Datasets for constructing the miRNA-disease association prediction models. Four datasets such as three positive sample sets “positive_miR”, “positive_HMDD” and “positive_miRcancer” and the negative sample set “negative_expression” are stored. The three positive sample sets are retrieved from the three existing databases such as miR2Disease, HMDD v2 and miRCancer, while the negative sample set was obtained via analyzing the expression of the miRNAs.This file can also be downloaded from: https://drive.google.com/open?id=0B6lH3mKdA9CSTkg2OVBPS0ZfVnM . (XLS 947 kb

Chaowang Lan (3861172)

Dacheng Tao (331902)

Gyorgy Hutvagner (3394115)

Hui Peng (116756)

Jinyan Li (24722)

Yi Zheng (113896)

PubMed

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Additional file 8 of Cross disease analysis of co-functional microRNA pairs on a reconstructed network of disease-gene-microRNA tripartite

The GSE accessions for extracting negative samples. There are totally 78 GSE accessions that we downloaded from the GEO database. We analyzed these files to compute the fold changes of the miRNAs according to the given platform information.This file can also be downloaded from: https://drive.google.com/open?id=0B6lH3mKdA9CSTkg2OVBPS0ZfVnM . (XLS 28 kb

Additional file 7 of Cross disease analysis of co-functional microRNA pairs on a reconstructed network of disease-gene-microRNA tripartite

© 2017 The Author(s). Background: MicroRNAs always function cooperatively in their regulation of gene expression. Dysfunctions of these co-functional microRNAs can play significant roles in disease development. We are interested in those multi-disease associated co-functional microRNAs that regulate their common dysfunctional target genes cooperatively in the development of multiple diseases. The research is potentially useful for human disease studies at the transcriptional level and for the study of multi-purpose microRNA therapeutics. Methods and results: We designed a computational method to detect multi-disease associated co-functional microRNA pairs and conducted cross disease analysis on a reconstructed disease-gene-microRNA (DGR) tripartite network. The construction of the DGR tripartite network is by the integration of newly predicted disease-microRNA associations with those relationships of diseases, microRNAs and genes maintained by existing databases. The prediction method uses a set of reliable negative samples of disease-microRNA association and a pre-computed kernel matrix instead of kernel functions. From this reconstructed DGR tripartite network, multi-disease associated co-functional microRNA pairs are detected together with their common dysfunctional target genes and ranked by a novel scoring method. We also conducted proof-of-concept case studies on cancer-related co-functional microRNA pairs as well as on non-cancer disease-related microRNA pairs. Conclusions: With the prioritization of the co-functional microRNAs that relate to a series of diseases, we found that the co-function phenomenon is not unusual. We also confirmed that the regulation of the microRNAs for the development of cancers is more complex and have more unique properties than those of non-cancer diseases

Peng, H

Lan, C

Zheng, Y

Hutvagner, G

Tao, D

Li, J

OPUS - University of Technology Sydney

Cross disease analysis of co-functional microRNA pairs on a reconstructed network of disease-gene-microRNA tripartite

Eysink, W.D. (author)

TU Delft Repository

Extra spuisluis in de Afsluitdijk: Effect op onderhoud havens

The disease-miRNA associations for constructing the DGR tripartite. We list the disease-miRNA associations for constructing the DGR tripartite here including the cancer related tripartite and the non-cancer disease associated tripartite.This file can also be downloaded from: https://drive.google.com/open?id=0B6lH3mKdA9CSTkg2OVBPS0ZfVnM . (XLS 689 kb

Additional file 1 of Cross disease analysis of co-functional microRNA pairs on a reconstructed network of disease-gene-microRNA tripartite

The supplementary results for our work. This file mainly introduces the supplementary results of our work such as the details of the supplementary files, the details of the model comparison, the case study results, our prioritized multi-disease associated co-function miRNA pairs, the supplementary codes and the related references.This file can also be downloaded from: https://drive.google.com/open?id=0B6lH3mKdA9CSTkg2OVBPS0ZfVnM . • Figure S1. Performances of the predictions under different precomputed kernel matrix and alpha. • Figure S2. The ROC curves of the permutation test. • Figure S3. Performances of the prediction models with different size ratio of negative and positive samples. • Figure S4. The ROC curves of our model compared with RLSMDA based on the same positive samples. • Figure S5. The ROC curves of our model and Xu’s based on the same positive sample set and 5-fold cross validation. • Figure S6. The ROC curves of our method and Jiang’s method based on their positive sample set. (PDF 1260 kb

Additional file 2 of Cross disease analysis of co-functional microRNA pairs on a reconstructed network of disease-gene-microRNA tripartite

Datasets for the comparison of different miRNA-disease association prediction models. This file contains the three datasets that used in three state-of-the-art methods such as RLSMDA, Xuâs method and Jiangâs method.This file can also be downloaded from: https://drive.google.com/open?id=0B6lH3mKdA9CSTkg2OVBPS0ZfVnM . (XLS 157 kb

Additional file 4 of Cross disease analysis of co-functional microRNA pairs on a reconstructed network of disease-gene-microRNA tripartite

https://opus.lib.uts.edu.au/bitstream/10453/103360/1/Cross%20disease%20analysis%20of%20co-functional.pdf

Extra spuisluis in de Afsluitdijk: Effect op onderhoud havens

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