Journal of Computer Science and Technology ›› 2021, Vol. 36 ›› Issue (2): 361-374.doi: 10.1007/s11390-021-0801-6

Special Issue: Emerging Areas

• Special Section on AI and Big Data Analytics in Biology and Medicine • Previous Articles     Next Articles

An Efficient WRF Framework for Discovering Risk Genes and Abnormal Brain Regions in Parkinson's Disease Based on Imaging Genetics Data

Xia-An Bi, Member, CCF, IEEE, Zhao-Xu Xing, Rui-Hui Xu, and Xi Hu        

  1. College of Information Science and Engineering, Hunan Normal University, Changsha 410006, China;Hunan Provincial Key Laboratory of Intelligent Computing and Language Information Processing, Hunan Normal University, Changsha 410006, China
  • Received:2020-07-14 Revised:2021-02-28 Online:2021-03-05 Published:2021-04-01
  • About author:Xia-An Bi received his Ph.D. degree in computer applications from Hunan University, Changsha, in 2012. He is currently a professor in the Hunan Provincial Key Laboratory of Intelligent Computing and Language Information Processing, and College of Information Science and Engineering in Hunan Normal University, Changsha. His current research interests include machine learning, brain science, and artificial intelligence. He is the author or coauthor of several technical papers including several ESI Highly Cited Papers according to 2020 Clarivate Analytics ESI report, and also a very active reviewer for many international journals and conferences. He is a member of CCF and IEEE.
  • Supported by:
    This work was supported by the National Natural Science Foundation of China under Grant No. 62072173, the Natural Science Foundation of Hunan Province of China under Grant No. 2020JJ4432, the Key Scientific Research Projects of Department of Education of Hunan Province under Grant No. 20A296, the Degree and Postgraduate Education Reform Project of Hunan Province under Grant No. 2019JGYB091, Hunan Provincial Science and Technology Project Foundation under Grant No. 2018TP1018, and the Innovation and Entrepreneurship Training Program of Hunan Xiangjiang Artificial Intelligence Academy.

As an emerging research field of brain science, multimodal data fusion analysis has attracted broader attention in the study of complex brain diseases such as Parkinson's disease (PD). However, current studies primarily lie with detecting the association among different modal data and reducing data attributes. The data mining method after fusion and the overall analysis framework are neglected. In this study, we propose a weighted random forest (WRF) model as the feature screening classifier. The interactions between genes and brain regions are detected as input multimodal fusion features by the correlation analysis method. We implement sample classification and optimal feature selection based on WRF, and construct a multimodal analysis framework for exploring the pathogenic factors of PD. The experimental results in Parkinson's Progression Markers Initiative (PPMI) database show that WRF performs better compared with some advanced methods, and the brain regions and genes related to PD are detected. The fusion of multi-modal data can improve the classification of PD patients and detect the pathogenic factors more comprehensively, which provides a novel perspective for the diagnosis and research of PD. We also show the great potential of WRF to perform the multimodal data fusion analysis of other brain diseases.

Key words: multimodal fusion feature; Parkinson's disease; pathogenic factor detection; sample classification; weighted random forest model;

[1] Arkinson C, Walden H. Parkin function in Parkinson's disease. Science, 2018, 360(6386):267-268. DOI:10.1126/science.aar6606.
[2] Lv D J, Li L X, Chen J, Wei S Z, Wang F, Hu H, Xie A M, Liu C F. Sleep deprivation caused a memory defects and emotional changes in a rotenone-based zebrafish model of Parkinson's disease. Behavioural Brain Research, 2019, 372:Article No. 112031. DOI:10.1016/j.bbr.2019.112031.
[3] Koros C, Simitsi A, Stefanis L. Genetics of Parkinson's disease:Genotype-phenotype correlations. International Review of Neurobiology, 2017, 132:197-231. DOI:10.1016/bs.irn.2017.01.009.
[4] Kim M, Kim J, Lee S H, Park H. Imaging genetics approach to Parkinson's disease and its correlation with clinical score. Scientific Reports, 2017, 7:Article No. 46700. DOI:10.1038/srep46700.
[5] Won J H, Kim M, Park B Y, Youn J, Park H. Effectiveness of imaging genetics analysis to explain degree of depression in Parkinson's disease. PLoS ONE, 2019, 14(2):Article No. e0211699. DOI:10.1371/journal.pone.0211699.
[6] Wang X, Yan J, Yao X et al. Longitudinal genotypephenotype association study through temporal structure auto-learning predictive model. Journal of Computational Biology, 2018, 25(7):809-824. DOI:10.1089/cmb.2018.0008.
[7] Hao X, Li C, Yan J, Yao X, Risacher S L, Saykin A J, Shen L, Zhang D, Alzheimer's Disease Neuroimaging Initiative. Identification of associations between genotypes and longitudinal phenotypes via temporally-constrained group sparse canonical correlation analysis. Bioinformatics, 2017, 33(14):i341-i349. DOI:10.1093/bioinformatics/btx245.
[8] Min W, Liu J, Zhang S. Edge-group sparse PCA for network-guided high dimensional data analysis. Bioinformatics, 2018, 34(20):3479-3487. DOI:10.1093/bioinformatics/bty362.
[9] Hua K, Zhang X. Estimating the total genome length of a metagenomic sample using k-mers. BMC Genomics, 2019, 20(2):Article No. 183. DOI:10.1186/s12864-019-5467-x.
[10] Calhoun V D, Liu J, AdalıT. A review of group ICA for fMRI data and ICA for joint inference of imaging, genetic, and ERP data. NeuroImage, 2009, 45(1, Supplement 1):S163-S172. DOI:10.1016/j.neuroimage.2008.10.057.
[11] Hamza T H, Zabetian C P, Tenesa A et al. Common genetic variation in the HLA region is associated with late-onset sporadic Parkinson's disease. Nature Genetics, 2010, 42(9):781-785. DOI:10.1038/ng.642.
[12] Peng J, Guan J, Shang X. Predicting Parkinson's disease genes based on Node2vec and autoencoder. Frontiers in Genetics, 2019, 10:Article No. 226. DOI:10.3389/fgene.2019.00226.
[13] Mohammed A, Zamani M, Bayford R, Demosthenous A. Toward on-demand deep brain stimulation using online Parkinson's disease prediction driven by dynamic detection. IEEE Transactions on Neural Systems and Rehabilitation Engineering, 2017, 25(12):2441-2452. DOI:10.1109/TNSRE.2017.2722986.
[14] Rana B, Juneja A, Saxena M, Gudwani S, Kumaran S S, Behari M, Agrawal R K. Relevant 3D local binary pattern based features from fused feature descriptor for differential diagnosis of Parkinson's disease using structural MRI. Biomedical Signal Processing and Control, 2017, 34:134-143. DOI:10.1016/j.bspc.2017.01.007.
[15] Gupta D, Julka A, Jain S, Aggarwal T, Khanna A, Arunkumar N, De Albuquerque V H C. Optimized cuttlefish algorithm for diagnosis of Parkinson's disease. Cognitive Systems Research, 2018, 52:36-48. DOI:10.1016/j.cogsys.2018.06.006.
[16] Zeng W, Liu F, Wang Q, Wang Y, Ma L, Zhang Y. Parkinson's disease classification using gait analysis via deterministic learning. Neuroscience Letters, 2016, 633:268-278. DOI:10.1016/j.neulet.2016.09.043.
[17] Huang Y A, Huang Z A, You Z H, Hu P, Li L P, Li Z W, Wang L. Precise prediction of pathogenic microorganisms using 16S rRNA gene sequences. In Proc. the 15th International Conference on Intelligent Computing, August 2019, pp.138-150. DOI:10.1007/978-3-030-26969-213.
[18] Du L, Liu K, Zhang T, Yao X, Yan J, Risacher S L, Han J, Guo L, Saykin A J, Shen L, Alzheimer's Disease Neuroimaging Initiative. A novel SCCA approach via truncated l1-norm and truncated group lasso for brain imaging genetics. Bioinformatics, 2017, 34(2):278-285. DOI:10.1093/bioinformatics/btx594.
[19] Du L, Liu K, Zhu L, Yao X, Risacher S L, Guo L, Saykin A J, Shen L, Alzheimer's Disease Neuroimaging Initiative. Identifying progressive imaging genetic patterns via multitask sparse canonical correlation analysis:A longitudinal study of the ADNI cohort. Bioinformatics, 2019, 35(14):i474-i483. DOI:10.1093/bioinformatics/btz320.
[20] Du L, Liu K, Yao X, Risacher S L, Shen L. Detecting genetic associations with brain imaging phenotypes in Alzheimer's disease via a novel structured SCCA approach. Medical Image Analysis, 2020, 61:Article No. 101656. DOI:10.1016/
[21] Wei L, Su R, Luan S, Liao Z, Manavalan B, Zou Q, Shi X. Iterative feature representations improve N4-methylcytosine site prediction. Bioinformatics, 2019, 35(23):4930-4937. DOI:10.1093/bioinformatics/btz408.
[22] Chen F X, Kang D Z, Chen F Y, Liu Y, Wu G, Li X, Yu L H, Lin Y X, Lin Z Y. Gray matter atrophy associated with mild cognitive impairment in Parkinson's disease. Neuroscience Letters, 2016, 617:160-165. DOI:10.1016/j.neulet.2015.12.055.
[23] Guimarães R P, Arci Santos M C, Dagher A et al. Pattern of reduced functional connectivity and structural abnormalities in Parkinson's disease:An exploratory study. Frontiers in Neurology, 2017, 7:243. DOI:10.3389/fneur.2016.00243.
[24] Hou Y, Wei Q, Ou R, Yang J, Song W, Gong Q, Shang H. Impaired topographic organization in cognitively unimpaired drug-naïve patients with rigidity-dominant Parkinson's disease. Parkinsonism & Related Disorders, 2018, 56:52-57. DOI:10.1016/j.parkreldis.2018.06.021.
[25] Zhao L, Wang E, Zhang X et al. Cortical structural connectivity alterations in primary insomnia:Insights from MRI-based morphometric correlation analysis. BioMed Research International, 2015, 2015:Article No. 817595. DOI:10.1155/2015/817595.
[26] Meunier D, Stamatakis E A, Tyler L K. Age-related functional reorganization, structural changes, and preserved cognition. Neurobiology of Aging, 2014, 35(1):42-54. DOI:10.1016/j.neurobiolaging.2013.07.003.
[27] Li H F, Yang L, Yin D, Chen W J, Liu G L, Ni W, Wang N, Yu W, Wu Z Y, Wang Z. Associations between neuroanatomical abnormality and motor symptoms in paroxysmal kinesigenic dyskinesia. Parkinsonism & Related Disorders, 2019, 62:134-140. DOI:10.1016/j.parkreldis.2018.12.029.
[28] Reijnders J S A M, Scholtissen B, Weber W E J, Aalten P, Verhey F R J, Leentjens A F G. Neuroanatomical correlates of apathy in Parkinson's disease:A magnetic resonance imaging study using voxel-based morphometry. Movement Disorders, 2010, 25(14):2318-2325. DOI:10.1002/mds.23268.
[29] Melzer T R, Watts R, MacAskill M R, Pitcher T L, Livingston L, Keenan R J, Dalrymple-Alford J C, Anderson T J. Grey matter atrophy in cognitively impaired Parkinson's disease. Journal of Neurology, Neurosurgery, and Psychiatry, 2012, 83(2):188-194. DOI:10.1136/jnnp-2011-300828.
[30] De Schipper L J, Hafkemeijer A, van der Grond J, Marinus J, Henselmans J M L, van Hilten J J. Altered whole-brain and network-based functional connectivity in Parkinson's disease. Frontiers in Neurology, 2018, 9:Article No. 419. DOI:10.3389/fneur.2018.00419.
[31] Evangelisti S, Pittau F, Testa C et al. L-dopa modulation of brain connectivity in Parkinson's disease patients:A pilot EEG-fMRI study. Frontiers in Neuroscience, 2019, 13:Article No. 611. DOI:10.3389/fnins.2019.00611.
[32] Wang Q, Li W X, Dai S X, Guo Y C, Han F F, Zheng J J, Li G H, Huang J F. Meta-analysis of Parkinson's disease and Alzheimer's disease revealed commonly impaired pathways and dysregulation of NRF2-dependent genes. Journal of Alzheimer's Disease, 2017, 56(4):1525-1539. DOI:10.3233/JAD-161032.
[33] International Parkinson Disease Genomics Consortium. Imputation of sequence variants for identification of genetic risks for Parkinson's disease:A meta-analysis of genomewide association studies. The Lancet, 2011, 377(9766):641-649. DOI:10.1016/S0140-6736(10)62345-8.
[34] Ahmed I, Tamouza R, Delord M et al. Association between Parkinson's disease and the HLA-DRB1 locus. Movement Disorders, 2012, 27(9):1104-1110. DOI:10.1002/mds.25035.
[35] Bao W, Jiang Z, Huang D S. Novel human microbe-disease association prediction using network consistency projection. BMC Bioinformatics, 2017, 18(16):Article No. 543. DOI:10.1186/s12859-017-1968-2.
[36] Sivaranjini S, Sujatha C M. Deep learning based diagnosis of Parkinson's disease using convolutional neural network. Multimedia Tools and Applications, 2019, 79(3):15467-15479. DOI:10.1007/s11042-019-7469-8.
[37] Martinez-Murcia F J, Ortiz A, Gorriz J M, Ramirez J, Castillo-Barnes D, Salas-Gonzalez D, Segovia F. Deep convolutional autoencoders vs PCA in a highly-unbalanced Parkinson's disease dataset:A DaTSCAN study. In Proc. the 13th International Conference on Soft Computing Models in Industrial and Environmental Applications, June 2018, pp. 47-56. DOI:10.1007/978-3-319-94120-25.
[38] Gao C, Sun H, Wang T et al. Model-based and modelfree machine learning techniques for diagnostic prediction and classification of clinical outcomes in Parkinson's disease. Scientific Reports, 2018, 8(1):Article No. 7129. DOI:10.1038/s41598-018-24783-4.
[39] Abós A, Baggio H C, Segura B, García-Díaz A I, Compta Y, Martí M J, Valldeoriola F, Junqué C. Discriminating cognitive status in Parkinson's disease through functional connectomics and machine learning. Scientific Reports, 2017, 7:Article No. 45347. DOI:10.1038/srep45347.
[40] Niu Y W, Wang G H, Yan G Y, Chen X. Integrating random walk and binary regression to identify novel miRNAdisease association. BMC Bioinformatics, 2019, 20(1):Article No. 59. DOI:10.1186/s12859-019-2640-9.
[41] Zhao Y, Chen X, Yin J. Adaptive boosting-based computational model for predicting potential miRNA-disease associations. Bioinformatics, 2019, 35(22):4730-4738. DOI:10.1093/bioinformatics/btz297.
No related articles found!
Full text



[1] Li Wanxue;. Almost Optimal Dynamic 2-3 Trees[J]. , 1986, 1(2): 60 -71 .
[2] C.Y.Chung; H.R.Hwa;. A Chinese Information Processing System[J]. , 1986, 1(2): 15 -24 .
[3] Gao Qingshi; Zhang Xiang; Yang Shufan; Chen Shuqing;. Vector Computer 757[J]. , 1986, 1(3): 1 -14 .
[4] Zhang Cui; Zhao Qinping; Xu Jiafu;. Kernel Language KLND[J]. , 1986, 1(3): 65 -79 .
[5] Huang Xuedong; Cai Lianhong; Fang Ditang; Chi Bianjin; Zhou Li; Jiang Li;. A Computer System for Chinese Character Speech Input[J]. , 1986, 1(4): 75 -83 .
[6] Shi Zhongzhi;. Knowledge-Based Decision Support System[J]. , 1987, 2(1): 22 -29 .
[7] Tang Tonggao; Zhao Zhaokeng;. Stack Method in Program Semantics[J]. , 1987, 2(1): 51 -63 .
[8] Min Yinghua;. Easy Test Generation PLAs[J]. , 1987, 2(1): 72 -80 .
[9] Xia Peisu; Fang Xinwo; Wang Yuxiang; Yan Kaiming; Zhang Tingjun; Liu Yulan; Zhao Chunying; Sun Jizhong;. Design of Array Processor Systems[J]. , 1987, 2(3): 163 -173 .
[10] Sun Yongqiang; Lu Ruzhan; Huang Xiaorong;. Termination Preserving Problem in the Transformation of Applicative Programs[J]. , 1987, 2(3): 191 -201 .

ISSN 1000-9000(Print)

CN 11-2296/TP

Editorial Board
Author Guidelines
Journal of Computer Science and Technology
Institute of Computing Technology, Chinese Academy of Sciences
P.O. Box 2704, Beijing 100190 P.R. China
  Copyright ©2015 JCST, All Rights Reserved