HKAM-MKM: A hybrid kernel alignment maximization-based multiple kernel model for identifying DNA-binding proteins

Zhao, Shulin; Ding, Yongsheng; Liu, Xiaobin; Su, Xi

doi:10.1016/j.compbiomed.2022.105395

Cited by 5 publications

(2 citation statements)

References 66 publications

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“…The EmbedCaps-DBP method's performance was compared with state-of-the-art methods using the same training and independent test datasets to ensure a fair evaluation. Table IV shows that we compared our proposed method with DNA-Prot [67], iDNA-Prot [21], iDNA-Prot|dis [68], MsDBP [31], DBP-CNN [1], and Target-DBPPred [10]. Most of these methods are sequence-based, with EI as their input.…”

Section: B Performance Comparison With Existing Predictors Using Pdb1...mentioning

confidence: 99%

“…This study used an independent dataset (PDB2272) to validate the proposed method. Table V compares the accuracy of its predictions with eight previous methods: DNA-Prot [67], iDNA-Prot [21], iDNA-Prot|dis [68], Local-DPP [20], MsDBP [31], DBP-CNN [51], Target-DBPPred [10], and HKAM-MKM [69]. EmbedCaps-DBP (ProtT5) performed better than all existing classifiers and had higher accuracy (by 12.65%), sensitivity (by 9.46%), specificity (by 14.65%), and MCC (by 0.25) than Target-DBPPred.…”

Section: B Performance Comparison With Existing Predictors Using Pdb1...mentioning

confidence: 99%

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EmbedCaps-DBP: Predicting DNA-Binding Proteins Using Protein Sequence Embedding and Capsule Network

Naim,

Mengko,

Hertadi

et al. 2023

IEEE Access

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DNA-binding interactions are an essential biological activity with important functions, such as DNA replication, transcription, repair, and recombination. DNA-binding proteins (DBPs) have been strongly associated with various human diseases, such as asthma, cancer, and HIV/AIDS. Therefore, some DBPs are used in the pharmaceutical industry to produce antibiotics, anticancer drugs, and anti-inflammatory drugs. Most previous methods have used evolutionary information to predict DBPs. However, these methods have high computing costs and produce unsatisfactory results. This study presents EmbedCaps-DBP, a new method for improving DBP prediction. First, we used three protein sequence embeddings (ProtT5, ESM-1b, and ESM-2) to extract learned feature representations from protein sequences. Those embedding methods can capture important information about amino acids, such as biophysics, biochemistry, structure, and domains, that have not been fully utilized in protein annotation tasks. Then, we used a 1D-capsule network (CapsNet) as a classifier. EmbedCaps-DBP significantly outperformed all existing classifiers in training and independent datasets. Based on two independent datasets, EmbedCaps-DBP (ProtT5) achieved 12.65% and 0.33% higher accuracies than a recent predictor on PDB2272 and PDB186, respectively. These results indicate that our proposed method is a promising predictor of DBPs.

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Section: B Performance Comparison With Existing Predictors Using Pdb1...mentioning

confidence: 99%

Section: B Performance Comparison With Existing Predictors Using Pdb1...mentioning

confidence: 99%

EmbedCaps-DBP: Predicting DNA-Binding Proteins Using Protein Sequence Embedding and Capsule Network

Naim,

Mengko,

Hertadi

et al. 2023

IEEE Access

View full text Add to dashboard Cite

show abstract

iTTCA-MVL: A multi-view learning model based on physicochemical information and sequence statistical information for tumor T cell antigens identification

Zhao,

Huang,

Niu

et al. 2024

Computers in Biology and Medicine

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Identification of CREB5 as a prognostic and immunotherapeutic biomarker in glioma through multi-omics pan-cancer analysis

Wu,

Wang,

et al. 2024

Computers in Biology and Medicine

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HKAM-MKM: A hybrid kernel alignment maximization-based multiple kernel model for identifying DNA-binding proteins

Cited by 5 publications

References 66 publications

EmbedCaps-DBP: Predicting DNA-Binding Proteins Using Protein Sequence Embedding and Capsule Network

EmbedCaps-DBP: Predicting DNA-Binding Proteins Using Protein Sequence Embedding and Capsule Network

iTTCA-MVL: A multi-view learning model based on physicochemical information and sequence statistical information for tumor T cell antigens identification

Identification of CREB5 as a prognostic and immunotherapeutic biomarker in glioma through multi-omics pan-cancer analysis

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