Multi-Relational Naïve Bayesian Classifier towards Relational Individual

Xu, Guang-Mei; Yang, Bian; Wu, Bolin; Wei, Song; Qin, Yiqing

doi:10.1142/9789812799524_0110

Cited by 2 publications

(2 citation statements)

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“…Multi-relationship data mining can effectively prevent the problems of information loss, statistical deviation and low efficiency, etc. These methods [3][4][5] such as CrossMine, MI-MRNBC and Graph-NB are fitting for multi-relational data mining, but cannot obtain causality in a multi-relational system.…”

Section: Introductionmentioning

confidence: 99%

NHL and RCGA Based Multi-Relational Fuzzy Cognitive Map Modeling for Complex Systems

Peng

Chen

2015

Applied Sciences

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Abstract:In order to model multi-dimensions and multi-granularities oriented complex systems, this paper firstly proposes a kind of multi-relational Fuzzy Cognitive Map (FCM) to simulate the multi-relational system and its auto construct algorithm integrating Nonlinear Hebbian Learning (NHL) and Real Code Genetic Algorithm (RCGA). The multi-relational FCM fits to model the complex system with multi-dimensions and multi-granularities. The auto construct algorithm can learn the multi-relational FCM from multi-relational data resources to eliminate human intervention. The Multi-Relational Data Mining (MRDM) algorithm integrates multi-instance oriented NHL and RCGA of FCM. NHL is extended to mine the causal relationships between coarse-granularity concept and its fined-granularity concepts driven by multi-instances in the multi-relational system. RCGA is used to establish high-quality high-level FCM driven by data. The multi-relational FCM and the integrating algorithm have been applied in complex system of Mutagenesis. The experiment demonstrates not only that they get better classification accuracy, but it also shows the causal relationships among the concepts of the system.

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Section: Introductionmentioning

confidence: 99%

NHL and RCGA Based Multi-Relational Fuzzy Cognitive Map Modeling for Complex Systems

Peng

Chen

2015

Applied Sciences

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“…Gligorijević et al (2021);Zhang et al (2022);Xu et al (2022) learn residues from a local part of protein structures Jing et al (2020)Zhang et al (2023) try to capture atomic structure knowledge in proteins.…”

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confidence: 99%

ESM All-Atom: Multi-scale Protein Language Model for Unified Molecular Modeling

Zheng,

Long,

et al. 2024

Preprint

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Protein language models have demonstrated significant potential in the field of protein engineering. However, current protein language models primarily operate at the residue scale, which limits their ability to provide information at the atom level. This limitation prevents us from fully exploiting the capabilities of protein language models for applications involving both proteins and small molecules. In this paper, we propose ms-ESM (multi-scale ESM), a novel approach that enables multi-scale unified molecular modeling. ms-ESM achieves this by pre-training on multi-scale code-switch protein sequences and utilizing a multi-scale position encoding to capture relationships among residues and atoms. Experimental results indicate that ms-ESM surpasses previous methods in protein-molecule tasks, demonstrating the full utilization of protein language models. Further investigations reveal that through unified molecular modeling, ms-ESM not only gains molecular knowledge but also retains its understanding of proteins.

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Multi-Relational Naïve Bayesian Classifier towards Relational Individual

Cited by 2 publications

References 0 publications

NHL and RCGA Based Multi-Relational Fuzzy Cognitive Map Modeling for Complex Systems

NHL and RCGA Based Multi-Relational Fuzzy Cognitive Map Modeling for Complex Systems

ESM All-Atom: Multi-scale Protein Language Model for Unified Molecular Modeling

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