validation of cancer drugs and their targets has demonstrated the lack and inadequate development of new and better drugs, based on available protocols. Even though the specificity of drug targets is a great challenge in the pharmaco-proteomics field of cancer biology, for eradicating such hurdles and paving the way for the drugs of future, a novel step has been envisaged here to study the relation between drug target network and the corresponding drug network using the advanced concepts of proteomics and network biology. The literature mining was done for the collection of receptors and the ligands. About 1000 natural compounds were collected and out of those 300 molecules showed anti-cancer activity against colon cancer. Ligand Vs multiple receptor docking was done using the software Quantum 3.3.0; the results were further used for the designing of a well connected Protein Ligand Interaction (PLI) network of colon cancer. The obtained network is then extrapolated to sort out the receptors expressed in the specific cancer type. The network is then statistically analyzed and represented by the graphical interpretation, in order to ascertain the hub nodes and their locally parsed neighbours. Based on the best docking scores, the graphs obtained from the docking analysis are statistically validated with the help of VisANT. In the network three hub nodes Neutrophil cytosol factor 2, UV excision repair protein RAD23 homolog A, & Receptor-type tyrosine-protein phosphatase eta were identified, which showed the highest interaction with the ligands. Butyrate and Farnesol showed highest interaction as ligands. Multiple Sequence Alignment was done of the binding site sequence of the drug targets to find out the evolutionary closeness of the binding sites. The phylogenetic tree was also constructed to further validate the observation. Further in-vitro and in-vivo studies needs to be done to analyse the receptor specificity and anti tumor activity of these compounds in Colon cancer

A network biology approach to prostate cancer

There is a need to identify genetic mediators of solid-tumor cancers, such as prostate cancer, where invasion and distant metastases determine the clinical outcome of the disease. Whole-genome expression profiling offers promise in this regard, but can be complicated by the challenge of identifying the genes affected by a condition from the hundreds to thousands of genes that exhibit changes in expression. Here, we show that reverse-engineered gene networks can be combined with expression profiles to compute the likelihood that genes and associated pathways are mediators of a disease. We apply our method to non-recurrent primary and metastatic prostate cancer data, and identify the androgen receptor gene (AR) among the top genetic mediators and the AR pathway as a highly enriched pathway for metastatic prostate cancer. These results were not obtained on the basis of expression change alone. We further demonstrate that the AR gene, in the context of the network, can be used as a marker to detect the aggressiveness of primary prostate cancers. This work shows that a network biology approach can be used advantageously to identify the genetic mediators and mediating pathways associated with a disease

Structural Measures for Network Biology Using QuACN

Background: Structural measures for networks have been extensively developed, but many of them have not yet demonstrated their sustainably. That means, it remains often unclear whether a particular measure is useful and feasible to solve a particular problem in network biology. Exemplarily, the classification of complex biological networks can be named, for which structural measures are used leading to a minimal classification error. Hence, there is a strong need to provide freely available software packages to calculate and demonstrate the appropriate usage of structural graph measures in network biology. Results: Here, we discuss topological network descriptors that are implemented in the R-package QuACN and demonstrate their behavior and characteristics by applying them to a set of example graphs. Moreover, we show a representative application to illustrate their capabilities for classifying biological networks. In particular, we infer gene regulatory networks from microarray data and classify them by methods provided by QuACN. Note that QuACN is the first freely available software written in R containing a large number of structural graph measures. Conclusion: The R package QuACN is under ongoing development and we add promising groups of topological network descriptors continuously. The package can be used to answer intriguing research questions in network biology, e.g., classifying biological data or identifying meaningful biological features, by analyzing the topology o

Networks for all

A report on the Cold Spring Harbor Laboratory/Wellcome Trust conference on Network Biology, Hinxton, UK, 27-31 August 2008