StackPDB: Predicting DNA-binding proteins based on XGB-RFE feature optimization and stacked ensemble classifier

Zhang, Qingmei; Liu, Peishun; Wang, Xue; Zhang, Yaqun; Yu, Han; Yu, Bin

doi:10.1016/j.asoc.2020.106921

“…RPT: RPT is a feature extraction method that reflects the evolutionary information of protein sequences ( Jeong et al , 2010 ). In the PSSM, domains with similar conservations are grouped according to conservation scores ( Wang et al , 2019 ; Zhang et al , 2021c ). Here, each particular columns corresponding are standard amino acids in the PSSM.…”

Section: Methodsmentioning

confidence: 99%

Predicting the multi-label protein subcellular localization through multi-information fusion and MLSI dimensionality reduction based on MLFE classifier

Liu

¹

,

Jin

²

,

Gao

³

et al. 2021

Self Cite

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Motivation Multi-label protein subcellular localization (SCL) is an indispensable way to study protein function. It can locate a certain protein (such as the human transmembrane protein that promotes the invasion of the SARS-CoV-2) or expression product at a specific location in a cell, which can provide a reference for clinical treatment of diseases such as COVID-19. Results The paper proposes a novel method named ML-locMLFE. First of all, six feature extraction methods are adopted to obtain protein effective information. These methods include pseudo amino acid composition (PseAAC), encoding based on grouped weight (EBGW), gene ontology (GO), multi-scale continuous and discontinuous (MCD), residue probing transformation (RPT) and evolutionary distance transformation (EDT). In the next part, we utilize the multi-label information latent semantic index (MLSI) method to avoid the interference of redundant information. In the end, multi-label learning with feature induced labeling information enrichment (MLFE) is adopted to predict the multi-label protein SCL. The Gram-positive bacteria dataset is chosen as a training set, while the Gram-negative bacteria dataset, virus dataset, newPlant dataset and SARS-CoV-2 dataset as the test sets. The overall actual accuracy (OAA) of the first four datasets is 99.23%, 93.82%, 93.24%, and 96.72% by the leave-one-out cross validation (LOOCV). It is worth mentioning that the OAA prediction result of our predictor on the SARS-CoV-2 dataset is 72.73%. The results indicate that the ML-locMLFE method has obvious advantages in predicting the SCL of multi-label protein, which provides new ideas for further research on the SCL of multi-label protein. Availability and implementation The source codes and data are publicly available at https://github.com/QUST-AIBBDRC/ML-locMLFE/. Supplementary information Supplementary data are available at Bioinformatics online.

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“…PredDBP-Stack ( Wang et al, 2020 ) improved DBP prediction performance by exploring valuable features from the HMM profile. StackPDB ( Zhang et al, 2020 ) took fusion features such as EDT, RPT, PseAAC, PsePSSM, and PSSM-TPC and then applied the stacked ensemble classifier to predict DBPs.…”

Section: Introductionmentioning

confidence: 99%

Prediction of DNA binding proteins using local features and long-term dependencies with primary sequences based on deep learning

Li

¹

,

Du

²

,

Li

³

et al. 2021

PeerJ

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DNA-binding proteins (DBPs) play pivotal roles in many biological functions such as alternative splicing, RNA editing, and methylation. Many traditional machine learning (ML) methods and deep learning (DL) methods have been proposed to predict DBPs. However, these methods either rely on manual feature extraction or fail to capture long-term dependencies in the DNA sequence. In this paper, we propose a method, called PDBP-Fusion, to identify DBPs based on the fusion of local features and long-term dependencies only from primary sequences. We utilize convolutional neural network (CNN) to learn local features and use bi-directional long-short term memory network (Bi-LSTM) to capture critical long-term dependencies in context. Besides, we perform feature extraction, model training, and model prediction simultaneously. The PDBP-Fusion approach can predict DBPs with 86.45% sensitivity, 79.13% specificity, 82.81% accuracy, and 0.661 MCC on the PDB14189 benchmark dataset. The MCC of our proposed methods has been increased by at least 9.1% compared to other advanced prediction models. Moreover, the PDBP-Fusion also gets superior performance and model robustness on the PDB2272 independent dataset. It demonstrates that the PDBP-Fusion can be used to predict DBPs from sequences accurately and effectively; the online server is at http://119.45.144.26:8080/PDBP-Fusion/.

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“…Many sequence-based methods and web servers have been developed to identify DBPs. Recent methods and server names among them are: PseDNA-Pro [20], Local-DPP [21], SVM-PSSM-DT [22], BindUP [23], PSFM-DBT [24], HMMBinder [25], iDNAProt-ES [26], DBPPred-PDSD [27], MSFBinder [28], DP-BINDER [29], and HMMPred [30].…”

Section: Introductionmentioning

confidence: 99%

“…PredDBP-Stack [21] improved DBP prediction performance by exploring valuable features from the HMM profile. StackPDB [22] took fusion features such as EDT, RPT, PseAAC, PsePSSM, and PSSM-TPC and then applied the stacked ensemble classifier to predict DBPs.…”

Section: Introductionmentioning

confidence: 99%

Peer Review #1 of "Prediction of DNA binding proteins using local features and long-term dependencies with primary sequences based on deep learning (v0.1)"

2021

0

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DNA-binding proteins (DBPs) play pivotal roles in many biological functions such as alternative splicing, RNA editing, and methylation. Many traditional machine learning (ML) methods and deep learning (DL) methods have been proposed to predict DBPs. However, these methods either rely on manual feature extraction or fail to capture long-term dependencies in the DNA sequence. In this paper, we propose a method, called PDBP-Fusion, to identify DBPs based on the fusion of local features and long-term dependencies only from primary sequences. We utilize convolutional neural network (CNN) to learn local features and use bi-directional long-short term memory network (Bi-LSTM) to capture critical long-term dependencies in context. Besides, we perform feature extraction, model training, and model prediction simultaneously. The PDBP-Fusion approach can predict DBPs with 86.45% sensitivity, 79.13% specificity, 82.81% accuracy, and 0.661 MCC on the PDB14189 benchmark dataset. The MCC of our proposed methods has been increased by at least 9.1% compared to other advanced prediction models. Moreover, the PDBP-Fusion also gets superior performance and model robustness on the PDB2272 independent dataset. It demonstrates that the PDBP-Fusion can be used to predict DBPs from sequences accurately and effectively; the online server is at http://119.45.144.26:8080/PDBP-Fusion/.

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StackPDB: Predicting DNA-binding proteins based on XGB-RFE feature optimization and stacked ensemble classifier

Cited by 45 publications

References 93 publications

Predicting the multi-label protein subcellular localization through multi-information fusion and MLSI dimensionality reduction based on MLFE classifier

Predicting the multi-label protein subcellular localization through multi-information fusion and MLSI dimensionality reduction based on MLFE classifier

Prediction of DNA binding proteins using local features and long-term dependencies with primary sequences based on deep learning

Peer Review #1 of "Prediction of DNA binding proteins using local features and long-term dependencies with primary sequences based on deep learning (v0.1)"

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