MHAM-NPI: Predicting ncRNA-protein interactions based on multi-head attention mechanism

Zhou, Ziqing; Du, Zhenya; Wei, Jinhang; Zhuo, Linlin; Pan, Shiyao; Fu, Xiquan; Lian, Xinze

doi:10.1016/j.compbiomed.2023.107143

Cited by 11 publications

(2 citation statements)

References 32 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…These output vectors can be concatenated or averaged to produce the final MHSA representation. This representation can be used for various downstream tasks such as semantic understanding, named entity recognition, machine translation ( Zhou et al, 2023 ), etc. The final MHSA representation is shown as Eq.…”

Section: Methodsmentioning

confidence: 99%

MIFAM-DTI: a drug-target interactions predicting model based on multi-source information fusion and attention mechanism

Li,

Sun,

Liu

et al. 2024

Front. Genet.

View full text Add to dashboard Cite

Accurate identification of potential drug-target pairs is a crucial step in drug development and drug repositioning, which is characterized by the ability of the drug to bind to and modulate the activity of the target molecule, resulting in the desired therapeutic effect. As machine learning and deep learning technologies advance, an increasing number of models are being engaged for the prediction of drug-target interactions. However, there is still a great challenge to improve the accuracy and efficiency of predicting. In this study, we proposed a deep learning method called Multi-source Information Fusion and Attention Mechanism for Drug-Target Interaction (MIFAM-DTI) to predict drug-target interactions. Firstly, the physicochemical property feature vector and the Molecular ACCess System molecular fingerprint feature vector of a drug were extracted based on its SMILES sequence. The dipeptide composition feature vector and the Evolutionary Scale Modeling -1b feature vector of a target were constructed based on its amino acid sequence information. Secondly, the PCA method was employed to reduce the dimensionality of the four feature vectors, and the adjacency matrices were constructed by calculating the cosine similarity. Thirdly, the two feature vectors of each drug were concatenated and the two adjacency matrices were subjected to a logical OR operation. And then they were fed into a model composed of graph attention network and multi-head self-attention to obtain the final drug feature vectors. With the same method, the final target feature vectors were obtained. Finally, these final feature vectors were concatenated, which served as the input to a fully connected layer, resulting in the prediction output. MIFAM-DTI not only integrated multi-source information to capture the drug and target features more comprehensively, but also utilized the graph attention network and multi-head self-attention to autonomously learn attention weights and more comprehensively capture information in sequence data. Experimental results demonstrated that MIFAM-DTI outperformed state-of-the-art methods in terms of AUC and AUPR. Case study results of coenzymes involved in cellular energy metabolism also demonstrated the effectiveness and practicality of MIFAM-DTI. The source code and experimental data for MIFAM-DTI are available at https://github.com/Search-AB/MIFAM-DTI.

show abstract

Section: Methodsmentioning

confidence: 99%

MIFAM-DTI: a drug-target interactions predicting model based on multi-source information fusion and attention mechanism

Li,

Sun,

Liu

et al. 2024

Front. Genet.

View full text Add to dashboard Cite

show abstract

“…Machine learning methods have seen substantial success in the field of biology [26][27][28], spurring the rapid advancement of research related to DNA methylation prediction. DNA4mC-LIP employs a linear weighting strategy to integrate six prediction models and constructs mixed features to predict 4mC sites [29].…”

Section: Introductionmentioning

confidence: 99%

StableDNAm: towards a stable and efficient model for predicting DNA methylation based on adaptive feature correction learning

Zhuo,

Wang,

et al. 2023

BMC Genomics

Self Cite

View full text Add to dashboard Cite

Background DNA methylation, instrumental in numerous life processes, underscores the paramount importance of its accurate prediction. Recent studies suggest that deep learning, due to its capacity to extract profound insights, provides a more precise DNA methylation prediction. However, issues related to the stability and generalization performance of these models persist. Results In this study, we introduce an efficient and stable DNA methylation prediction model. This model incorporates a feature fusion approach, adaptive feature correction technology, and a contrastive learning strategy. The proposed model presents several advantages. First, DNA sequences are encoded at four levels to comprehensively capture intricate information across multi-scale and low-span features. Second, we design a sequence-specific feature correction module that adaptively adjusts the weights of sequence features. This improvement enhances the model’s stability and scalability, or its generality. Third, our contrastive learning strategy mitigates the instability issues resulting from sparse data. To validate our model, we conducted multiple sets of experiments on commonly used datasets, demonstrating the model’s robustness and stability. Simultaneously, we amalgamate various datasets into a single, unified dataset. The experimental outcomes from this combined dataset substantiate the model’s robust adaptability. Conclusions Our research findings affirm that the StableDNAm model is a general, stable, and effective instrument for DNA methylation prediction. It holds substantial promise for providing invaluable assistance in future methylation-related research and analyses.

show abstract

ncRS: A resource of non-coding RNAs in sepsis

Zhong,

Dai,

Chen

et al. 2024

Computers in Biology and Medicine

View full text Add to dashboard Cite

MHAM-NPI: Predicting ncRNA-protein interactions based on multi-head attention mechanism

Cited by 11 publications

References 32 publications

MIFAM-DTI: a drug-target interactions predicting model based on multi-source information fusion and attention mechanism

MIFAM-DTI: a drug-target interactions predicting model based on multi-source information fusion and attention mechanism

StableDNAm: towards a stable and efficient model for predicting DNA methylation based on adaptive feature correction learning

ncRS: A resource of non-coding RNAs in sepsis

Contact Info

Product

Resources

About