Predicting Protein-Protein Interactions Using Correlation Coefficient and Principle Component Analysis

Thanathamathee, Putthiporn; Lursinsap, Chidchanok

doi:10.1109/icbbe.2009.5163211

Cited by 3 publications

(2 citation statements)

References 14 publications

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“…Eleven real-world data sets with various sizes were examined. Ten of them are available on the University of California, Irvine [35], and the rest data set is of a physical protein-protein interaction of yeast Saccharomyces Cerevisiae [36] given in S1 Dataset. The size of each data set was determined by the product of the numbers of features and data.…”

Section: Experiments and Performance Evaluationmentioning

confidence: 99%

Streaming chunk incremental learning for class-wise data stream classification with fast learning speed and low structural complexity

2019

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Due to the fast speed of data generation and collection from advanced equipment, the amount of data obviously overflows the limit of available memory space and causes difficulties achieving high learning accuracy. Several methods based on discard-after-learn concept have been proposed. Some methods were designed to cope with a single incoming datum but some were designed for a chunk of incoming data. Although the results of these approaches are rather impressive, most of them are based on temporally adding more neurons to learn new incoming data without any neuron merging process which can obviously increase the computational time and space complexities. Only online versatile elliptic basis function (VEBF) introduced neuron merging to reduce the space-time complexity of learning only a single incoming datum. This paper proposed a method for further enhancing the capability of discard-after-learn concept for streaming data-chunk environment in terms of low computational time and neural space complexities. A set of recursive functions for computing the relevant parameters of a new neuron, based on statistical confidence interval, was introduced. The newly proposed method, named streaming chunk incremental learning (SCIL), increases the plasticity and the adaptabilty of the network structure according to the distribution of incoming data and their classes. When being compared to the others in incremental-like manner, based on 11 benchmarked data sets of 150 to 581,012 samples with attributes ranging from 4 to 1,558 formed as streaming data, the proposed SCIL gave better accuracy and time in most data sets.

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Section: Experiments and Performance Evaluationmentioning

confidence: 99%

Streaming chunk incremental learning for class-wise data stream classification with fast learning speed and low structural complexity

2019

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“…Shen et al [ 20 ] used SVM as a classifier and applied the conjoint triad method for feature extraction, in which the 20 amino acids are divided into seven categories according to the volumes and dipoles of their side chains. Thanathamathee and Lursinsap [ 24 ] employed the proteinproplot algorithm for feature extraction from protein sequences. They reduced the dimensions of features using principle component analysis (PCA) and adopted the feed-forward neural network as a classifier.…”

Section: Introductionmentioning

confidence: 99%

Detection of Interactions between Proteins through Rotation Forest and Local Phase Quantization Descriptors

Wong

You

Ming

et al. 2015

IJMS

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Protein-Protein Interactions (PPIs) play a vital role in most cellular processes. Although many efforts have been devoted to detecting protein interactions by high-throughput experiments, these methods are obviously expensive and tedious. Targeting these inevitable disadvantages, this study develops a novel computational method to predict PPIs using information on protein sequences, which is highly efficient and accurate. The improvement mainly comes from the use of the Rotation Forest (RF) classifier and the Local Phase Quantization (LPQ) descriptor from the Physicochemical Property Response (PR) Matrix of protein amino acids. When performed on three PPI datasets including Saccharomyces cerevisiae, Homo sapiens, and Helicobacter pylori, we obtained good results of average accuracies of 93.8%, 97.96%, and 89.47%, which are much better than in previous studies. Extensive validations have also been explored to evaluate the performance of the Rotation Forest ensemble classifier with the state-of-the-art Support Vector Machine classifier. These promising results indicate that the proposed method might play a complementary role for future proteomics research.

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Protein–protein interaction prediction based on ordinal regression and recurrent convolutional neural networks

Gao

Yang

et al. 2021

BMC Bioinformatics

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Background Protein protein interactions (PPIs) are essential to most of the biological processes. The prediction of PPIs is beneficial to the understanding of protein functions and thus is helpful to pathological analysis, disease diagnosis and drug design etc. As the amount of protein data is growing fast in the post genomic era, high-throughput experimental methods are expensive and time-consuming for the prediction of PPIs. Thus, computational methods have attracted researcher’s attention in recent years. A large number of computational methods have been proposed based on different protein sequence encoders. Results Notably, the confidence score of a protein sequence pair could be regarded as a kind of measurement to PPIs. The higher the confidence score for one protein pair is, the more likely the protein pair interacts. Thus in this paper, a deep learning framework, called ordinal regression and recurrent convolutional neural network (OR-RCNN) method, is introduced to predict PPIs from the perspective of confidence score. It mainly contains two parts: the encoder part of protein sequence pair and the prediction part of PPIs by confidence score. In the first part, two recurrent convolutional neural networks (RCNNs) with shared parameters are applied to construct two protein sequence embedding vectors, which can automatically extract robust local features and sequential information from the protein pairs. Based on it, the two embedding vectors are encoded into one novel embedding vector by element-wise multiplication. By taking the ordinal information behind confidence score into consideration, ordinal regression is used to construct multiple sub-classifiers in the second part. The results of multiple sub-classifiers are aggregated to obtain the final confidence score. Following that, the existence of PPIs is determined by the confidence score. We set a threshold $$\theta$$ θ , and say the interaction exists between the protein pair if its confidence score is bigger than $$\theta$$ θ . Conclusions We applied our method to predict PPIs on data sets S. cerevisiae and Homo sapiens. Through experimental verification, our method outperforms state-of-the-art PPI prediction models.

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Predicting Protein-Protein Interactions Using Correlation Coefficient and Principle Component Analysis

Cited by 3 publications

References 14 publications

Streaming chunk incremental learning for class-wise data stream classification with fast learning speed and low structural complexity

Streaming chunk incremental learning for class-wise data stream classification with fast learning speed and low structural complexity

Detection of Interactions between Proteins through Rotation Forest and Local Phase Quantization Descriptors

Protein–protein interaction prediction based on ordinal regression and recurrent convolutional neural networks

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