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29. Synergistic Application of Multiple Machine Learning Algorithms and Hyperparameter Optimization Strategies for Net Ecosystem Productivity Prediction in Southeast Asia

Author

Huang, Chaoqing, primary, Chen, Bin, additional, Sun, Chuanzhun, additional, Wang, Yuan, additional, Zhang, Junye, additional, Yang, Huan, additional, Wu, Shengbiao, additional, Tu, Peiyue, additional, Nguyen, MinhThu, additional, Hong, Song, additional, and He, Chao, additional

Published
2023
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39. Synergistic Application of Multiple Machine Learning Algorithms and Hyperparameter Optimization Strategies for Net Ecosystem Productivity Prediction in Southeast Asia.

Author

Huang, Chaoqing, Chen, Bin, Sun, Chuanzhun, Wang, Yuan, Zhang, Junye, Yang, Huan, Wu, Shengbiao, Tu, Peiyue, Nguyen, MinhThu, Hong, Song, and He, Chao

Subjects
MACHINE learning, ECOLOGICAL models, CARBON sequestration, ECOSYSTEMS, RESTORATION ecology, CLIMATE change
Abstract

The spatiotemporal patterns and shifts of net ecosystem productivity (NEP) play a pivotal role in ecological conservation and addressing climate change. For example, by quantifying the NEP information within ecosystems, we can achieve the protection and restoration of natural ecological balance. Monitoring the changes in NEP enables a more profound understanding and prediction of ecosystem alterations caused by global warming, thereby providing a scientific basis for formulating policies aimed at mitigating and adapting to climate change. The accurate prediction of NEP sheds light on the ecosystem's response to climatic variations and aids in formulating targeted carbon sequestration policies. While traditional ecological process models provide a comprehensive approach to predicting NEP, they often require extensive experimental and empirical data, increasing research costs. In contrast, machine-learning models offer a cost-effective alternative for NEP prediction; however, the delicate balance in algorithm selection and hyperparameter tuning is frequently overlooked. In our quest for the optimal prediction model, we examined a combination of four mainstream machine-learning algorithms with four hyperparameter-optimization techniques. Our analysis identified that the backpropagation neural network combined with Bayesian optimization yielded the best performance, with an R2 of 0.68 and an MSE of 1.43. Additionally, deep-learning models showcased promising potential in NEP prediction. Selecting appropriate algorithms and executing precise hyperparameter-optimization strategies are crucial for enhancing the accuracy of NEP predictions. This approach not only improves model performance but also provides us with new tools for a deeper understanding of and response to ecosystem changes induced by climate change. [ABSTRACT FROM AUTHOR]

Published
2024
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47. Constructing Unique Mesoporous Carbon Superstructures via Monomicelle Interface Confined Assembly

Author

Zhao, Zaiwang, primary, Duan, Linlin, additional, Zhao, Yujuan, additional, Wang, Lipeng, additional, Zhang, Junye, additional, Bu, Fanxing, additional, Sun, Zhihao, additional, Zhang, Tengsheng, additional, Liu, Mengli, additional, Chen, Hanxing, additional, Yang, Yi, additional, Lan, Kun, additional, Lv, Zirui, additional, Zu, Lianhai, additional, Zhang, Pengfei, additional, Che, Renchao, additional, Tang, Yun, additional, Chao, Dongliang, additional, Li, Wei, additional, and Zhao, Dongyuan, additional

Published
2022
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50. EVlncRNA-Dpred: improved prediction of experimentally validated lncRNAs by deep learning.

Author

Zhou, Bailing, Ding, Maolin, Feng, Jing, Ji, Baohua, Huang, Pingping, Zhang, Junye, Yu, Xue, Cao, Zanxia, Yang, Yuedong, Zhou, Yaoqi, and Wang, Jihua

Subjects
INTERNET servers, DEEP learning, LINCRNA, MACHINE learning, CONVOLUTIONAL neural networks, NUCLEOTIDE sequencing, SOURCE code
Abstract

Long non-coding RNAs (lncRNAs) played essential roles in nearly every biological process and disease. Many algorithms were developed to distinguish lncRNAs from mRNAs in transcriptomic data and facilitated discoveries of more than 600 000 of lncRNAs. However, only a tiny fraction (<1%) of lncRNA transcripts (~4000) were further validated by low-throughput experiments (EVlncRNAs). Given the cost and labor-intensive nature of experimental validations, it is necessary to develop computational tools to prioritize those potentially functional lncRNAs because many lncRNAs from high-throughput sequencing (HTlncRNAs) could be resulted from transcriptional noises. Here, we employed deep learning algorithms to separate EVlncRNAs from HTlncRNAs and mRNAs. For overcoming the challenge of small datasets, we employed a three-layer deep-learning neural network (DNN) with a K-mer feature as the input and a small convolutional neural network (CNN) with one-hot encoding as the input. Three separate models were trained for human (h), mouse (m) and plant (p), respectively. The final concatenated models (EVlncRNA-Dpred (h), EVlncRNA-Dpred (m) and EVlncRNA-Dpred (p)) provided substantial improvement over a previous model based on support-vector-machines (EVlncRNA-pred). For example, EVlncRNA-Dpred (h) achieved 0.896 for the area under receiver-operating characteristic curve, compared with 0.582 given by sequence-based EVlncRNA-pred model. The models developed here should be useful for screening lncRNA transcripts for experimental validations. EVlncRNA-Dpred is available as a web server at https://www.sdklab-biophysics-dzu.net/EVlncRNA-Dpred/index.html , and the data and source code can be freely available along with the web server. [ABSTRACT FROM AUTHOR]

Published
2023
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