Improving Prediction of Self-interacting Proteins Using Stacked Sparse Auto-Encoder with PSSM profiles

Wang, Yanbin; You, Zhu-Hong; Li, Liping; Huang, De-Shuang; Zhou, Fengfeng; Yang, Shan

doi:10.7150/ijbs.23817

Cited by 20 publications

(11 citation statements)

References 46 publications

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“…We discovered distantly correlative proteins by applying the Position-Specific Scoring Matrix (PSSM) [ 55 , 56 , 57 ], which is a helpful tool. Therefore, a PSSM can be transformed from each protein sequence information by applying the Position-Specific Iterated BLAST (PSI-BLAST) [ 58 ].…”

Section: Materials and Methodologymentioning

confidence: 99%

Prediction of Self-Interacting Proteins from Protein Sequence Information Based on Random Projection Model and Fast Fourier Transform

Chen

You

et al. 2019

IJMS

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It is significant for biological cells to predict self-interacting proteins (SIPs) in the field of bioinformatics. SIPs mean that two or more identical proteins can interact with each other by one gene expression. This plays a major role in the evolution of protein‒protein interactions (PPIs) and cellular functions. Owing to the limitation of the experimental identification of self-interacting proteins, it is more and more significant to develop a useful biological tool for the prediction of SIPs from protein sequence information. Therefore, we propose a novel prediction model called RP-FFT that merges the Random Projection (RP) model and Fast Fourier Transform (FFT) for detecting SIPs. First, each protein sequence was transformed into a Position Specific Scoring Matrix (PSSM) using the Position Specific Iterated BLAST (PSI-BLAST). Second, the features of protein sequences were extracted by the FFT method on PSSM. Lastly, we evaluated the performance of RP-FFT and compared the RP classifier with the state-of-the-art support vector machine (SVM) classifier and other existing methods on the human and yeast datasets; after the five-fold cross-validation, the RP-FFT model can obtain high average accuracies of 96.28% and 91.87% on the human and yeast datasets, respectively. The experimental results demonstrated that our RP-FFT prediction model is reasonable and robust.

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Section: Materials and Methodologymentioning

confidence: 99%

Prediction of Self-Interacting Proteins from Protein Sequence Information Based on Random Projection Model and Fast Fourier Transform

Chen

You

et al. 2019

IJMS

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“…From the biological perspective, PSSM is a matrix that is used to distinguish the similarity of two sequences, since PSSM are able to predict quaternary structural attributes, protein disulfide connectivity, and folding pattern [ 22 , 23 , 24 ]. Each element of the PSSM indicates the probability of the substitution of an amino acid to another amino acid [ 25 ]. If the replacement of these two amino acids is frequent, then it indicates that this substitution can be accepted by nature with high amino acid substitution scores [ 26 ].…”

Section: Methodsmentioning

confidence: 99%

BGFE: A Deep Learning Model for ncRNA-Protein Interaction Predictions Based on Improved Sequence Information

Zhan

Jia

Zhou

et al. 2019

IJMS

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The interactions between ncRNAs and proteins are critical for regulating various cellular processes in organisms, such as gene expression regulations. However, due to limitations, including financial and material consumptions in recent experimental methods for predicting ncRNA and protein interactions, it is essential to propose an innovative and practical approach with convincing performance of prediction accuracy. In this study, based on the protein sequences from a biological perspective, we put forward an effective deep learning method, named BGFE, to predict ncRNA and protein interactions. Protein sequences are represented by bi-gram probability feature extraction method from Position Specific Scoring Matrix (PSSM), and for ncRNA sequences, k-mers sparse matrices are employed to represent them. Furthermore, to extract hidden high-level feature information, a stacked auto-encoder network is employed with the stacked ensemble integration strategy. We evaluate the performance of the proposed method by using three datasets and a five-fold cross-validation after classifying the features through the random forest classifier. The experimental results clearly demonstrate the effectiveness and the prediction accuracy of our approach. In general, the proposed method is helpful for ncRNA and protein interacting predictions and it provides some serviceable guidance in future biological research.

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“…Chen et al 15 combined protein sequence information with wavelet transform, and predicted self-interacting proteins accurately through deep forest predictor. Wang et al 16 proposed a prediction model for SIPs based on machine learning algorithms, which combines the Zernike Moments (ZMs) descriptor on protein sequences with the Probabilistic Classification Vector Machines (PCVM) and Stacked Sparse Auto-Encoder (SSAE), and classifies the self-interaction of proteins by Probabilistic Classification Vector Machine (PCVM).…”

Section: Introductionmentioning

confidence: 99%

NLPEI: A Novel Self-Interacting Protein Prediction Model Based on Natural Language Processing and Evolutionary Information

Jia

Yan

You

et al. 2020

Evol Bioinform Online

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The study of protein self-interactions (SIPs) can not only reveal the function of proteins at the molecular level, but is also crucial to understand activities such as growth, development, differentiation, and apoptosis, providing an important theoretical basis for exploring the mechanism of major diseases. With the rapid advances in biotechnology, a large number of SIPs have been discovered. However, due to the long period and high cost inherent to biological experiments, the gap between the identification of SIPs and the accumulation of data is growing. Therefore, fast and accurate computational methods are needed to effectively predict SIPs. In this study, we designed a new method, NLPEI, for predicting SIPs based on natural language understanding theory and evolutionary information. Specifically, we first understand the protein sequence as natural language and use natural language processing algorithms to extract its features. Then, we use the Position-Specific Scoring Matrix (PSSM) to represent the evolutionary information of the protein and extract its features through the Stacked Auto-Encoder (SAE) algorithm of deep learning. Finally, we fuse the natural language features of proteins with evolutionary features and make accurate predictions by Extreme Learning Machine (ELM) classifier. In the SIPs gold standard data sets of human and yeast, NLPEI achieved 94.19% and 91.29% prediction accuracy. Compared with different classifier models, different feature models, and other existing methods, NLPEI obtained the best results. These experimental results indicated that NLPEI is an effective tool for predicting SIPs and can provide reliable candidates for biological experiments.

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Improving Prediction of Self-interacting Proteins Using Stacked Sparse Auto-Encoder with PSSM profiles

Cited by 20 publications

References 46 publications

Prediction of Self-Interacting Proteins from Protein Sequence Information Based on Random Projection Model and Fast Fourier Transform

Prediction of Self-Interacting Proteins from Protein Sequence Information Based on Random Projection Model and Fast Fourier Transform

BGFE: A Deep Learning Model for ncRNA-Protein Interaction Predictions Based on Improved Sequence Information

NLPEI: A Novel Self-Interacting Protein Prediction Model Based on Natural Language Processing and Evolutionary Information

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