TY - GEN
T1 - Central Feature Network Enables Accurate Detection of Both Small and Large Particles in Cryo-Electron Tomography
AU - Wang, Yaoyu
AU - Wan, Xiaohua
AU - Chen, Cheng
AU - Zhang, Fa
AU - Cui, Xuefeng
N1 - Publisher Copyright:
© The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.
PY - 2024
Y1 - 2024
N2 - The advancements in Cryo-Electron Tomography (cryo-ET) have made it possible to visualize molecules in their natural cellular settings in three-dimensional space. Such visualizations play a crucial role in investigating the functions of biological entities under native conditions. Recently, deep learning techniques have proven effective in addressing the challenge of detecting particles in cryo-ET data. Nevertheless, the task of precisely identifying and categorizing multi-class molecules remains difficult due to factors such as the low signal-to-noise ratio and the diverse range of sizes in particle selection. In this study, we present a new framework called Central Feature Network (CFN) for detecting objects in 3D and implement it in cryo-ET analysis. A key strength of CFN is its ability to integrate central features across different scales, enabling the accurate detection of both small and large molecules. In comparison to existing methods, CFN enhances the F1 score for classification by 3.6%, 7.3%, 6.6%, and 5.1% for the four smallest molecules tested, while maintaining similar or higher F1 scores for other molecules examined. Our code is available at https://github.com/Wangyaoyuu/cfn_scr.
AB - The advancements in Cryo-Electron Tomography (cryo-ET) have made it possible to visualize molecules in their natural cellular settings in three-dimensional space. Such visualizations play a crucial role in investigating the functions of biological entities under native conditions. Recently, deep learning techniques have proven effective in addressing the challenge of detecting particles in cryo-ET data. Nevertheless, the task of precisely identifying and categorizing multi-class molecules remains difficult due to factors such as the low signal-to-noise ratio and the diverse range of sizes in particle selection. In this study, we present a new framework called Central Feature Network (CFN) for detecting objects in 3D and implement it in cryo-ET analysis. A key strength of CFN is its ability to integrate central features across different scales, enabling the accurate detection of both small and large molecules. In comparison to existing methods, CFN enhances the F1 score for classification by 3.6%, 7.3%, 6.6%, and 5.1% for the four smallest molecules tested, while maintaining similar or higher F1 scores for other molecules examined. Our code is available at https://github.com/Wangyaoyuu/cfn_scr.
KW - Cryo-electron tomography
KW - Deep learning
KW - Particle detection
KW - Pattern recognition
UR - http://www.scopus.com/inward/record.url?scp=85200555165&partnerID=8YFLogxK
U2 - 10.1007/978-981-97-5128-0_17
DO - 10.1007/978-981-97-5128-0_17
M3 - Conference contribution
AN - SCOPUS:85200555165
SN - 9789819751273
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 212
EP - 223
BT - Bioinformatics Research and Applications - 20th International Symposium, ISBRA 2024, Proceedings
A2 - Peng, Wei
A2 - Cai, Zhipeng
A2 - Skums, Pavel
PB - Springer Science and Business Media Deutschland GmbH
T2 - 20th International Symposium on Bioinformatics Research and Applications, ISBRA 2024
Y2 - 19 July 2024 through 21 July 2024
ER -