iMem-2LSAAC: A two-level model for discrimination of membrane proteins and their types by extending the notion of SAAC into chou's pseudo amino acid composition

Arif, Muhammad; Hayat, Maqsood; Jan, Zahoor

doi:10.1016/j.jtbi.2018.01.008

Cited by 93 publications

(27 citation statements)

References 112 publications

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“…We also compared our representation of enzyme sample with other popular protein prediction methods, such as PseAAC (which is the most commonly used to prediction diverse protein attributes), SAAC (which has been used in predicting enzyme functional class) [24], and GM2 proposed by Xiao et al (which can catch the essence of a protein sequence and better reflect its overall pattern by grey dynamic model) [25]. Table 3 shows the average precision, subset accuracy, one error, coverage, hamming loss, and ranking loss for the dataset obtained for each approach in multi-label enzyme functional classification.…”

Section: Resultsmentioning

confidence: 99%

MF-EFP: Predicting Multi-Functional Enzymes Function Using Improved Hybrid Multi-Label Classifier

et al. 2020

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Predicting enzymes function is an important and difficult problem, particularly when enzymes may have the multiplex character, i.e., some enzymes simultaneously have two or three function classes. Most of the existing enzyme function predictor can only be used to deal with the mono-functional enzymes. Actually, multi-functional enzymes should not be ignored because they usually possess diverse biological functions worthy of our special notice. By introducing the ''improved Hybrid Multi-label Classifier'' and ''neighbor score'', a new predictor, called MF-EFP, has been developed that can be used to deal with the systems containing both mono-functional and multi-functional enzymes. As demonstration, the jackknife cross-validation was performed with MF-EFP on a benchmark dataset of enzymes classified into the following 7 functional classes: (1) EC 1 Oxidoreductase, (2) EC 2 Transferase, (3) EC 3 Hydrolase, (4) EC 4 Lyase, (5) EC 5 Isomerase, (6) EC 6 Ligase, (7) EC7 Translocases, where none of enzymes included has ≥90% pairwise sequence identity to any other in a same subset. The subset accuracy and average precision thus obtained by MF-EFP was 85.62% and 94.16% respectively. Extensive experiments also show that MF-EFP can outperform the existing predictors that also have the capacity to deal with such a complicated and stringent system. As a user-friendly web-server, MF-EFP is freely accessible to the public at the web-site http:// www.jci-bioinfo.cn/MF-EFP.

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Section: Resultsmentioning

confidence: 99%

MF-EFP: Predicting Multi-Functional Enzymes Function Using Improved Hybrid Multi-Label Classifier

et al. 2020

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“…According to the 5-step rule 75 widely used in performing various genome or proteome analyses 40 , 41 , 42 , 43 , 44 , 76 , 77 , 78 , 79 , 80 , 81 , 82 , 83 , 84 , 85 , 86 , 87 , 88 , 89 , 90 , the first important thing is to construct or select an effective benchmark dataset.…”

Section: Methodsmentioning

confidence: 99%

Implications of Newly Identified Brain eQTL Genes and Their Interactors in Schizophrenia

Cai

Huang

et al. 2018

Molecular Therapy - Nucleic Acids

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Schizophrenia (SCZ) is a devastating genetic mental disorder. Identification of the SCZ risk genes in brains is helpful to understand this disease. Thus, we first used the minimum Redundancy-Maximum Relevance (mRMR) approach to integrate the genome-wide sequence analysis results on SCZ and the expression quantitative trait locus (eQTL) data from ten brain tissues to identify the genes related to SCZ. Second, we adopted the variance inflation factor regression algorithm to identify their interacting genes in brains. Third, using multiple analysis methods, we explored and validated their roles. By means of the aforementioned procedures, we have found that (1) the cerebellum may play a crucial role in the pathogenesis of SCZ and (2) ITIH4 may be utilized as a clinical biomarker for the diagnosis of SCZ. These interesting findings may stimulate novel strategy for developing new drugs against SCZ. It has not escaped our notice that the approach reported here is of use for studying many other genome diseases as well.

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“…For constructing a robust and reliable predictor, it is a crucial step to transform the input sequence into a set of numerical attributes that could really reflect the intrinsic correlation with the desired target [47]. To avoid the bias of using single descriptor, integrating complementary information from different types of protein feature representations has become a new trend of feature design [48], [49].…”

Section: B Feature Extractionmentioning

confidence: 99%

“…To avoid losing sequence order information hidden in protein sequences, the pseudo amino acid composition (PseAAC) proposed by KC Chou [36] is introduced to comprehensively incorporate the occurrences and physicochemical properties of amino acids. Ever since then, the concept of PseAAC has been penetrated into various areas of computational proteomics [47], [50], [51].…”

Section: ) Pseudo Amino Acid Compositionmentioning

confidence: 99%

PCSPred_SC: Prediction of Protein Citrullination Sites Using an Effective Sequence-Based Combined Method

et al. 2020

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As one of post-translational modifications (PTMs), protein citrullination is crucial in a diverse array of cellular processes and implicated in a slew of human pathology. Therefore, accurate identification of protein citrullination sites (PCSs) is urgently needed to illuminate the reaction details and the complex pathogenesis related to the protein citrullination. In view of the limitations of the existing PCS predictors, this study proposes a novel and powerful sequence-based combined method named PCSPred_SC to further enhance the prediction performance. Various feature extraction methods are developed to mine sequence-derived biological information. Under the feature space, the predictive capabilities of different prediction algorithms, over-sampling methods, and feature selection methods are respectively explored. Experimental results indicate that the over-sampling methods are effective to solve the imbalanced dataset problem and the feature selection methods are significant in removing irrelevant and redundant features. On the same dataset using 10-fold cross validation, PCSPred_SC constructed by the combination of support vector machine (SVM), Adasyn, and t-distributed stochastic neighbor embedding (t-SNE) achieves much more outstanding performance than the competing methods, while reducing the number of features used for this task remarkably. It is anticipated that the proposed method will provide significant information to broaden our knowledge of citrullination-related biological processes.

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iMem-2LSAAC: A two-level model for discrimination of membrane proteins and their types by extending the notion of SAAC into chou's pseudo amino acid composition

Cited by 93 publications

References 112 publications

MF-EFP: Predicting Multi-Functional Enzymes Function Using Improved Hybrid Multi-Label Classifier

MF-EFP: Predicting Multi-Functional Enzymes Function Using Improved Hybrid Multi-Label Classifier

Implications of Newly Identified Brain eQTL Genes and Their Interactors in Schizophrenia

PCSPred_SC: Prediction of Protein Citrullination Sites Using an Effective Sequence-Based Combined Method

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