TY - JOUR
T1 - BioMedR: an R/CRAN package for integrated data analysis pipeline in biomedical study
AU - Dong, Jie
AU - Zhu, Min-Feng
AU - Yun, Yong-Huan
AU - Lu, Ai-Ping
AU - Hou, Ting-Jun
AU - Cao, Dong-Sheng
N1 - Funding Information:
This work was supported by the National Key Basic Research Program (2015CB910700), Hunan Provincial Natural Science Foundation of China (2019JJ51003), National Science Foun- dation of China (21575128, 81773632), Zhejiang Provincial Natural Science Foundation of China (LZ19H300001) and Hong Kong Baptist University (HKBU) Strategic Development Fund project (SDF19-0402-P02). The studies meet with the approval of the university’s review board.
Publisher Copyright:
© 2019 The Author(s). Published by Oxford University Press. All rights reserved
PY - 2021/1
Y1 - 2021/1
N2 - 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/.
AB - 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/.
KW - bioinformatics
KW - cheminformatics
KW - drug discovery
KW - molecular representation
KW - R package
UR - http://www.scopus.com/inward/record.url?scp=85097753076&partnerID=8YFLogxK
U2 - 10.1093/bib/bbz150
DO - 10.1093/bib/bbz150
M3 - Journal article
C2 - 31885044
AN - SCOPUS:85097753076
SN - 1467-5463
VL - 22
SP - 474
EP - 484
JO - Briefings in Bioinformatics
JF - Briefings in Bioinformatics
IS - 1
ER -