Mr. Ge Zhang Profile

Ge Zhang

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Proceedings Article | 5 July 2024 Paper

FF-LSTM: phenotype prediction based on feature fusion

Ge Zhang, Zhou Zhang, Xuye Kou, Yitong Chen

Proceedings Volume 13184, 131846H (2024) https://doi.org/10.1117/12.3032863

KEYWORDS: Data modeling, Feature fusion, Feature extraction, Performance modeling, Data processing, Yield improvement, Machine learning, Genetics, Agriculture, Neurons

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Phenotypic prediction before crop planting and harvest facilitates plant phenotyping analysis and the implementation of precision agriculture, which is crucial for food security policy formulation, crop management, and food security. Environmental data collected in the field, as important factors affecting phenotypes, can be used together with genotypes to train phenotypic prediction models to improve their prediction capabilities. Although the rise of deep learning has provided a powerful tool for predicting phenotypes using genotype and environmental data, current research only performs simple splicing operations when processing genotype and environmental data and fails to effectively model the impact of genotype and environment on phenotype. Therefore, it is still challenging to accurately predict phenotype using genotype and environment. To solve the problems mentioned above, we design a phenotype prediction model named FF-LSTM. In this method, LSTM is used to extract features from SNP and environmental, and the feature fusion method weights the extracted features to simulate the influence of genotype and environment on phenotype. We conducted experiments on the dataset and verified the effectiveness of this method by analyzing and evaluating the experimental results.

Proceedings Article | 8 November 2023 Paper

A method for esophageal cancer classification using mutual information feature selection and adversarial samples

Lu Bai, Ge Zhang

Proceedings Volume 12923, 1292323 (2023) https://doi.org/10.1117/12.3011459

KEYWORDS: Cancer, Feature selection, Neural networks, Deep learning, Data modeling, Artificial intelligence

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Early detection and treatment of esophageal cancer may improve the survival rate of patients, despite its high incidence and mortality. The use of computer technology can assist in the diagnosis of esophageal cancer. RNA-Seq gene expression data can be used for the diagnosis of esophageal cancer, but it is difficult to analyze directly because of its high dimension and small sample size. Applying computer technology to this data can solve these problems. In our work, we used the RNA-Seq gene expression dataset and considered the specificity of the sample, proposed an artificial intelligence approach for esophageal cancer classification through selecting the comprehensive features of RNA-Seq gene expression data using mutual information feature selection and obtaining a set of sample specific features by generating adversarial examples using one-pixel attack method to reduce the dimensionality of the dataset. Finally, the deep learning method is used to construct a deep neural network as the classifier. The experimental results reveal that this method outperforms other state-of-the-art algorithms in terms of accuracy and other metrics.

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