A multi-stream network for retrosynthesis prediction

Zhang, Qiang; Liu, Juan; Zhang, Wen; Yang, Feng; Yang, Zhihui; Zhang, Xiaolei

doi:10.1007/s11704-023-3103-z

Cited by 1 publication

(1 citation statement)

References 6 publications

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“…Machine learning methods have been applied to various association discovery tasks (Zou et al, 2018;Peng et al, 2019;Shen et al, 2022;Wu et al, 2022;Yu et al, 2022;Lin et al, 2023a;Peng et al, 2023a;Peng et al, 2023b;Peng et al, 2024b;Han et al, 2023;Liu and Zhang, 2023;Qi and Zou, 2023;Xiong et al, 2023;Xu et al, 2024;Zhang et al, 2024). Consequently, machine learning algorithms have been broadly applied in LDA prediction, for example, collaborative filtering (Yu et al, 2019), graph regularization (Liu et al, 2020;, matrix factorization (Fu et al, 2018;Wang et al, 2020;Xi et al, 2022), heterogeneous graph learning framework, (Cao et al, 2023), and ensemble learning models (Peng et al, 2022a).…”

Section: Introductionmentioning

confidence: 99%

Finding potential lncRNA–disease associations using a boosting-based ensemble learning model

Zhou,

Peng,

Zeng

et al. 2024

Front. Genet.

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Introduction: Long non-coding RNAs (lncRNAs) have been in the clinical use as potential prognostic biomarkers of various types of cancer. Identifying associations between lncRNAs and diseases helps capture the potential biomarkers and design efficient therapeutic options for diseases. Wet experiments for identifying these associations are costly and laborious.Methods: We developed LDA-SABC, a novel boosting-based framework for lncRNA–disease association (LDA) prediction. LDA-SABC extracts LDA features based on singular value decomposition (SVD) and classifies lncRNA–disease pairs (LDPs) by incorporating LightGBM and AdaBoost into the convolutional neural network.Results: The LDA-SABC performance was evaluated under five-fold cross validations (CVs) on lncRNAs, diseases, and LDPs. It obviously outperformed four other classical LDA inference methods (SDLDA, LDNFSGB, LDASR, and IPCAF) through precision, recall, accuracy, F1 score, AUC, and AUPR. Based on the accurate LDA prediction performance of LDA-SABC, we used it to find potential lncRNA biomarkers for lung cancer. The results elucidated that 7SK and HULC could have a relationship with non-small-cell lung cancer (NSCLC) and lung adenocarcinoma (LUAD), respectively.Conclusion: We hope that our proposed LDA-SABC method can help improve the LDA identification.

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