Review of Protein Subcellular Localization Prediction

Wang, Zhen; Zou, Quan; Jiang, Yi; Ju, Ying; Zeng, Xiangxiang

doi:10.2174/1574893609666140212000304

Cited by 33 publications

(19 citation statements)

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“…There are various variations of protein sequences occurring in the biological evolution process, for instance, the insertion, substitution or deletion of one or several amino acid residues in the sequence [ 21 ]. With long-term accumulation of these variations, the similarities between the original and the new synthesis proteins are reducing gradually, but these homologous proteins may exhibit remarkably similar structures and functions [ 22 ].…”

Section: The Related Workmentioning

confidence: 99%

“…On the other hand, simplicity is also an important principle in machine learning. A compact representation can yield a preferred prediction model [ 21 ]. Therefore, this paper first proposes two effective fusion representations by combining two single representations, respectively, and then uses the dimension reduction method of linear discriminant analysis (LDA) to arrive at an optimal expression for k -nearest neighbors classifier (KNN).…”

Section: Introductionmentioning

confidence: 99%

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Protein Sub-Nuclear Localization Based on Effective Fusion Representations and Dimension Reduction Algorithm LDA

Wang

Liu

2015

IJMS

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An effective representation of a protein sequence plays a crucial role in protein sub-nuclear localization. The existing representations, such as dipeptide composition (DipC), pseudo-amino acid composition (PseAAC) and position specific scoring matrix (PSSM), are insufficient to represent protein sequence due to their single perspectives. Thus, this paper proposes two fusion feature representations of DipPSSM and PseAAPSSM to integrate PSSM with DipC and PseAAC, respectively. When constructing each fusion representation, we introduce the balance factors to value the importance of its components. The optimal values of the balance factors are sought by genetic algorithm. Due to the high dimensionality of the proposed representations, linear discriminant analysis (LDA) is used to find its important low dimensional structure, which is essential for classification and location prediction. The numerical experiments on two public datasets with KNN classifier and cross-validation tests showed that in terms of the common indexes of sensitivity, specificity, accuracy and MCC, the proposed fusing representations outperform the traditional representations in protein sub-nuclear localization, and the representation treated by LDA outperforms the untreated one.

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Section: The Related Workmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Protein Sub-Nuclear Localization Based on Effective Fusion Representations and Dimension Reduction Algorithm LDA

Wang

Liu

2015

IJMS

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“…Predicting protein function is one of the most basic research topics in bioinformatics. It involves predicting protein-protein interactions and interaction sites [ 68 , 69 ], localizing subcellular protein [ 70 – 78 ], predicting and classifying transmembrane protein [ 79 – 82 ], protein remote homology detection [ 83 , 84 ], classifying protein functions [ 85 – 93 ], recognizing multifunctional enzymes [ 94 – 96 ], and DNA binding protein identification [ 97 , 98 ].…”

Section: Applying Text Mining Technologies To Protein Researchmentioning

confidence: 99%

Survey of Natural Language Processing Techniques in Bioinformatics

Zeng

Shi

et al. 2015

Computational and Mathematical Methods in Medicine

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Informatics methods, such as text mining and natural language processing, are always involved in bioinformatics research. In this study, we discuss text mining and natural language processing methods in bioinformatics from two perspectives. First, we aim to search for knowledge on biology, retrieve references using text mining methods, and reconstruct databases. For example, protein-protein interactions and gene-disease relationship can be mined from PubMed. Then, we analyze the applications of text mining and natural language processing techniques in bioinformatics, including predicting protein structure and function, detecting noncoding RNA. Finally, numerous methods and applications, as well as their contributions to bioinformatics, are discussed for future use by text mining and natural language processing researchers.

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“…Computational predictions of protein subcellular localizations have been heavily studied in bioinformatics. In the early 1990s, computational systems were developed to recognize the sorting signals from the primary sequences of proteins (Nakai and Kanehisa, 1991;Nakai and Horton, 1999;Wang et al, 2014). When statistical sequence features were introduced to represent protein sequences, machine learningbased algorithms were employed to predict protein sorting destinations.…”

Section: Introductionmentioning

confidence: 99%

Predicting Endoplasmic Reticulum Resident Proteins Using Auto-Cross Covariance Transformation With a U-Shaped Residue Weight-Transfer Function

Miao

Zhao

et al. 2019

Front. Genet.

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Background: The endoplasmic reticulum (ER) is an important organelle in eukaryotic cells. It is involved in many important biological processes, such as cell metabolism, protein synthesis, and post-translational modification. The proteins that reside within the ER are called ER-resident proteins. These proteins are closely related to the biological functions of the ER. The difference between the ER-resident proteins and other nonresident proteins should be carefully studied. Methods: We developed a support vector machine (SVM)-based method. We developed a U-shaped weight-transfer function and used it, along with the positional-specific physiochemical properties (PSPCP), to integrate together sequence order information, signaling peptides information, and evolutionary information. Result: Our method achieved over 86% accuracy in a jackknife test. We also achieved roughly 86% sensitivity and 67% specificity in an independent dataset test. Our method is capable of identifying ER-resident proteins.

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Review of Protein Subcellular Localization Prediction

Cited by 33 publications

References 0 publications

Protein Sub-Nuclear Localization Based on Effective Fusion Representations and Dimension Reduction Algorithm LDA

Protein Sub-Nuclear Localization Based on Effective Fusion Representations and Dimension Reduction Algorithm LDA

Survey of Natural Language Processing Techniques in Bioinformatics

Predicting Endoplasmic Reticulum Resident Proteins Using Auto-Cross Covariance Transformation With a U-Shaped Residue Weight-Transfer Function

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