Zobrazeno 1 - 10
of 1 248
pro vyhledávání: '"Fa Zhang"'
Publikováno v:
BMC Psychology, Vol 12, Iss 1, Pp 1-19 (2024)
Abstract Background Emotion analysis of social media texts is an innovative method for gaining insight into the mental state of the public and understanding social phenomena. However, emotion is a complex psychological phenomenon, and there are vario
Externí odkaz:
https://doaj.org/article/5ac7e9d598a64a4586ec1e0984428978
Publikováno v:
Big Data Mining and Analytics, Vol 7, Iss 3, Pp 590-602 (2024)
Kirsten rat sarcoma viral oncogene homolog (namely KRAS) is a key biomarker for prognostic analysis and targeted therapy of colorectal cancer. Recently, the advancement of machine learning, especially deep learning, has greatly promoted the developme
Externí odkaz:
https://doaj.org/article/e5318c9013be456dab7a3a654e626975
Publikováno v:
BMC Microbiology, Vol 24, Iss 1, Pp 1-10 (2024)
Abstract Background Phylogeographic studies have gained prominence in linking past geological events to the distribution patterns of biodiversity, primarily in mountainous regions. However, such studies often focus on plant taxa, neglecting the intri
Externí odkaz:
https://doaj.org/article/ffaa833ba8844db482a674f3a5d88a0b
Publikováno v:
Nature Communications, Vol 15, Iss 1, Pp 1-14 (2024)
Abstract The dynamics of proteins are crucial for understanding their mechanisms. However, computationally predicting protein dynamic information has proven challenging. Here, we propose a neural network model, RMSF-net, which outperforms previous me
Externí odkaz:
https://doaj.org/article/450d303c9a8d42b7a89e8e9f18b5042f
Improving the enzymatic activity and stability of N-carbamoyl hydrolase using deep learning approach
Publikováno v:
Microbial Cell Factories, Vol 23, Iss 1, Pp 1-12 (2024)
Abstract Background Optically active D-amino acids are widely used as intermediates in the synthesis of antibiotics, insecticides, and peptide hormones. Currently, the two-enzyme cascade reaction is the most efficient way to produce D-amino acids usi
Externí odkaz:
https://doaj.org/article/25d9821add1743d9b9465d483877987f
Publikováno v:
Nature Communications, Vol 15, Iss 1, Pp 1-15 (2024)
Abstract Advances in cryo-electron microscopy (cryo-EM) imaging technologies have led to a rapidly increasing number of cryo-EM density maps. Alignment and comparison of density maps play a crucial role in interpreting structural information, such as
Externí odkaz:
https://doaj.org/article/adc2f66cd73d4006b81f3375665dbb1b
Publikováno v:
IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, Vol 17, Pp 17945-17956 (2024)
The area of impervious surfaces serves as a critical metric to gauge urbanization levels and evaluates ecological health in a given region. However, in some areas with low-density impervious surfaces, these impervious surfaces are often small in size
Externí odkaz:
https://doaj.org/article/1e6880f88d5b43f88c4427ff6d7584da
Autor:
Enming Zhao, Hongyi Zhao, Guangyu Liu, Jianbo Jiang, Fa Zhang, Jilei Zhang, Chuang Luo, Bobo Chen, Xiaoyan Yang
Publikováno v:
IEEE Access, Vol 12, Pp 81314-81328 (2024)
Accurate identification of fungal species is crucial for mycological research, relying significantly on experienced taxonomists’ ability to recognize fungal morphology. With the dwindling number of taxonomists, developing a rapid, accurate, and aut
Externí odkaz:
https://doaj.org/article/5ac1c03bfbff4202ab4944b618fbae72
Publikováno v:
Fundamental Research, Vol 4, Iss 4, Pp 713-714 (2024)
Externí odkaz:
https://doaj.org/article/5841a02973c049b7a5dc1db43e3b1aa3
Autor:
Wenkai Wang, Chenjie Feng, Renmin Han, Ziyi Wang, Lisha Ye, Zongyang Du, Hong Wei, Fa Zhang, Zhenling Peng, Jianyi Yang
Publikováno v:
Nature Communications, Vol 14, Iss 1, Pp 1-13 (2023)
Abstract RNA 3D structure prediction is a long-standing challenge. Inspired by the recent breakthrough in protein structure prediction, we developed trRosettaRNA, an automated deep learning-based approach to RNA 3D structure prediction. The trRosetta
Externí odkaz:
https://doaj.org/article/e73bf44fcb834b27bfaba50be86eb52f