Volume 6 Supplement 3
Inferring drug-disease associations from integration of chemical, genomic and phenotype data using network propagation
© Huang et al; licensee BioMed Central Ltd. 2013
Published: 11 November 2013
During the last few years, the knowledge of drug, disease phenotype and protein has been rapidly accumulated and more and more scientists have been drawn the attention to inferring drug-disease associations by computational method. Development of an integrated approach for systematic discovering drug-disease associations by those informational data is an important issue.
We combine three different networks of drug, genomic and disease phenotype and assign the weights to the edges from available experimental data and knowledge. Given a specific disease, we use our network propagation approach to infer the drug-disease associations.
We apply prostate cancer and colorectal cancer as our test data. We use the manually curated drug-disease associations from comparative toxicogenomics database to be our benchmark. The ranked results show that our proposed method obtains higher specificity and sensitivity and clearly outperforms previous methods. Our result also show that our method with off-targets information gets higher performance than that with only primary drug targets in both test data.
We clearly demonstrate the feasibility and benefits of using network-based analyses of chemical, genomic and phenotype data to reveal drug-disease associations. The potential associations inferred by our method provide new perspectives for toxicogenomics and drug reposition evaluation.
Disease an intricate phenotype is usually caused by congenital disorder or dysfunctions of abnormal genes which induce multi-factor-driven alterations and disrupt functional modules . Drugs achieve their therapeutic effect by changing downstream processes of their targets which contend with the alterations of those abnormal genes. The previous reports also showed that pharmaceutical company takes approximately 15 years and over $1 billion to develop a novel drug into the market and more than 90% of experimental drugs fail to move beyond the early clinical test stages [2, 3]. Because drug discovery is complexity, time-consuming process and there are odds of low therapeutic efficacy and/or unacceptable toxicity [4, 5]. With the merits of shorting development time and reducing risk, more and more scientists have been drawn attention to inferring drug-disease associations by computational method. Development of an integrated approach for systematic discovering those associations is necessary.
Several studies investigated some methods to increase the efficacy of drug discovery and they found there are positive and negative relationships between existing drugs and disease phenotypes. The Comparative Toxicogenomics Database (CTD; http://ctd.mdibl.org) is the public database which inferred chemical-disease associations by manually curated chemical-gene interactions, and gene-disease relationships from published literature . Cheng also presented a comprehensive predicted database of chemical -gene-disease associations (PredCTD) by integrating the information from chemical, gene, and disease . Pharmacogenetics and pharmacogenomics knowledge base (PharmGKB) is a repository which contains the relationships between genomics, drug-response and its related phenotype and clinical information . Eichborn developed PROMISCUOUS database which includes network-based resources of protein-protein and protein-drug interactions, side-effects and structural information .
The high-throughput microarray technology plays an important role in investigating drug-disease associations by providing a genome-side monitoring of gene expression in the past decade. Some methods aims at restoring the abnormal state to normal state which means the expressions of the transcriptional level induced by drug should reverse those under disease state. On the other hand, if the differential expression profile under drug exposure and disease states is significantly anti-correlated, the drug compounds may have the potential to cure that disease. The Connectivity Map (Cmap) project is one of the most comprehensive and systematic approaches for drug-disease associations . The Cmap provided a reference collection of genome-wide gene expressions profiles among drugs, which were obtained by systematically exposing to few key cell lines . Drug compounds negatively correlated to disease-specific gene signature may be the candidate therapeutic for further investigation. On the other hand, drug compounds positively correlated to gene signature are able to induce the disease phenotype. Li built disease-specific drug-protein associations derived from the Cmap by integrating gene/protein and drug connectivity information based on protein interaction network (PIN) and literature mining from PubMed abstracts . Previous research used the "guilt by association" (GBA) approach, which assumed that when two diseases share similar therapies then the drug treats only one of the two might be also treat another, to predict novel drug-disease associations . With a gold standard set of the drug-disease associations, Gottlieb designed a novel computational method called PREDICT to identify drug-disease associations and also predict new drug indications based on their features including chemical structure, side effects, gene expression profile, and chemical-protein interactome . However, to build an accurate prediction model based on different feature must have the positive and negative data to infer drug-disease associations. There are some technically difficulties to obtain negative data such as non-drug targets due to the lack of value of research. Except learning a classifier to predict the associations between drugs and disease, network- based approach has been widely used to infer the relationships. In genetic and molecular biology, increasing evidences suggested that common functional modules are not affected by an individual gene but usually are organized by a group of interacting genes underlie similar diseases, which point out the therapeutic importance of those modules . Therefore, the other basic hypothesis is that the mechanism of the drug and disease in the pathological processes may share similar functional modules. Daminelli created a drug-target-disease network and mined the bi-cliques where every drug is linked to every target and disease . If the known data form an incomplete bi-clique, the incomplete relations in the bi-cliques to be identified as predicted links between drugs and diseases. Ye integrated known drug target information and proposed a disease-oriented strategy for evaluating the relationships between drugs and a specific disease based on their pathway profile .
The huge amount of chemical, genomic and disease phenotype data is rapidly accumulated, but the drug-diseases associations are still not clear. For this purpose, we design a method of inferring drug- protein/gene-disease phenotype relationships with a network propagation model, where genes with similar functional modules are related to not only drugs but also the disease phenotype.
Construct phenotype homo-network
A node in a phenotype homo-network as a disease phenotype is extracted from Online Mendelian Inheritance in Man (OMIM) database . We use a scoring schema of phenotypic similarities as edges that quantitatively measures the phenotypic overlap of OMIM records constructed by van Driel  using text mining techniques. If the similarity score of two diseases falls in the range [0.6, 1], it means informative similarity which indicates potentially relevant phenotypic similarity. On the other hand, if the similarity score falls within [0, 0.3], it indicates non-informative similarity. Therefore, we apply a logistic function from  to convert the phenotypic similarity scores among diseases into a value as close to 1 as possible while the non-informative score into a value as close to 0 possible over all the entries in the phenotype similarity score matrix. The symmetric similarity matrix W p (p i ,p j ) in phenotype homo-network denotes the phenotypic similarity score between phenotypes p i and p j .
Construct drug homo-network using chemical similarity
We extract the FDA-approved drugs and their canonical simplified molecular input line entry specification (SMILES) from DrugBank database . We calculate the hashed fingerprints using Chemical Development Kit (CDK) . The chemical similarities are calculated by two hashed fingerprints using Tanimoto coefficient . It calculates the size of the common substructures over the union between two fingerprints of the drugs which is defined as sim(x, x')=|x∩x'|/|x∪x'| between two chemical structures of drug x and x'. The symmetric chemical structures similarity matrix as the edge weight in drug homo-network is denoted as W d and each value falls in the range between zero (no bits in common) to unity (all bits the same)
Construct protein interaction homo-network using gene expression data
where W g (g i ,g j ) denotes the weight function from gene g i to gene g j . denotes the absolute value of Pearson correlation coefficient of the interaction between gene g i and g j from case data. and are the average gene expression values of gene g i in case sample and control sample.
Integrated disease, protein interactions and chemical homo-networks
The gene-phenotype hetero-network shows the relationships between disease phenotype and disease-associated genes extracted from OMIM database . The drug-protein hetero-network denotes the drug and its targets which is obtained from DrugBank database . The asymmetric matrices W pg , W dg represent the adjacency matrices of link structures from phenotype-gene relationships and drug-target protein interactions, respectively. If drug d i has a target g j , then W dg (d i , g j ) = 1, otherwise W dg (d i , g j ) = 0. When a drug target or disease-associated gene has no link with other proteins in PIN, we set the probability of connection to any other protein as 1/(n-1), where n is the total number of proteins in PIN. Since n is usually very large, so the probability will be very small. The reason that we use small probability instead of zero probability is to prevent a node in the network becoming a "sink node" in PIN and allows the probability to be propagated through the node.
Network propagation in the integrated network
We identify the inferring drug-disease associations problem as probability propagation over a network which simulates a random walker stochastically move on query phenotype to its immediate neighbors in heterogeneous network [26, 27]. We adopt the idea from  which developed a label propagation algorithm for an integrated network.
where i denotes the node in a homo-network and j denotes the node in the other homo-network.
The first term denotes the random walker can "restarts" to the initial probability distribution among the nodes with the diffusion parameter 1-α. The second term denotes an iterative walk to reach the further nodes in the network based on the transition matrix S and a diffusion parameter α. The random walker will be trapped at initial nodes if α is zero. Let P t be a probability distribution where a node in the network holds the probability of finding itself in the iterative random walker process up to the step t. After certain steps, the probabilities will reach a steady state which the difference between P t and P t-1 measured by L2 norm falls below a very small value such as 10-9.
Thus, network propagation is calculated with an enriched initialization from the other homo-networks through hetero-subnetworks and the proof of convergence is in .
Given a query phenotype, we first set the initial probability distribution over nodes where the probabilities to the query disease nodes set to one and other nodes in the other homo-networks to zero. Second, we apply our network propagation method iteratively until the probability converges on each homo-network. Finally, we use the coverged probabilities of the nodes in homo-networks as initail probability distribution and then repeated the network propagation processes until all homo-networks converge to a final probability distribution.
Evaluation of association specificity between drug and disease
Here, P i (v) denote the probability of node v in homo-network i using genomic data calculated by our method. The functions avg and std denote the average and standard deviation for the set of reference probabilities P i ref in homo-network i respectively.
Gene expression profile
We adopt microarray data taken from  that consists of 62 primary prostate tumors and 41 normal tissues from Stanford Microarray Database (SMD) . We use genome-wide gene expression profiles from tissue samples of 18 healthy normal controls and 27 patients with colorectal cancer evaluated by HG-U133 Plus 2.0 platform microarrays (Affymetrix, Santa Clara) through from Gene Expression Omnibus (GEO) database (GSE4183 and GSE4107) [35, 36].
Protein interaction network
We successfully obtained 137,037 interactions among 13,388 genes by integrating five protein interaction network databases (HPRD, BIND, IntAct, MINT, and OPHID) and by mapping the UniProt protein ID to the human Entrez gene ID, erase the duplicated interaction pairs.
Phenotype network and Phenotype-genotype hetero-network
The OMIM database constructed the catalogue of genetic diseases in human and provides the phenotype-genotype association for 14,433 genes and 5,080 diseases . The gene-phenotype hetero-network contains 275 disease phenotypes and 649 genes from 877 relations while mapping the genes in microarray data and PIN.
Drug network and drug-target hetero-network
We collect 1,571 FDA-approved drugs and 1,410 of them with available SMILES data in DrugBank database . There are 4,456 relations between 1,215 drugs and 1,141 targets to be the drug-target hetero-network.
Benchmark of drug-disease associations
We extract 53 and 106 known associations between drug, prostate cancer and colorectal cancer extracted from CTD database  in May 2013 as our benchmark.
The performance of our method
The number of gene signature with different fold changes in prostate cancer and colorectal cancer
# over-expressed genes
# down-expressed genes
The AUC with varying diffusion parameters
The performance of our method with different data source
Case study: prostate cancer
Potential drug and prostate cancer relations
Drug-prostate cancer associations
The significant functional modules related to the drug-prostate cancer association
Functional modules related to the drug-prostate cancer associations
Fanconi anemia pathway
Huntington's disease pathway
p53 signaling pathway
BRCA1, BRCA2 and ATR pathway
Negative regulation of cell proliferation
Alzheimer's disease pathway
Pancreatic cancer pathway
Case study: colorectal cancer
Potential drug and colorectal cancer relations
Drug-colorectal cancer associations
The significant functional modules related to the drug-colorectal cancer association
Functional modules related to the drug-colorectal cancer associations
KEGG, BIOCARTA, REACTOME
Toll like receptor pathway
Renal cell carcinoma
Chronic myeloid leukemia
VEGF signaling pathway
Innate Immune System
Fc epsilon RI signaling pathway
Regulation of ornithine decarboxylase (ODC)
Wnt signaling pathway
Neurotrophin signaling pathway
We integrate the information of drug, genomic and disease phenotype from available experimental data and knowledge as weighted networks and their connected relationships together. We apply disease-oriented network propagation approach for inferring and evaluating the likelihood of the probability between drugs and query disease. In our experiment, we adopt the prostate cancer and colorectal cancer as our case study and the results clearly outperform previous Cmap project. Our results are also found to be significantly enriched in both the biomedical literature and clinical trials. The success of our methods can be attributed as follows: First, we integrate heterogeneous data and knowledge about disease phenotype, chemical structure of drugs, and gene expression into our model. Second, our network propagation method combines the information not only from the single network but also derived the information from other connected homo-networks to infer the drug-disease association. Finally, our method with off-targets information gets higher performance than that with only primary drug targets in both test data. We believe that the combination of network and heterogeneous data source could help us to generate new hypotheses to infer the drug-disease associations and even speed up the drug development processes. Our study provides opportunities for future toxicogenomics and drug discovery applications but the limitation is the difficulty in distinguishing the positive and negative associations between drug and disease. In the future, we can choose different methods to calculate the chemical structural similarity between drugs, which could improve the limitations by using Tanimoto coefficient. On the other hand, our approach heavily relies on the weights for the edges in each network derived from the existing knowledge of drugs, targets, protein, disease or reported databases, or experimental results from the public database and the incompleteness of such information would limit our prediction power. We can also integrate various data sources such as drug response profile, side effect and pharmacological data and therapeutic/toxicological expression profiles to verify the reliability and confidence of the interactions.
This research is partially supported by the Bioresources Collection and Research Center of Linko Chang Gung Memorial Hospital and National Tsing Hua University of Taiwan R. O. C. under the grant number 101N2761E1. Based on "Network-based inferring drug-disease associations from chemical, genomic and phenotype data", by Yu-Fen Huang, Hsiang-Yuan Yeh, Von-Wun Soo which appeared in Bioinformatics and Biomedicine (BIBM), 2012 IEEE International Conference on. © 2012 IEEE [10.1109/BIBM.2012.6392658].
The publication costs for this article were funded by the corresponding author.
This article has been published as part of BMC Medical Genomics Volume 6 Supplement 3, 2013: Selected articles from the IEEE International Conference on Bioinformatics and Biomedicine 2012: Medical Genomics. The full contents of the supplement are available online at http://www.biomedcentral.com/bmcmedgenomics/supplements/6/S3.
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