BioMedR: an R/CRAN package for integrated data analysis pipeline in biomedical study

Author:

Dong Jie12,Zhu Min-Feng2,Yun Yong-Huan3,Lu Ai-Ping4,Hou Ting-Jun5,Cao Dong-Sheng24

Affiliation:

1. National Engineering Laboratory for Deep Processing of Rice and Byproducts, Hunan Key Laboratory of Processed Food for Special Medical Purpose, College of Food Science and Engineering, Central South University of Forestry and Technology, Changsha,410003 P. R. China

2. Xiangya School of Pharmaceutical Sciences, Central South University, Changsha, 410003 P. R. China

3. College of Food Science and Engineering, Hainan University, Haikou, 570228 PR China

4. Institute for Advancing Translational Medicine in Bone & Joint Diseases, School of Chinese Medicine, Hong Kong Baptist University, Hong Kong SAR, P. R. China

5. College of Pharmaceutical Sciences, Zhejiang University, Hangzhou, 310058 Zhejiang, P. R. China

Abstract

Abstract Background With the increasing development of biotechnology and information technology, publicly available data in chemistry and biology are undergoing explosive growth. Such wealthy information in these resources needs to be extracted and then transformed to useful knowledge by various data mining methods. However, a main computational challenge is how to effectively represent or encode molecular objects under investigation such as chemicals, proteins, DNAs and even complicated interactions when data mining methods are employed. To further explore these complicated data, an integrated toolkit to represent different types of molecular objects and support various data mining algorithms is urgently needed. Results We developed a freely available R/CRAN package, called BioMedR, for molecular representations of chemicals, proteins, DNAs and pairwise samples of their interactions. The current version of BioMedR could calculate 293 molecular descriptors and 13 kinds of molecular fingerprints for small molecules, 9920 protein descriptors based on protein sequences and six types of generalized scale-based descriptors for proteochemometric modeling, more than 6000 DNA descriptors from nucleotide sequences and six types of interaction descriptors using three different combining strategies. Moreover, this package realized five similarity calculation methods and four powerful clustering algorithms as well as several useful auxiliary tools, which aims at building an integrated analysis pipeline for data acquisition, data checking, descriptor calculation and data modeling. Conclusion BioMedR provides a comprehensive and uniform R package to link up different representations of molecular objects with each other and will benefit cheminformatics/bioinformatics and other biomedical users. It is available at: https://CRAN.R-project.org/package=BioMedR and https://github.com/wind22zhu/BioMedR/.

Funder

HKBU Strategic Development Fund

Zhejiang Provincial Natural Science Foundation of China

National Science Foundation of China

Hunan Provincial Natural Science Foundation of China

National Key Basic Research Program

Publisher

Oxford University Press (OUP)

Subject

Molecular Biology,Information Systems

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