An Ensemble Classifier with Random Projection for Predicting Protein–Protein Interactions Using Sequence and Evolutionary Information

Song, Xiaoyu; Chen, Zhan-Heng; Sun, Xiangyang; You, Zhu-Hong; Li, Liping; Yang, Zaihui

doi:10.3390/app8010089

Cited by 26 publications

(10 citation statements)

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“…But it seems that (Wang et al, 2017b) obtained the highest performance according to each evaluation metric and the following one is RFEC (Song et al, 2018). Note that, the Legendre moments (LMs) (Wang et al, 2017b), Zernike moments (ZM) descriptor (Wang et al, 2017a) and the evolutionary information (Song et al, 2018) all extracted discriminatory information embedded in the position-specific scoring matrix (PSSM) which is generated by Position-Specific Iterated Basic Local Alignment Search Tool (PSI-BLAST) (Altschul et al, 1997). Above three methods run much slower because it is required to run BLAST against a huge protein NR database to generate a PSSM matrix as its feature.…”

Section: Resultsmentioning

confidence: 99%

An integration of deep learning with feature embedding for protein–protein interaction prediction

Yu¹,

Du²,

Diao³

et al. 2019

PeerJ

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Protein–protein interactions are closely relevant to protein function and drug discovery. Hence, accurately identifying protein–protein interactions will help us to understand the underlying molecular mechanisms and significantly facilitate the drug discovery. However, the majority of existing computational methods for protein–protein interactions prediction are focused on the feature extraction and combination of features and there have been limited gains from the state-of-the-art models. In this work, a new residue representation method named Res2vec is designed for protein sequence representation. Residue representations obtained by Res2vec describe more precisely residue-residue interactions from raw sequence and supply more effective inputs for the downstream deep learning model. Combining effective feature embedding with powerful deep learning techniques, our method provides a general computational pipeline to infer protein–protein interactions, even when protein structure knowledge is entirely unknown. The proposed method DeepFE-PPI is evaluated on the S. Cerevisiae and human datasets. The experimental results show that DeepFE-PPI achieves 94.78% (accuracy), 92.99% (recall), 96.45% (precision), 89.62% (Matthew’s correlation coefficient, MCC) and 98.71% (accuracy), 98.54% (recall), 98.77% (precision), 97.43% (MCC), respectively. In addition, we also evaluate the performance of DeepFE-PPI on five independent species datasets and all the results are superior to the existing methods. The comparisons show that DeepFE-PPI is capable of predicting protein–protein interactions by a novel residue representation method and a deep learning classification framework in an acceptable level of accuracy. The codes along with instructions to reproduce this work are available from https://github.com/xal2019/DeepFE-PPI.

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Section: Resultsmentioning

confidence: 99%

An integration of deep learning with feature embedding for protein–protein interaction prediction

Yu¹,

Du²,

Diao³

et al. 2019

PeerJ

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“…In mathematics and statistics, Random Projection (RP) is a classifier for dimensionality reduction of some points which lie in Euclidean space. RP classifier showed that N points in N dimensional space can almost always be mapped to a space of dimension ClogN with command on the ratio of error and distances [46, 47]. It has been successfully applied in rebuilding of frequency-sparse signals [48], face recognition [49], protein subcellular localization [50] and textual and visual information retrieval [51].…”

Section: Materials and Methodologymentioning

confidence: 99%

Identification of self-interacting proteins by integrating random projection classifier and finite impulse response filter

Chen

You

et al. 2019

BMC Genomics

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BackgroundIdentification of protein-protein interactions (PPIs) is crucial for understanding biological processes and investigating the cellular functions of genes. Self-interacting proteins (SIPs) are those in which more than two identical proteins can interact with each other and they are the specific type of PPIs. More and more researchers draw attention to the SIPs detection, and several prediction model have been proposed, but there are still some problems. Hence, there is an urgent need to explore a efficient computational model for SIPs prediction.ResultsIn this study, we developed an effective model to predict SIPs, called RP-FIRF, which merges the Random Projection (RP) classifier and Finite Impulse Response Filter (FIRF) together. More specifically, each protein sequence was firstly transformed into the Position Specific Scoring Matrix (PSSM) by exploiting Position Specific Iterated BLAST (PSI-BLAST). Then, to effectively extract the discriminary SIPs feature to improve the performance of SIPs prediction, a FIRF method was used on PSSM. The R’classifier was proposed to execute the classification and predict novel SIPs. We evaluated the performance of the proposed RP-FIRF model and compared it with the state-of-the-art support vector machine (SVM) on human and yeast datasets, respectively. The proposed model can achieve high average accuracies of 97.89 and 97.35% using five-fold cross-validation. To further evaluate the high performance of the proposed method, we also compared it with other six exiting methods, the experimental results demonstrated that the capacity of our model surpass that of the other previous approaches.ConclusionExperimental results show that self-interacting proteins are accurately well-predicted by the proposed model on human and yeast datasets, respectively. It fully show that the proposed model can predict the SIPs effectively and sufficiently. Thus, RP-FIRF model is an automatic decision support method which should provide useful insights into the recognition of SIPs.

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“…The method was then tested on three databases, using fivefold cross-validation. The accuracies reported ranged from 88 to 97% depending on the dataset used [124].…”

Section: Binding Affinities and Interactionsmentioning

confidence: 99%

Artificial Intelligence in Drug Design: Algorithms, Applications, Challenges and Ethics

Arabi

2021

Future Drug. Discov.

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The discovery paradigm of drugs is rapidly growing due to advances in machine learning (ML) and artificial intelligence (AI). This review covers myriad faces of AI and ML in drug design. There is a plethora of AI algorithms, the most common of which are summarized in this review. In addition, AI is fraught with challenges that are highlighted along with plausible solutions to them. Examples are provided to illustrate the use of AI and ML in drug discovery and in predicting drug properties such as binding affinities and interactions, solubility, toxicology, blood–brain barrier permeability and chemical properties. The review also includes examples depicting the implementation of AI and ML in tackling intractable diseases such as COVID-19, cancer and Alzheimer’s disease. Ethical considerations and future perspectives of AI are also covered in this review.

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An Ensemble Classifier with Random Projection for Predicting Protein–Protein Interactions Using Sequence and Evolutionary Information

Cited by 26 publications

References 50 publications

An integration of deep learning with feature embedding for protein–protein interaction prediction

An integration of deep learning with feature embedding for protein–protein interaction prediction

Identification of self-interacting proteins by integrating random projection classifier and finite impulse response filter

Artificial Intelligence in Drug Design: Algorithms, Applications, Challenges and Ethics

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