In silico prediction methods of self-interacting proteins: an empirical and academic survey

Chen, Zhanheng; You, Zhu‐Hong; Zhang, Qinhu; Guo, Zhen-Hao; Wang, Siguo; Wang, Yanbin

doi:10.1007/s11704-022-1563-1

Cited by 6 publications

(3 citation statements)

References 143 publications

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“…The high cost of experimentally determining protein structures results in relatively limited coverage of structure data [ 126 ]. In addition, PPI data may contain noise and false positives [ 91 , 127 , 128 ], which could impact the accuracy and reliability of protein interactions. Therefore, more effort should be invested in collecting reliable protein data.…”

Section: Discussionmentioning

confidence: 99%

A comprehensive review and comparison of existing computational methods for protein function prediction

Lin,

Luo,

Liu

et al. 2024

Briefings in Bioinformatics

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Protein function prediction is critical for understanding the cellular physiological and biochemical processes, and it opens up new possibilities for advancements in fields such as disease research and drug discovery. During the past decades, with the exponential growth of protein sequence data, many computational methods for predicting protein function have been proposed. Therefore, a systematic review and comparison of these methods are necessary. In this study, we divide these methods into four different categories, including sequence-based methods, 3D structure-based methods, PPI network-based methods and hybrid information-based methods. Furthermore, their advantages and disadvantages are discussed, and then their performance is comprehensively evaluated and compared. Finally, we discuss the challenges and opportunities present in this field.

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Section: Discussionmentioning

confidence: 99%

A comprehensive review and comparison of existing computational methods for protein function prediction

Lin,

Luo,

Liu

et al. 2024

Briefings in Bioinformatics

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“…7−10 While traditional biological experimental methods exhibit high accuracy in discovering potential correlations, the process is intricate and time-consuming. 11,12 Hence, capitalizing on the rapid development of computer technology, developing an efficient and convenient computational method for detecting the correlation between LncRNAs and diseases is of significant importance. 13−16 Cheng et al 17 presented the first solution for predicting associations in LncRNA-disease relationships.…”

Section: ■ Introductionmentioning

confidence: 99%

“…In esophageal cancer (EC), the long noncoding RNA ADAMTS9-AS2 effectively suppresses cancer cell proliferation, invasion, and migration processes . Therefore, investigating potential associations between LncRNAs and diseases contributes to the prevention, detection, and treatment of relevant diseases in humans. − While traditional biological experimental methods exhibit high accuracy in discovering potential correlations, the process is intricate and time-consuming. , Hence, capitalizing on the rapid development of computer technology, developing an efficient and convenient computational method for detecting the correlation between LncRNAs and diseases is of significant importance. − Cheng et al presented the first solution for predicting associations in LncRNA-disease relationships. Subsequently, an increasing number of computational prediction models, , including matrix factorization, have been applied to predict LncRNA-disease associations.…”

Section: Introductionmentioning

confidence: 99%

HPTRMF: Collaborative Matrix Factorization-Based Prediction Method for LncRNA-Disease Associations Using High-Order Perturbation and Flexible Trifactor Regularization

Xie,

Li,

Lin

et al. 2024

J. Chem. Inf. Model.

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Existing matrix factorization methods face challenges, including the cold start problem and global nonlinear data loss during similarity learning, particularly in predicting associations between long noncoding RNAs (LncRNAs) and diseases. To overcome these issues, we introduce HPTRMF, a matrix factorization approach incorporating high-order perturbation and flexible trifactor regularization. HPTRMF constructs a high-order correlation matrix utilizing the known association matrix, leveraging highorder perturbation to effectively address the cold start problem caused by data sparsity. Additionally, HPTRMF incorporates a flexible trifactor regularization term to capture similarity information on LncRNAs and diseases, enabling the effective handling of global nonlinear data loss by capturing such data in the similarity matrix. Experimental results demonstrate the superiority of HPTRMF over nine state-of-the-art algorithms in Leave-One-Out Cross-Validation (LOOCV) and Five-Fold Cross-Validation (5-Fold CV) on three data sets.HPTRMF and data sets are available in https://github.com/Llvvvv/HPTRMF.

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Vislocas: Vision transformers for identifying protein subcellular mis-localization signatures of different cancer subtypes from immunohistochemistry images

Wen,

Zhang,

2024

Computers in Biology and Medicine

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In silico prediction methods of self-interacting proteins: an empirical and academic survey

Cited by 6 publications

References 143 publications

A comprehensive review and comparison of existing computational methods for protein function prediction

A comprehensive review and comparison of existing computational methods for protein function prediction

HPTRMF: Collaborative Matrix Factorization-Based Prediction Method for LncRNA-Disease Associations Using High-Order Perturbation and Flexible Trifactor Regularization

Vislocas: Vision transformers for identifying protein subcellular mis-localization signatures of different cancer subtypes from immunohistochemistry images

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