Efficient computational model for classification of protein localization images using Extended Threshold Adjacency Statistics and Support Vector Machines

Tahir, Muhammad; Jan, Bismillah; Hayat, Maqsood; Shah, Shakir Ullah; Amin, Muhammad

doi:10.1016/j.cmpb.2018.01.021

Cited by 14 publications

(10 citation statements)

References 70 publications

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“…It is remarkable that the proposed model achieved better prediction performance on two benchmark datasets and can be used to detect SIPs fairly well. However, Support Vector Machine (SVM), as a widespread data mining algorithm, has strong practicality both in machine learning and in pattern recognition, which has an excellent prediction performance especially in dealing with classification and regression problems [ 44 ]. Hence, to better understand the predictive performance of our classifier, we try to adopt the most popular SVM instead of WSRC to perform SIPs, which is a comparison experiment for the proposed method.…”

Section: Resultsmentioning

confidence: 99%

Robust and accurate prediction of self-interacting proteins from protein sequence information by exploiting weighted sparse representation based classifier

You

et al. 2022

BMC Bioinformatics

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Background Self-interacting proteins (SIPs), two or more copies of the protein that can interact with each other expressed by one gene, play a central role in the regulation of most living cells and cellular functions. Although numerous SIPs data can be provided by using high-throughput experimental techniques, there are still several shortcomings such as in time-consuming, costly, inefficient, and inherently high in false-positive rates, for the experimental identification of SIPs even nowadays. Therefore, it is more and more significant how to develop efficient and accurate automatic approaches as a supplement of experimental methods for assisting and accelerating the study of predicting SIPs from protein sequence information. Results In this paper, we present a novel framework, termed GLCM-WSRC (gray level co-occurrence matrix-weighted sparse representation based classification), for predicting SIPs automatically based on protein evolutionary information from protein primary sequences. More specifically, we firstly convert the protein sequence into Position Specific Scoring Matrix (PSSM) containing protein sequence evolutionary information, exploiting the Position Specific Iterated BLAST (PSI-BLAST) tool. Secondly, using an efficient feature extraction approach, i.e., GLCM, we extract abstract salient and invariant feature vectors from the PSSM, and then perform a pre-processing operation, the adaptive synthetic (ADASYN) technique, to balance the SIPs dataset to generate new feature vectors for classification. Finally, we employ an efficient and reliable WSRC model to identify SIPs according to the known information of self-interacting and non-interacting proteins. Conclusions Extensive experimental results show that the proposed approach exhibits high prediction performance with 98.10% accuracy on the yeast dataset, and 91.51% accuracy on the human dataset, which further reveals that the proposed model could be a useful tool for large-scale self-interacting protein prediction and other bioinformatics tasks detection in the future.

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Section: Resultsmentioning

confidence: 99%

Robust and accurate prediction of self-interacting proteins from protein sequence information by exploiting weighted sparse representation based classifier

You

et al. 2022

BMC Bioinformatics

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“…The proposed technique has shown somehow better performance through efficient exploitation of two simple methods. Moreover, Tahir et al [18] enhanced the discriminative power of TAS technique by incorporating seven threshold ranges resulting in seven binary images as compared to three [13]. The seven SVMs are trained using features from each binarized image, while the majority voting scheme delivers the final output.…”

Section: Related Workmentioning

confidence: 99%

“…These include HeLa [34], CHO [10], LOCATE datasets [13], and Yeast [4]. Next, we evaluate our network against conventional algorithms such as SVM-SubLoc [1], ETAS-Subloc [18], and IEH-GT [9] as well as convolutional neural networks such as AlexNet [22], ResNet [26], GoogleNet [35], DenseNet [33], M-CNN [24] and DeepYeast [4]. In the end, we analyze various aspects of the proposed network and present ablation studies.…”

Section: Experimental Settingsmentioning

confidence: 99%

“…Likewise, identifying suitable representative features is also a cumbersome job. Moreover, the SVM-SubLoc [1], ETAS-Subloc [18], and IEH-GT [9] are ensemble methods i.e. combination of multiple traditional classification algorithms; that is why, the performances are higher than many of the complex systems [14], [39], [40] for HeLa and CHO datasets.…”

Section: Comparisonsmentioning

confidence: 99%

“…Tahir et al [1] captured multi-resolution subspaces of each image before extracting features. Similarly, the model ETAS-SubLoc [18] for the feature extraction, performs extensive pre-processing to produce multiple thresholded images from a single protein image. Similarly, IEH-GT [9] has achieved 99.7% performance accuracy for the CHO dataset where the authors had to employ several handcrafted pre-processing and feature extraction steps for such efficient classification.…”

Section: Comparisonsmentioning

confidence: 99%

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Accurate localization of proteins from fluorescence microscopy images is a challenging task due to the inter-class similarities and intra-class disparities introducing grave concerns in addressing multi-class classification problems. Conventional machine learning-based image prediction relies heavily on preprocessing such as normalization and segmentation followed by hand-crafted feature extraction before classification to identify useful and informative as well as application specific features.We propose an end-to-end Protein Localization Convolutional Neural Network (PLCNN) that classifies protein localization images more accurately and reliably. PLCNN directly processes raw imagery without involving any pre-processing steps and produces outputs without any customization or parameter adjustment for a particular dataset. The output of our approach is computed from probabilities produced by the network. Experimental analysis is performed on five publicly available benchmark datasets. PLCNN consistently outperformed the existing state-of-the-art approaches from machine learning and deep architectures

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Feature selection are highly important to improve the classification accuracy of recognition systems for foreign matter in cotton. To address this problem, this paper presents six filter approaches of feature selection for obtaining the good feature combination with high classification accuracy and small size, and make comparisons using support vector machine and k-nearest neighbor classifier. The result shows that filter approach can efficiently find the good feature sets with high classification accuracy and small size, and the selected feature sets can effectively improve the performane of recognition system for foreign matter in cotton. The selected feature combination has smaller size and higher accuracy than original feature combination. It is important for developing the recognition systems for cotton matter using machine vision technology.

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Efficient computational model for classification of protein localization images using Extended Threshold Adjacency Statistics and Support Vector Machines

Cited by 14 publications

References 70 publications

Robust and accurate prediction of self-interacting proteins from protein sequence information by exploiting weighted sparse representation based classifier

Robust and accurate prediction of self-interacting proteins from protein sequence information by exploiting weighted sparse representation based classifier

Deep localization of subcellular protein structures from fluorescence microscopy images

Feature Selection for Cotton Matter Classification

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