iKcr-PseEns: Identify lysine crotonylation sites in histone proteins with pseudo components and ensemble classifier

Qiu, Wang-Ren; Sun, Bo; Xiao, Xuan; Xu, Zhaochun; Jia, Jianhua; Chou, Kuo‐Chen

doi:10.1016/j.ygeno.2017.10.008

Cited by 128 publications

(48 citation statements)

References 132 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Several autoimmune diseases, including BD, are associated with certain TLR gene polymorphisms [83,84]. A significant association has been found between polymorphism of TLR2 and ocular BD patients [85]. The expression of TLR4 was significantly up-regulated in monocyte-derived macrophages from VKH patients [86].…”

Section: Immune System Regulation Genesmentioning

confidence: 96%

“…Nanni [23] 83.00 86.00 85.10 N/A Nanni [32] 84.00 86.00 84.00 N/A Nanni and Lumini [25] 86.60 86.70 85.00 N/A Z-H You [29] 87 …”

Section: Model Acc (%) Sen (%) Pre (%) MCC (%)mentioning

confidence: 99%

“…Structures were energy minimized by using the Gasteiger-Hückel charge [83], Tripos force field [84], and Powell methods [85] with a convergence criterion of 0.005 kcal/(mol Å). The iterations maximum number was set to 10,000, and multiple conformation search was used.…”

Section: Dataset and Molecular Modelingmentioning

confidence: 99%

“…In the case of Sv-IGFBP, an additional cis-peptide bond was defined in the C' immunoglobulin domain. In each case, the optimal model was selected via its lowest energy and associated DOPE score [62] 85). For the ILPs the C-peptide was kept intact for modelling (as it is likely to be involved with orientation and folding) and later removed.…”

Section: Protein Structure Modellingmentioning

confidence: 99%

See 3 more Smart Citations

Special Protein Molecules Computational Identification

2018

View full text Add to dashboard Cite

Section: Immune System Regulation Genesmentioning

confidence: 96%

“…Nanni [23] 83.00 86.00 85.10 N/A Nanni [32] 84.00 86.00 84.00 N/A Nanni and Lumini [25] 86.60 86.70 85.00 N/A Z-H You [29] 87 …”

Section: Model Acc (%) Sen (%) Pre (%) MCC (%)mentioning

confidence: 99%

Section: Dataset and Molecular Modelingmentioning

confidence: 99%

Section: Protein Structure Modellingmentioning

confidence: 99%

See 2 more Smart Citations

Special Protein Molecules Computational Identification

2018

View full text Add to dashboard Cite

“…As demonstrated in a series of recent publications [106][107][108][109][110]120], a user-friendly and publicly accessible web-server represents the future direction for developing practically more useful prediction methods and computational tools. A variety of web-servers have increasing impacts on medical science [121], driving medicinal chemistry into an unprecedented revolution [122], the efforts shall be made in future to provide a web-server for the prediction and performance assessment presented in this study.…”

Section: Moreover 15 Protein Families Only Existed In Plants Microbmentioning

confidence: 99%

Assessing the Performances of Protein Function Prediction Algorithms from the Perspectives of Identification Accuracy and False Discovery Rate

Yu¹,

Li²,

Yang³

et al. 2017

Preprint

View full text Add to dashboard Cite

The knowledge of protein function is essential for the study of biological processes, the understanding of disease mechanism and the exploration of novel therapeutic target. Apart from experimental methods, a number of in-silico approaches have been developed and extensively used for protein function prediction.Among these approaches, BLAST predicts functions based on protein sequence similarity, and machine learning predicts functional families from protein sequences irrespective of their similarity, which complements BLAST and other methods in predicting diverse classes of proteins including distantly related proteins and homologous proteins of different functions. However, their identification accuracies and the false discovery rate have not yet been assessed so far, which greatly limits the usage of these prediction algorithms. Herein, a comprehensive comparison of the performances among four popular functional prediction algorithms (BLAST, SVM, PNN and KNN) was conducted. In particular, the performance of these algorithms were systematically assessed by four metrics (sensitivity, specificity, accuracy and Matthews correlation coefficient) based on the independent test datasets generated from 93 protein families defined by UniProtKB Keywords. Moreover, the false discovery rates of these algorithms were evaluated by scanning the genomes of four representative model species (homo sapiens, arabidopsis thaliana, saccharomyces cerevisiae and mycobacterium tuberculosis). As a result, the substantially higher sensitivity and stability of BLAST and SVM were observed compared with that of PNN and KNN. But the machine learning algorithms (PNN, KNN and SVM) were found capable of significantly reducing the false discovery rate (SVM < PNN ≈ KNN). In summary, this study comprehensively assessed the performance of four popular algorithms applied to protein function prediction, which could facilitate the selection of the most appropriate method in the related biomedical research. KEYWORDSfalse discovery rate; machine learning; protein function prediction; support vector machine; BLAST Preprints (www.preprints.org) | NOT PEER-REVIEWED | Posted:

show abstract

DeepIon: Deep learning approach for classifying ion transporters and ion channels from membrane proteins

Taju

2019

J Comput Chem

View full text Add to dashboard Cite

The movement of ions across the cell membrane is an essential for many biological processes. This study is focused on ion channels and ion transporters (pumps) as types of border guards control the incessant traffic of ions across cell membranes. Ion channels and ion transporters function to regulate membrane potential and electrical signaling and play important roles in cell proliferation, migration, apoptosis, and differentiation. In their behaviors, it is found that ion channels differ significantly from ion transporters. Therefore, a method for automatically classifying ion transporters and ion channels from membrane proteins is proposed by training deep neural networks and using the position‐specific scoring matrix profile as an input. The key of novelty is the three‐stage approach, in which five techniques for data normalization are used; next three imbalanced data techniques are applied to the minority classes and then, six classifiers are compared with the proposed method. © 2019 Wiley Periodicals, Inc.

show abstract

iKcr-PseEns: Identify lysine crotonylation sites in histone proteins with pseudo components and ensemble classifier

Cited by 128 publications

References 132 publications

Special Protein Molecules Computational Identification

Special Protein Molecules Computational Identification

Assessing the Performances of Protein Function Prediction Algorithms from the Perspectives of Identification Accuracy and False Discovery Rate

DeepIon: Deep learning approach for classifying ion transporters and ion channels from membrane proteins

Contact Info

Product

Resources

About