DeepDBP: Deep neural networks for identification of DNA-binding proteins

Shadab, Shadman; Khan, Tawab; Neezi, Nazia Afrin; Adilina, Sheikh; Shatabda, Swakkhar

doi:10.1016/j.imu.2020.100318

Cited by 30 publications

(36 citation statements)

References 34 publications

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“…This strategy applied to Deep-Sea [38], and DeepBind models significantly improved AUC. DeepDBP-CNN, inspired by previously existing models like DeepBind, used pre-learned embedding and CNN and produced a training accuracy of > 94%, a sensitivity of 0.83, and an AUC of 0.986 [28]. A comparison of Deep-DBP-CNN with other methods showed promising results.…”

Section: Prediction Of Dna/rna Binding Sites In Proteinsmentioning

confidence: 95%

“…For instance, DeepECA, a model predicting protein contact from multiple sequence alignment, obtained the 1D amino acid sequence data using PISCES, a PDB sequence culling server [27]. Similarly, for DNA-binding protein identification, Shadab et al extracted information from Protein Data Bank (PDB) and named the training dataset as 'PDB1075′ [28]. Training a deep CNN from scratch has its challenges.…”

Section: Training a Neural Networkmentioning

confidence: 99%

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Convolutional neural networks (CNNs): concepts and applications in pharmacogenomics

Vaz

Balaji

2021

Mol Divers

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Convolutional neural networks (CNNs) have been used to extract information from various datasets of different dimensions. This approach has led to accurate interpretations in several subfields of biological research, like pharmacogenomics, addressing issues previously faced by other computational methods. With the rising attention for personalized and precision medicine, scientists and clinicians have now turned to artificial intelligence systems to provide them with solutions for therapeutics development. CNNs have already provided valuable insights into biological data transformation. Due to the rise of interest in precision and personalized medicine, in this review, we have provided a brief overview of the possibilities of implementing CNNs as an effective tool for analyzing one-dimensional biological data, such as nucleotide and protein sequences, as well as small molecular data, e.g., simplified molecular-input line-entry specification, InChI, binary fingerprints, etc., to categorize the models based on their objective and also highlight various challenges. The review is organized into specific research domains that participate in pharmacogenomics for a more comprehensive understanding. Furthermore, the future intentions of deep learning are outlined.

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Section: Prediction Of Dna/rna Binding Sites In Proteinsmentioning

confidence: 95%

Section: Training a Neural Networkmentioning

confidence: 99%

Convolutional neural networks (CNNs): concepts and applications in pharmacogenomics

Vaz

Balaji

2021

Mol Divers

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“…The former used a generated feature set trained on the traditional neural network. The latter used a previously learned embedding and a convolution neural network ( Shadab et al, 2019 ).…”

Section: Introductionmentioning

confidence: 99%

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

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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“…Recently, sets of features based on Chou's general PseAAC have been proposed in [18] and transformed for deep learning in [19] using an embedding layer common in natural language processing. Convolutional neural networks (CNNs) have also been trained on sequence-based descriptors in [20][21][22][23][24][25][26]. In [20], for instance, CNNs were trained on amino acid sequences combined with contextual information, and, in [26], several CNNs and recurrent neural networks (RNNs), as well as combinations of the two, were trained on sequential descriptors and compared.…”

Section: Introductionmentioning

confidence: 99%

Robust ensemble of handcrafted and learned approaches for DNA-binding proteins

Nanni

Brahnam

2021

ACI

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PurposeAutomatic DNA-binding protein (DNA-BP) classification is now an essential proteomic technology. Unfortunately, many systems reported in the literature are tested on only one or two datasets/tasks. The purpose of this study is to create the most optimal and universal system for DNA-BP classification, one that performs competitively across several DNA-BP classification tasks.Design/methodology/approachEfficient DNA-BP classifier systems require the discovery of powerful protein representations and feature extraction methods. Experiments were performed that combined and compared descriptors extracted from state-of-the-art matrix/image protein representations. These descriptors were trained on separate support vector machines (SVMs) and evaluated. Convolutional neural networks with different parameter settings were fine-tuned on two matrix representations of proteins. Decisions were fused with the SVMs using the weighted sum rule and evaluated to experimentally derive the most powerful general-purpose DNA-BP classifier system.FindingsThe best ensemble proposed here produced comparable, if not superior, classification results on a broad and fair comparison with the literature across four different datasets representing a variety of DNA-BP classification tasks, thereby demonstrating both the power and generalizability of the proposed system.Originality/valueMost DNA-BP methods proposed in the literature are only validated on one (rarely two) datasets/tasks. In this work, the authors report the performance of our general-purpose DNA-BP system on four datasets representing different DNA-BP classification tasks. The excellent results of the proposed best classifier system demonstrate the power of the proposed approach. These results can now be used for baseline comparisons by other researchers in the field.

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DeepDBP: Deep neural networks for identification of DNA-binding proteins

Cited by 30 publications

References 34 publications

Convolutional neural networks (CNNs): concepts and applications in pharmacogenomics

Convolutional neural networks (CNNs): concepts and applications in pharmacogenomics

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

Robust ensemble of handcrafted and learned approaches for DNA-binding proteins

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