Application of Clustering Analysis in Brain Gene Data Based on Deep Learning

In the current research, cluster analysis has become a very good way to obtain biological information by analyzing the brain gene expression data. In recent years, many experts have used improved traditional clustering algorithm and a new clustering algorithm to mine brain gene expression data. First, the random Forest method is used to preprocess high-dimensional and high-complexity brain gene expression data. Then, a clustering model based on deep learning is proposed, and a clustering algorithm is implemented by using deep belief network (DBN) and fuzzy c-means algorithm (FCM). This model makes full use of the generality of unsupervised learning of deep learning and clustering technology, combines the advantages of deep learning with clustering, and makes clustering effect better and more convenient for clustering high-dimensional data.

[1]  Ai-Min Yang,et al.  Research on a Fusion Scheme of Cellular Network and Wireless Sensor for Cyber Physical Social Systems , 2018, IEEE Access.

[2]  Rinkle Rani,et al.  DFuzzy: a deep learning-based fuzzy clustering model for large graphs , 2018, Knowledge and Information Systems.

[3]  Renzo Kottmann,et al.  The antiSMASH database, a comprehensive database of microbial secondary metabolite biosynthetic gene clusters , 2016, Nucleic Acids Res..

[4]  Elchanan Mossel,et al.  Learning restricted Boltzmann machines via influence maximization , 2018, STOC.

[5]  Kuo-Chen Chou,et al.  pSuc-Lys: Predict lysine succinylation sites in proteins with PseAAC and ensemble random forest approach. , 2016, Journal of theoretical biology.

[6]  Yaozong Gao,et al.  Longitudinal clinical score prediction in Alzheimer's disease with soft-split sparse regression based random forest , 2016, Neurobiology of Aging.

[7]  Jianzhong Xu,et al.  Tag Array gene chip rapid diagnosis anti-tuberculosis drug resistance in pulmonary tuberculosis -a feasibility study. , 2018, Tuberculosis.

[8]  Hongkun Chen,et al.  Optimal Sizing for Wind/PV/Battery System Using Fuzzy -Means Clustering with Self-Adapted Cluster Number , 2017 .

[9]  Song Jiang,et al.  Ensemble Prediction Algorithm of Anomaly Monitoring Based on Big Data Analysis Platform of Open-Pit Mine Slope , 2018, Complex..

[10]  Jianhui Wu,et al.  Differential Diagnosis Model of Hypocellular Myelodysplastic Syndrome and Aplastic Anemia Based on the Medical Big Data Platform , 2018, Complex..

[11]  Li Shu.  Gene Selection Method Based on SVM-RFE-SFS , 2010 .

[12]  Omar Suleiman Arabeyyat.  Information security model using decision tree for Jordanian public sector , 2018, Int. J. Electron. Secur. Digit. Forensics.

[13]  SHI Xin,et al.  Power transformer fault classifying model based on deep belief network , 2015 .

[14]  E. Lombaert,et al.  Colonization history of the western corn rootworm (Diabrotica virgifera virgifera) in North America: insights from random forest ABC using microsatellite data , 2017, bioRxiv.

[15]  R. J. Kuo,et al.  A hybrid metaheuristic and kernel intuitionistic fuzzy c-means algorithm for cluster analysis , 2018, Appl. Soft Comput..

[16]  J. Lupski,et al.  Genotyping microarray (gene chip) for the ABCR (ABCA4) gene , 2003, Human mutation.

[17]  J. Chen,et al.  Equivalence of restricted Boltzmann machines and tensor network states , 2017, 1701.04831.

[18]  Jie Li,et al.  Dynamic Prediction Research of Silicon Content in Hot Metal Driven by Big Data in Blast Furnace Smelting Process under Hadoop Cloud Platform , 2018, Complex..

[19]  Jie Dong,et al.  Newborn Screening of Genetic Mutations in Common Deafness Genes With Bloodspot-Based Gene Chip Array. , 2017, American journal of audiology.

[20]  Tanya Barrett,et al.  The Gene Expression Omnibus Database , 2016, Statistical Genomics.

[21]  Long Thanh Ngo,et al.  Multiple kernel approach to semi-supervised fuzzy clustering algorithm for land-cover classification , 2018, Eng. Appl. Artif. Intell..

[22]  Ali Feizollah,et al.  Evaluation of Network Traffic Analysis Using Fuzzy C-Means Clustering Algorithm in Mobile Malware Detection , 2018 .

[23]  Zhaoxing Li,et al.  Research on Big Data Digging of Hot Topics about Recycled Water Use on Micro-Blog Based on Particle Swarm Optimization , 2018, Sustainability.

[24]  Robert C. Thompson,et al.  Evaluation of Affymetrix Gene Chip sensitivity in rat hippocampal tissue using SAGE analysis * , 2002, The European journal of neuroscience.

[25]  Yaoguo Li,et al.  Discrete-valued gravity inversion using the guided fuzzy c-means clustering technique , 2018, GEOPHYSICS.

[26]  Wenke Zang,et al.  A Fuzzy Density Peaks Clustering Algorithm Based on Improved DNA Genetic Algorithm and K-Nearest Neighbors , 2018, IScIDE.

[27]  Jun Yu,et al.  MTD: a mammalian transcriptomic database to explore gene expression and regulation , 2016, Briefings Bioinform..

[28]  Hao Zhang,et al.  Systematic Research on the Application of Steel Slag Resources under the Background of Big Data , 2018, Complex..

[29]  Mohammad Hassan Moradi,et al.  Mortality prediction in intensive care units (ICUs) using a deep rule-based fuzzy classifier , 2018, J. Biomed. Informatics.