Yongjun Piao

Orcid: 0000-0002-2769-6437

According to our database1, Yongjun Piao authored at least 12 papers between 2012 and 2021.

Collaborative distances:
  • Dijkstra number2 of five.
  • Erdős number3 of four.

Timeline

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Bibliography

2021
An Efficient Prediction Method for Coronary Heart Disease Risk Based on Two Deep Neural Networks Trained on Well-Ordered Training Datasets.
IEEE Access, 2021

2020
Wafer map defect pattern classification based on convolutional neural network features and error-correcting output codes.
J. Intell. Manuf., 2020

2019
Symmetrical Uncertainty-Based Feature Subset Generation and Ensemble Learning for Electricity Customer Classification.
Symmetry, 2019

Identification of DNA Methylation Signatures for Diagnosis of Lung Adenocarcinoma.
Proceedings of the IEEE 10th International Conference on Awareness Science and Technology, 2019

2017
Multiclass cancer classification using a feature subset-based ensemble from microRNA expression profiles.
Comput. Biol. Medicine, 2017

CAME: identification of chromatin accessibility from nucleosome occupancy and methylome sequencing.
Bioinform., 2017

A Hybrid Feature Selection Method to Classification and Its Application in Hypertension Diagnosis.
Proceedings of the Information Technology in Bio- and Medical Informatics, 2017

Detection of differentially expressed genes using feature selection approach from RNA-seq.
Proceedings of the 2017 IEEE International Conference on Big Data and Smart Computing, 2017

A Hybrid Feature Selection Method Based on Symmetrical Uncertainty and Support Vector Machine for High-Dimensional Data Classification.
Proceedings of the Intelligent Information and Database Systems - 9th Asian Conference, 2017

2015
Comparing the normalization methods for the differential analysis of Illumina high-throughput RNA-Seq data.
BMC Bioinform., 2015

2014
Ensemble method for classification of high-dimensional data.
Proceedings of the International Conference on Big Data and Smart Computing, BIGCOMP 2014, 2014

2012
An ensemble correlation-based gene selection algorithm for cancer classification with gene expression data.
Bioinform., 2012


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