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01.03.2018 | Research | Sonderheft 1/2018 Open Access

BMC Medical Informatics and Decision Making 1/2018

Heterogeneous network propagation for herb target identification

Zeitschrift:
BMC Medical Informatics and Decision Making > Sonderheft 1/2018
Autoren:
Kuo Yang, Guangming Liu, Ning Wang, Runshun Zhang, Jian Yu, Jianxin Chen, Xuezhong Zhou
Wichtige Hinweise

Electronic supplementary material

The online version of this article (https://​doi.​org/​10.​1186/​s12911-018-0592-z) contains supplementary material, which is available to authorized users.

Abstract

Background

Identifying targets of herbs is a primary step for investigating pharmacological mechanisms of herbal drugs in Traditional Chinese medicine (TCM). Experimental targets identification of herbs is a difficult and time-consuming work. Computational method for identifying herb targets is an efficient approach. However, how to make full use of heterogeneous network data about herbs and targets to improve the performance of herb targets prediction is still a dilemma.

Methods

In our study, a random walk algorithm on the heterogeneous herb-target network (named heNetRW) has been proposed to identify protein targets of herbs. By building a heterogeneous herb-target network involving herbs, targets and their interactions and simulating random walk algorithm on the network, the candidate targets of the given herb can be predicted.

Results

The experimental results on large-scale dataset showed that heNetRW had higher performance of targets prediction than PRINCE (improved F1-score by 0.08 and Hit@1 by 21.34% in one validation setting, and improved F1-score by 0.54 and Hit@1 by 69.08% in the other validation setting). Furthermore, we evaluated novel candidate targets of two herbs (rhizoma coptidis and turmeric), which showed our approach could generate potential targets that are valuable for further experimental investigations.

Conclusions

Compared with PRINCE algorithm, heNetRW algorithm can fuse more known information (such as, known herb-target associations and pathway-based similarities of protein pairs) to improve prediction performance. Experimental results also indicated heNetRW had higher performance than PRINCE. The prediction results not only can be used to guide the selection of candidate targets of herbs, but also help to reveal the molecule mechanisms of herbal drugs.
Zusatzmaterial
Additional file 1: – HIT_herb_target.xls. 23,453 herb-target associations between 1016 herbs and 1214 targets were collected and integrated from the HIT database. (XLS 1200 kb)
12911_2018_592_MOESM1_ESM.xls
Additional file 2: – CHPA_herb_efficacy.xls. 3487 herb-efficacy associations between 742 herbs and 360 efficacies were collected from the Chinese pharmacopoeia (CHPA, 2015 edition). (XLS 200 kb)
12911_2018_592_MOESM2_ESM.xls
Additional file 3: – KEGG_protein_pathway.xls. 16,162 protein-pathway associations between 4794 proteins and 244 pathways were collected from KEGG database. (XLS 896 kb)
12911_2018_592_MOESM3_ESM.xls
Literatur
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