Prioritization of disease microRNAs through a human phenome-microRNAome network

<p>Abstract</p> <p>Background</p> <p>The identification of disease-related microRNAs is vital for understanding the pathogenesis of diseases at the molecular level, and is critical for designing specific molecular tools for diagnosis, treatment and prevention. Experimental identification of disease-related microRNAs poses considerable difficulties. Computational analysis of microRNA-disease associations is an important complementary means for prioritizing microRNAs for further experimental examination.</p> <p>Results</p> <p>Herein, we devised a computational model to infer potential microRNA-disease associations by prioritizing the entire human microRNAome for diseases of interest. We tested the model on 270 known experimentally verified microRNA-disease associations and achieved an area under the ROC curve of 75.80%. Moreover, we demonstrated that the model is applicable to diseases with which no known microRNAs are associated. The microRNAome-wide prioritization of microRNAs for 1,599 disease phenotypes is publicly released to facilitate future identification of disease-related microRNAs.</p> <p>Conclusions</p> <p>We presented a network-based approach that can infer potential microRNA-disease associations and drive testable hypotheses for the experimental efforts to identify the roles of microRNAs in human diseases.</p

Komplex hálózatok a molekuláris biológiai szabályozásban = Complex networks in molecular biological regulation

A projektben molekuláris biológiai szabályozási hálózatok elemzését végeztük számítógépes biológiai (bioinformatikai) eszközökkel. Az első vizsgált szabályozási hálózat típus rövid szabályozó RNS-ek (mikroRNS-ek) és messenger RNS-ek kapcsolatait írja le, a második vizsgált hálózat típus sejten belüli jelátviteli kölcsönhatásokat ír le. Az első esetben azonosítottunk együtt szabályozó mikroRNS-ekből álló modulokat, és javaslatot tettünk konkrét kísérletekre, amelyekkel megállapítható, hogy az egyes mikroRNS-ek az emberi sejtek számára mennyire nélkülözhetőek. A második esetben összeállítottunk egy jelátviteli útvonal adatbázist, javaslatot tettünk (fontossági/szignifikancia sorrendben) új gyógyszer célpont fehérjékre. Továbbá előrelejeztünk a hálózatban résztvevő újabb fehérjéket, amelyek közül 6 fehérjére az előrejelzést kísérletekkel ellenőrizték társszerzők az ELTE Genetika tanszékén. | In this project we have analyzed molecular biological regulatory networks with computational biological tools. The first type of analyzed regulatory networks contains interactions between short regulating RNAs (microRNAs) and messenger RNAs, while the second type of regulatory networks contains intracellular signaling pathways. In the first case we have identified modules (groups) of microRNAs with highly similar regulatory tasks within each group, and proposed experiments to determine the relative level of dispensability of each human microRNA. In the second case have compiled a resource of intracellular signaling pathways and suggested (in order of significance) novel drug target proteins. Moreover, we have predicted novel pathway member proteins, of which 6 have been experimentally verified by co-authors at the Department of Genetics at Eotvos Universit

Protein-driven inference of miRNA-disease associations

Motivation: MicroRNAs (miRNAs) are a highly abundant class of non-coding RNA genes involved in cellular regulation and thus also diseases. Despite miRNAs being important disease factors, miRNA–disease associations remain low in number and of variable reliability. Furthermore, existing databases and prediction methods do not explicitly facilitate forming hypotheses about the possible molecular causes of the association, thereby making the path to experimental follow-up longer. Results: Here we present miRPD in which miRNA–Protein–Disease associations are explicitly inferred. Besides linking miRNAs to diseases, it directly suggests the underlying proteins involved, which can be used to form hypotheses that can be experimentally tested. The inference of miRNAs and diseases is made by coupling known and predicted miRNA–protein associations with protein–disease associations text mined from the literature. We present scoring schemes that allow us to rank miRNA–disease associations inferred from both curated and predicted miRNA targets by reliability and thereby to create high- and medium-confidence sets of associations. Analyzing these, we find statistically significant enrichment for proteins involved in pathways related to cancer and type I diabetes mellitus, suggesting either a literature bias or a genuine biological trend. We show by example how the associations can be used to extract proteins for disease hypothesis. Availability and implementation: All datasets, software and a searchable Web site are available at http://mirpd.jensenlab.org. Contact: [email protected] or [email protected]

Characteristics of binding sites of intergenic, intronic and exonic miRNAs with mRNAs of oncogenes coding intronic miRNAs

International audienceThe interaction of 784 intergenic (ig-miRNA), 686 intronic (in-miRNA) and 49 exonic miRNAs (ex-miRNA) with mRNAs of 51 oncogenes coding in-miRNAs was investigated. Out of the studied genes, 44 were targets for 94 ig-miRNAs, 29 were targets for 44 in-miRNAs and 7 were targets for 7 ex-miRNAs. The density of miRNA binding sites was higher in 5'-untranslated regions than it was in coding sequences and 3'-untranslated regions. Three types of miRNA interaction with mRNA were revealed: 5'-dominant canonical, 3'-compensatory and complementary types. In-miRNAs do not interact with mRNAs of host genes (where in-miRNA is encoded). Linkage between some mRNAs of genes encodes in-miRNAs via other in-miRNAs was revealed. These data promote the understanding of interaction mechanism of miRNA with mRNA genes participating in gastrointestinal and breast cancers.L'interaction de miRNAs avec des mRNAs de 51 incogènes a été étudiée, Trois types d'interaction ont été mis en évidence. Ces données améliorent la compréhension du mécanisme d'interaction des miRNAs avec des gènes mRNAs impliqués dans des cancers de l'intestin ou du sein

Characteristics of Intronic and Intergenic Human miRNAs and Features of their Interaction with mRNA

International audienceRegulatory relationships of 686 intronic miRNA and 784 intergenic miRNAs with mRNAs of 51 intronic miRNA coding genes were established. Interaction features of studied miRNAs with 5'UTR, CDS and 3'UTR of mRNA of each gene were revealed. Functional regions of mRNA were shown to be significantly heterogenous according to the number of binding sites of miRNA and to the location density of these sites.On a etabli une relation de regulation entre les miARN -intronique 686 et intergénique 784 et des ARNm de 51 genes. Des propriétés d'interaction des miARN avec les zones 5'UTR, CDS et 3'UTR des RNAm ont été mises en évidence. On a montré une hétérogénéité des régions fonctionnelles des ARNm par rapport au nombre de sites de fixation des miARM et à leur densité

Random walks on mutual microRNA-target gene interaction network improve the prediction of disease-associated microRNAs

Background: MicroRNAs (miRNAs) have been shown to play an important role in pathological initiation, progression and maintenance. Because identification in the laboratory of disease-related miRNAs is not straightforward, numerous network-based methods have been developed to predict novel miRNAs in silico. Homogeneous networks (in which every node is a miRNA) based on the targets shared between miRNAs have been widely used to predict their role in disease phenotypes. Although such homogeneous networks can predict potential disease-associated miRNAs, they do not consider the roles of the target genes of the miRNAs. Here, we introduce a novel method based on a heterogeneous network that not only considers miRNAs but also the corresponding target genes in the network model. Results: Instead of constructing homogeneous miRNA networks, we built heterogeneous miRNA networks consisting of both miRNAs and their target genes, using databases of known miRNA-target gene interactions. In addition, as recent studies demonstrated reciprocal regulatory relations between miRNAs and their target genes, we considered these heterogeneous miRNA networks to be undirected, assuming mutual miRNA-target interactions. Next, we introduced a novel method (RWRMTN) operating on these mutual heterogeneous miRNA networks to rank candidate disease-related miRNAs using a random walk with restart (RWR) based algorithm. Using both known disease-associated miRNAs and their target genes as seed nodes, the method can identify additional miRNAs involved in the disease phenotype. Experiments indicated that RWRMTN outperformed two existing state-of-the-art methods: RWRMDA, a network-based method that also uses a RWR on homogeneous (rather than heterogeneous) miRNA networks, and RLSMDA, a machine learning-based method. Interestingly, we could relate this performance gain to the emergence of "disease modules" in the heterogeneous miRNA networks used as input for the algorithm. Moreover, we could demonstrate that RWRMTN is stable, performing well when using both experimentally validated and predicted miRNA-target gene interaction data for network construction. Finally, using RWRMTN, we identified 76 novel miRNAs associated with 23 disease phenotypes which were present in a recent database of known disease-miRNA associations. Conclusions: Summarizing, using random walks on mutual miRNA-target networks improves the prediction of novel disease-associated miRNAs because of the existence of "disease modules" in these networks

Prediction of miRNA-disease associations with a vector space model

MicroRNAs play critical roles in many physiological processes. Their dysregulations are also closely related to the development and progression of various human diseases, including cancer. Therefore, identifying new microRNAs that are associated with diseases contributes to a better understanding of pathogenicity mechanisms. MicroRNAs also represent a tremendous opportunity in biotechnology for early diagnosis. To date, several in silico methods have been developed to address the issue of microRNA-disease association prediction. However, these methods have various limitations. In this study, we investigate the hypothesis that information attached to miRNAs and diseases can be revealed by distributional semantics. Our basic approach is to represent distributional information on miRNAs and diseases in a high-dimensional vector space and to define associations between miRNAs and diseases in terms of their vector similarity. Cross validations performed on a dataset of known miRNA-disease associations demonstrate the excellent performance of our method. Moreover, the case study focused on breast cancer confirms the ability of our method to discover new disease-miRNA associations and to identify putative false associations reported in databases

2K09 and thereafter : the coming era of integrative bioinformatics, systems biology and intelligent computing for functional genomics and personalized medicine research

Significant interest exists in establishing synergistic research in bioinformatics, systems biology and intelligent computing. Supported by the United States National Science Foundation (NSF), International Society of Intelligent Biological Medicine (http://www.ISIBM.org), International Journal of Computational Biology and Drug Design (IJCBDD) and International Journal of Functional Informatics and Personalized Medicine, the ISIBM International Joint Conferences on Bioinformatics, Systems Biology and Intelligent Computing (ISIBM IJCBS 2009) attracted more than 300 papers and 400 researchers and medical doctors world-wide. It was the only inter/multidisciplinary conference aimed to promote synergistic research and education in bioinformatics, systems biology and intelligent computing. The conference committee was very grateful for the valuable advice and suggestions from honorary chairs, steering committee members and scientific leaders including Dr. Michael S. Waterman (USC, Member of United States National Academy of Sciences), Dr. Chih-Ming Ho (UCLA, Member of United States National Academy of Engineering and Academician of Academia Sinica), Dr. Wing H. Wong (Stanford, Member of United States National Academy of Sciences), Dr. Ruzena Bajcsy (UC Berkeley, Member of United States National Academy of Engineering and Member of United States Institute of Medicine of the National Academies), Dr. Mary Qu Yang (United States National Institutes of Health and Oak Ridge, DOE), Dr. Andrzej Niemierko (Harvard), Dr. A. Keith Dunker (Indiana), Dr. Brian D. Athey (Michigan), Dr. Weida Tong (FDA, United States Department of Health and Human Services), Dr. Cathy H. Wu (Georgetown), Dr. Dong Xu (Missouri), Drs. Arif Ghafoor and Okan K Ersoy (Purdue), Dr. Mark Borodovsky (Georgia Tech, President of ISIBM), Dr. Hamid R. Arabnia (UGA, Vice-President of ISIBM), and other scientific leaders. The committee presented the 2009 ISIBM Outstanding Achievement Awards to Dr. Joydeep Ghosh (UT Austin), Dr. Aidong Zhang (Buffalo) and Dr. Zhi-Hua Zhou (Nanjing) for their significant contributions to the field of intelligent biological medicine

LLCMDA: A Novel Method for Predicting miRNA Gene and Disease Relationship Based on Locality-Constrained Linear Coding

MiRNAs are small non-coding regulatory RNAs which are associated with multiple diseases. Increasing evidence has shown that miRNAs play important roles in various biological and physiological processes. Therefore, the identification of potential miRNA-disease associations could provide new clues to understanding the mechanism of pathogenesis. Although many traditional methods have been successfully applied to discover part of the associations, they are in general time-consuming and expensive. Consequently, computational-based methods are urgently needed to predict the potential miRNA-disease associations in a more efficient and resources-saving way. In this paper, we propose a novel method to predict miRNA-disease associations based on Locality-constrained Linear Coding (LLC). Specifically, we first reconstruct similarity networks for both miRNAs and diseases using LLC and then apply label propagation on the similarity networks to get relevant scores. To comprehensively verify the performance of the proposed method, we compare our method with several state-of-the-art methods under different evaluation metrics. Moreover, two types of case studies conducted on two common diseases further demonstrate the validity and utility of our method. Extensive experimental results indicate that our method can effectively predict potential associations between miRNAs and diseases