A new hybrid coding for protein secondary structure prediction based on primary structure similarity

Li, Zhong; Wang, Jing; Zhang, Shunpu; Zhang, Zuo‐Feng; Wu, Wuming

doi:10.1016/j.gene.2017.03.011

Cited by 18 publications

(9 citation statements)

References 18 publications

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“…Bouziane et al [7] also investigated the effect of two input data types including Position-Specific Scoring Matrix (PSSM) [38] and a coding scheme which is used to represent the amino-acids. -Support Vector Machine Using Hybrid Coding for Protein (SVM-HC): The method proposed by Li et al [30] initially employs the geometry-based similarities and where the similarity comparison is not applicable, the SVM module will be used to leverage the final protein structure. Moreover, SVM-HC extracts a 6-bit code from the physiochemical properties of both amino-acids and the tendency factors.…”

Section: Comparison and Discussionmentioning

confidence: 99%

“…Feature extraction from protein sequences is the first step when applying a machine learning approach. However, the extracted features may not reflect all the information a sequence contains and thus can lead to information loss [10,30,33]. Nevertheless, from biological perspective, protein sequence contains indispensable information to adopt certain structures [31,33,34].…”

Section: Introductionmentioning

confidence: 99%

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MCP: A multi-component learning machine to predict protein secondary structure

Khalatbari

Kangavari

Hosseini

et al. 2019

Computers in Biology and Medicine

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The Gene or DNA sequence in every cell does not control genetic properties on its own; Rather, this is done through translation of DNA into protein and subsequent formation of a certain 3D structure. The biological function of a protein is tightly connected to its specific 3D structure. Prediction of the protein secondary structure is a crucial intermediate step towards elucidating its 3D structure and function. Traditional experimental methods for prediction of protein structure are expensive and time-consuming. Therefore, various machine learning approaches have been proposed to predict the protein secondary structure. Nevertheless, the average accuracy of the suggested solutions has hardly reached beyond 80%. The possible underlying reasons are the ambiguous sequence-structure relation, noise in input protein data, class imbalance, and the high dimensionality of the encoding schemes that represent the protein sequence. In this paper, we propose an accurate multi-component prediction machine to overcome the challenges of protein structure prediction. We devise a multi-component designation to address the high complexity challenge in sequence-structure relation. Furthermore, we utilize a compound string dissimilarity measure to directly interpret protein sequence content and avoid information loss. In order to improve the accuracy, we employ two different classifiers including support vector machine and fuzzy nearest neighbor and collectively aggregate the classification outcomes to infer the final protein secondary structures. We conduct comprehensive experiments to compare our model with the current state-of-the-art approaches. The experimental results demonstrate that given a set of input sequences, our multi-component framework can accurately predict the protein structure. Nevertheless, the effectiveness of our unified model can be further enhanced through framework configuration.

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Section: Comparison and Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

MCP: A multi-component learning machine to predict protein secondary structure

Khalatbari

Kangavari

Hosseini

et al. 2019

Computers in Biology and Medicine

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show abstract

“…We did not perform additional attribute evaluations with other methods, such as PCA 28 , because our set of attributes is already small, i.e., this is not the case of high dimensionality issues. Furthermore, the IG analysis and the density plots clearly demonstrate that all nine classification attributes chosen in this work present some degree of relevance.…”

Section: Methodsmentioning

confidence: 99%

Rama: a machine learning approach for ribosomal protein prediction in plants

Carvalho

Silva

Calil

et al. 2017

Sci Rep

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Ribosomal proteins (RPs) play a fundamental role within all type of cells, as they are major components of ribosomes, which are essential for translation of mRNAs. Furthermore, these proteins are involved in various physiological and pathological processes. The intrinsic biological relevance of RPs motivated advanced studies for the identification of unrevealed RPs. In this work, we propose a new computational method, termed Rama, for the prediction of RPs, based on machine learning techniques, with a particular interest in plants. To perform an effective classification, Rama uses a set of fundamental attributes of the amino acid side chains and applies a two-step procedure to classify proteins with unknown function as RPs. The evaluation of the resultant predictive models showed that Rama could achieve mean sensitivity, precision, and specificity of 0.91, 0.91, and 0.82, respectively. Furthermore, a list of proteins that have no annotation in Phytozome v.10, and are annotated as RPs in Phytozome v.12, were correctly classified by our models. Additional computational experiments have also shown that Rama presents high accuracy to differentiate ribosomal proteins from RNA-binding proteins. Finally, two novel proteins of Arabidopsis thaliana were validated in biological experiments. Rama is freely available at http://inctipp.bioagro.ufv.br:8080/Rama.

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“…For example, Ref [25] proposed an ensemble method that combines four predictors and uses a random forest classifier for the prediction of human sub-cellular localization. Four predictors, based either on support vector machine [26] or naive Bayes [27], employed different features of protein to make predictions.…”

Section: The Ensemble Algorithm Based On Bi-lstmmentioning

confidence: 99%

A Bi-LSTM Based Ensemble Algorithm for Prediction of Protein Secondary Structure

Zhong

Elofsson

et al. 2019

Applied Sciences

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The prediction of protein secondary structure continues to be an active area of research in bioinformatics. In this paper, a Bi-LSTM based ensemble model is developed for the prediction of protein secondary structure. The ensemble model with dual loss function consists of five sub-models, which are finally joined by a Bi-LSTM layer. In contrast to existing ensemble methods, which generally train each sub-model and then join them as a whole, this ensemble model and sub-models can be trained simultaneously and the performance of each model can be observed and compared during the training process. Three independent test sets (e.g., data1199, 513 protein Cuff & Barton set (CB513) and 203 proteins from Critical Appraisals Skills Programme (CASP203)) are employed to test the method. On average, the ensemble model achieved 84.3% in Q 3 accuracy and 81.9% in segment overlap measure ( SOV ) score by using 10-fold cross validation. There is an improvement of up to 1% over some state-of-the-art prediction methods of protein secondary structure.

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A new hybrid coding for protein secondary structure prediction based on primary structure similarity

Cited by 18 publications

References 18 publications

MCP: A multi-component learning machine to predict protein secondary structure

MCP: A multi-component learning machine to predict protein secondary structure

Rama: a machine learning approach for ribosomal protein prediction in plants

A Bi-LSTM Based Ensemble Algorithm for Prediction of Protein Secondary Structure

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