Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: Structural heterogeneity in single-particle images presents a major challenge for high-resolution cryo-electron microscopy (cryo-EM) structure determination. Here  introduce a statistical manifold learning approach for unsupervised single-particle deep classification. When optimized for Intel high-performance computing (HPC) processors, our approach can generate thousands of reference-free class averages within several hours from hundreds of thousands of single-particle cryo-EM images. Deep classification thus assists in computational purification of single-particle datasets for high-resolution reconstruction.
['Jiayi Wu', 'Yong-Bei Ma', 'Charles Condgon', 'Bevin Brett', 'Shuobing Chen', 'Qi Ouyang', 'Youdong Mao']
I am writing a research paper on a idea Unsupervised single-particle deep classification via statistical manifold learning
[{"from": "human", "value": "I am writing a research paper on a idea Unsupervised single-particle deep classification via statistical manifold learning"}, {"from": "gpt", "value": "Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: Structural heterogeneity in single-particle images presents a major challenge for high-resolution cryo-electron microscopy (cryo-EM) structure determination. Here  introduce a statistical manifold learning approach for unsupervised single-particle deep classification. When optimized for Intel high-performance computing (HPC) processors, our approach can generate thousands of reference-free class averages within several hours from hundreds of thousands of single-particle cryo-EM images. Deep classification thus assists in computational purification of single-particle datasets for high-resolution reconstruction."}]