Application and prospect of cutting-edge information technology in biomedical big data
Received date: 2021-05-31
Revised date: 2021-09-16
Online published: 2021-10-26
Supported by
Surpported by the Strategic Priority Research Program of Chinese Academy of Sciences No(XDB38050200)
In recent years, with the development of various high-throughput omics based biological technologies (BT), biomedical research began to enter the era of big data. In the face of high-dimensional, multi-domain and multi-modal biomedical big data, scientific research requires a new paradigm of data intensive scientific research. The vigorous development of cutting-edge information technologies (IT) such as cloud computing, blockchain and artificial intelligence provides technical means for the practice of this new research paradigm. Here,we describe the application of such cutting-edge information technologies in biomedical big data, and propose a forward-looking prospect for the construction of a new paradigm supporting environment for data intensive scientific research. We expect to establish a new research scheme and new scientific research paradigm integrating BT & IT technology, which can finally promote the great leap forward development of biomedical research.
Guangyong Zheng, Tao Zeng, Yixue Li . Application and prospect of cutting-edge information technology in biomedical big data[J]. Hereditas(Beijing), 2021 , 43(10) : 924 -929 . DOI: 10.16288/j.yczz21-192
| [1] | Goodwin S, McPherson JD, McCombie WR.. Coming of age: ten years of next-generation sequencing technologies. Nat Rev Gene, 2016,17(6):333-351. |
| [2] | Nimrod R, Ron S . Multi-omic and multi-view clustering algorithms: review and cancer benchmark. Nucleic Acids Res, 2018,46(20):10546-10562. |
| [3] | Houle D, Govindaraju DR, Omholt S . Phenomics: the next challenge. Nat Rev Genet, 2010,11(12):855-866. |
| [4] | Brown SDM, Holmes CC, Mallon AM, Meehan TF, Smedley D, Wells S . High-throughput mouse phenomics for characterizing mammalian gene function. Nat Rev Genet, 2018,19(6):357-370. |
| [5] | Milicchio F, Rose R, Bian J, Min J, Prosperi M . Visual programming for next-generation sequencing data analytics. BioData Min, 2016,9:16. |
| [6] | Fischer M, Snajder R, Pabinger S, Dander A, Schossig A, Zschocke J, Trajanoski Z, Stocker G . SIMPLEX: cloud-enabled pipeline for the comprehensive analysis of exome sequencing data. PLoS ONE, 2012,7(8):e41948. |
| [7] | Angiuoli SV, Matalka M, Gussman A, Galens K, Vangala M, Riley DR, Arze C, White JR, White O, Fricke WF . CloVR: a virtual machine for automated and portable sequence analysis from the desktop usingcloud computing. BMC Bioinformatics, 2011,12:356. |
| [8] | Langmead B, Schatz MC, Lin J, Pop M, Salzberg SL . Searching for SNPs with cloud computing. Genome Biol, 2009,10(11):R134. |
| [9] | Guo X, Yu N, Ding XJ, Wang JX, Pan Y . DIME: a novel framework for de novo metagenomic sequence assembly. J Comput Biol, 2015,22(2):159-177. |
| [10] | Byrd JB, Greene AC, Prasad DV, Jiang XQ, Greene CS . Responsible, practical genomic data sharing that accelerates research. Nat Rev Genet, 2020,21(10):615-629. |
| [11] | Fan K, Wang S, Ren Y, Li H, Yang Y . MedBlock: efficient and secure medical data sharing via blockchain. J Med Syst, 2018,42(8):136. |
| [12] | Jin XL, Zhang M, Zhou ZY, Yu XY . Application of a blockchain platform to manage and secure personal genomic data: a case study of LifeCODE.ai in China. J Med Internet Res, 2019,21(9):e13587. |
| [13] | Zhavoronkov A, Church G . The advent of human life data economics. Trends Mol Med, 2019. 25(7):566-570. |
| [14] | Wu F, Lu CW, Zhu MJ, Chen H, Zhu J, Yu K, Li L, Li M, Chen QF, Li X, Cao XD, Wang ZY, Zha ZJ, Zhuang YT, Pan YH . Towards a new generation of artificial intelligence in China. Nat Mach Intell, 2020,2(6):312-316. |
| [15] | Zhao XT, Yang YD, Qu HZ, Fang XD. Applications of machine learning in clinical decision support in the omic era. Hereditas(Beijing), 2018,40(9):693-703. |
| [15] | 赵学彤, 杨亚东, 渠鸿竹, 方向东. 组学时代下机器学习方法在临床决策支持中的应用. 遗传, 2018,40(9):693-703. |
| [16] | Alexandra Maslova, Ricardo N. Ramirez, Ke Ma, Hugo Schmutz, Chendi Wang, Curtis Fox, Bernard Ng, Christophe Benoist, Sara Mostafavi. Deep learning of immune cell differentiation. Proc Natl Acad Sci USA, 2020,117(41):25655-25666. |
| [17] | Ethan C. Alley, Grigory Khimulya, Surojit Biswas, Mohammed AlQuraishi, George M. Church. Unified rational protein engineering with sequence-based deep representation learning. Nat Methods, 2019,16(12):1315-1322. |
| [18] | Morton JT, Aksenov AA, Nothias LF, Foulds JR, Quinn RA, Badri MH, Swenson TL, Van Goethem MW, Northen TR, Vazquez-Baeza Y, Wang M, Bokulich NA, Watters A, Song SJ, Bonneau R, Dorrestein PC, Knight R . Learning representations of microbe-metabolite interactions. Nat Methods, 2019,16(12):1306-1314. |
| [19] | Shao X, Lu XY, Liao J, Chen HJ, Fan XH . New avenues for systematically inferring cell-cell communication: through single-cell transcriptomics data. Protein Cell, 2020,11(12):866-880. |
| [20] | Li J, Chen H, Wang YM, May Chen MJ, Liang H . Next- generation analytics for omics data. Cancer Cell. 2021, 39(1):3-6. |
| [21] | Hong LX, Lin JJ, Li SY, Wan FP, Yang H, Jiang T, Zhao D, Zeng JY . A novel machine learning framework for automated biomedical relation extraction from large-scale literature repositories. Nat Mach Intell, 2020,2(6):347-355. |
| [22] | Liu Q, Chen SQ, Jiang R, Wong WH . Simultaneous deep generative modeling and clustering of single cell genomic data. Nat Mach Intell, 2021,3(6):536-544. |
| [23] | Esteva A, Kuprel B, Novoa RA, Ko J, Swetter SM, Blau HM, Thrun S . Dermatologist-level classification of skin cancer with deep neural networks. Nature, 2017,542(7639):115-118. |
| [24] | Jing YK, Bian YM, Hu ZH, Wang LR, Xie XQ . Deep learning for drug design: an artificial intelligence paradigm for drug discovery in the big data era. AAPS J, 2018,20(3):58. |
/
| 〈 |
|
〉 |
www.chinagene.cn
备案号：京ICP备09063187号
