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A new ensemble-based targeted observational method and its application in the TPOS 2020.

ABSTRACT: Ensemble Kalman filter-based targeted observation is one of the best methods for determining the optimal observational array for oceanic buoy deployment. This study proposes a new algorithm suitable for a 'cross-region and cross-variable' approach by introducing a projection operator into the optimization process. A targeted observational analysis was conducted for El Niño-Southern Oscillation (ENSO) events in the tropical western Pacific for the Tropical Pacific Observation System (TPOS) 2020. The prediction target was at the Niño 3.4 region and the first 10 optimal observational sites detected reduced initial uncertainties by 70%, with the best observational array located where the Rossby wave signal dominates. At the vertical level, the most significant contribution was derived from observations near the thermocline. This study provides insights into understanding ENSO-related variability and offers a practical approach to designing an optimal mooring array. It serves as a scientific guidance for designing a TPOS observation network.

SUBMITTER: Rao W

PROVIDER: S-EPMC10583287 | biostudies-literature | 2023 Nov

REPOSITORIES: biostudies-literature

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A new ensemble-based targeted observational method and its application in the TPOS 2020.

Rao Weixun W Tang Youmin Y Wu Yanling Y Shen Zheqi Z Song Xiangzhou X Li Xiaojing X Lian Tao T Chen Dake D Zhou Feng F

National science review 20230902 11

Ensemble Kalman filter-based targeted observation is one of the best methods for determining the optimal observational array for oceanic buoy deployment. This study proposes a new algorithm suitable for a 'cross-region and cross-variable' approach by introducing a projection operator into the optimization process. A targeted observational analysis was conducted for El Niño-Southern Oscillation (ENSO) events in the tropical western Pacific for the Tropical Pacific Observation System (TPOS) 2020. ...[more]

PMID: 37859634

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Project description:BackgroundMany classifiers which are constructed with chosen gene markers have been proposed to forecast the prognosis of patients who suffer from breast cancer. However, few of them has been applied in clinical practice because of the bad generalization, which results from the situation that markers selected by one method are very different from those obtained by another method, and thus such markers always lack discriminative capability in the other data sets.MethodsIn this work, a new ensemble classifier, on the basis of context specific miRNA regulation modules, has been proposed to forecast the metastasis risk of cancer sufferers. First, we defined all of the miRNAs which regulate the same context as a module that contains miRNAs and their regulating context, and applied the CoMi (Context-specific miRNA activity) score in order to illustrate a miRNA's effect which happened in a particular background; then the miRNA regulation modules with distinguishing abilities were detected and each of them was responsible for building a weak classifier separately; at last, by using majority voting strategy, we integrated all weak classifiers to establish an ensembled one that was applied to forecast the prognosis of patients who suffer from cancer.ResultsAfter comparing, the results on the cohorts containing over 1,000 samples showed that the proposed ensemble classifier is superior to other three classifiers based on miRNA expression profiles, mRNA expression profiles and CoMi activity patterns respectively. Significantly, our method outperforms the representative works. Moreover, the detected modules from different data sets show great stability (with p-value of 6.40e-08). For investigating the biological significance of those selected modules, case studies have been done by us and the results suggested that our method do help to reveal latent mechanism in metastasis of breast cancer.ConclusionsOne context specific miRNA regulation module can uncover one critical biological process and its involved miRNAs that are related to the cancer outcome, and several modules together can help to study the biological mechanism in cancer metastasis, thus the classifer based on ensembling multiple classifers which were built with different context specific miRNA regulation modules has showed promising performances in terms with both prediction accuracy and generalization.

Project description:Background: Pseudouridine (Ψ) is one of the most abundant RNA modifications found in a variety of RNA types, and it plays a significant role in many biological processes. The key to studying the various biochemical functions and mechanisms of Ψ is to identify the Ψ sites. However, identifying Ψ sites using experimental methods is time-consuming and expensive. Therefore, it is necessary to develop computational methods that can accurately predict Ψ sites based on RNA sequence information. Methods: In this study, we proposed a new model called PseU-ST to identify Ψ sites in Homo sapiens (H. sapiens), Saccharomyces cerevisiae (S. cerevisiae), and Mus musculus (M. musculus). We selected the best six encoding schemes and four machine learning algorithms based on a comprehensive test of almost all of the RNA sequence encoding schemes available in the iLearnPlus software package, and selected the optimal features for each encoding scheme using chi-square and incremental feature selection algorithms. Then, we selected the optimal feature combination and the best base-classifier combination for each species through an extensive performance comparison and employed a stacking strategy to build the predictive model. Results: The results demonstrated that PseU-ST achieved better prediction performance compared with other existing models. The PseU-ST accuracy scores were 93.64%, 87.74%, and 89.64% on H_990, S_628, and M_944, respectively, representing increments of 13.94%, 6.05%, and 0.26%, respectively, higher than the best existing methods on the same benchmark training datasets. Conclusion: The data indicate that PseU-ST is a very competitive prediction model for identifying RNA Ψ sites in H. sapiens, M. musculus, and S. cerevisiae. In addition, we found that the Position-specific trinucleotide propensity based on single strand (PSTNPss) and Position-specific of three nucleotides (PS3) features play an important role in Ψ site identification. The source code for PseU-ST and the data are obtainable in our GitHub repository (https://github.com/jluzhangxinrubio/PseU-ST).

Dataset Information

A new ensemble-based targeted observational method and its application in the TPOS 2020.

Publications

A new ensemble-based targeted observational method and its application in the TPOS 2020.

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