iTTCA-RF: a random forest predictor for tumor T cell antigens.
Reads0
Chats0
TLDR
Li et al. as mentioned in this paper used four types feature encoding methods to build an efficient predictor, including amino acid composition, global protein sequence descriptors and grouped amino acid and peptide composition, and employed a two-step feature selection technique to search for the optimal feature subset.Abstract:
Cancer is one of the most serious diseases threatening human health. Cancer immunotherapy represents the most promising treatment strategy due to its high efficacy and selectivity and lower side effects compared with traditional treatment. The identification of tumor T cell antigens is one of the most important tasks for antitumor vaccines development and molecular function investigation. Although several machine learning predictors have been developed to identify tumor T cell antigen, more accurate tumor T cell antigen identification by existing methodology is still challenging. In this study, we used a non-redundant dataset of 592 tumor T cell antigens (positive samples) and 393 tumor T cell antigens (negative samples). Four types feature encoding methods have been studied to build an efficient predictor, including amino acid composition, global protein sequence descriptors and grouped amino acid and peptide composition. To improve the feature representation ability of the hybrid features, we further employed a two-step feature selection technique to search for the optimal feature subset. The final prediction model was constructed using random forest algorithm. Finally, the top 263 informative features were selected to train the random forest classifier for detecting tumor T cell antigen peptides. iTTCA-RF provides satisfactory performance, with balanced accuracy, specificity and sensitivity values of 83.71%, 78.73% and 88.69% over tenfold cross-validation as well as 73.14%, 62.67% and 83.61% over independent tests, respectively. The online prediction server was freely accessible at http://lab.malab.cn/~acy/iTTCA
. We have proven that the proposed predictor iTTCA-RF is superior to the other latest models, and will hopefully become an effective and useful tool for identifying tumor T cell antigens presented in the context of major histocompatibility complex class I.read more
Citations
More filters
Journal ArticleDOI
CRBPDL: Identification of circRNA-RBP interaction sites using an ensemble neural network approach
Mengting Niu,Quan Zou,Chen Lin +2 more
TL;DR: A novel calculation model, CRBPDL, which uses an Adaboost integrated deep hierarchical network to identify the binding sites of circular RNA-RBP and is capable of performing universal, reliable, and robust.
Journal ArticleDOI
Protein–DNA/RNA interactions: Machine intelligence tools and approaches in the era of artificial intelligence and big data
TL;DR: An overview of the development progress of computational methods for protein–DNA/RNA interactions using machine intelligence techniques is provided and the advantages and shortcomings of these methods are summarized.
Journal ArticleDOI
Risk prediction of diabetes and pre-diabetes based on physical examination data.
Yumei Han,Hui Yang,Qin-Lai Huang,Zi-Jie Sun,Ming Liang Li,Jingbo Zhang,Ke-Jun Deng,Shuo Chen,Hao Lin +8 more
TL;DR: This work collected the physical examination data from Beijing Physical Examination Center from January 2006 to December 2017, and divided the population into three groups according to the WHO (1999) Diabetes Diagnostic Standards.
Journal ArticleDOI
DeepMC-iNABP: Deep learning for multiclass identification and classification of nucleic acid-binding proteins
TL;DR: In this article , a computational predictor, called DeepMC-iNABP, was proposed to solve the problem of ignoring DNA- and RNA-binding proteins (DRBPs), and the cross-predicting problem referring to DBP predictors predicting DBPs as RBPs, and vice versa.
Journal ArticleDOI
Prediction of Hormone-Binding Proteins Based on K-mer Feature Representation and Naive Bayes
TL;DR: Wang et al. as mentioned in this paper used k-mer (K=3) feature representation method to extract features, and feature selection algorithm was used to reduce the dimensionality of the extracted features and select the appropriate optimal feature set.
References
More filters
Journal ArticleDOI
Citrullination Site Prediction by Incorporating Sequence Coupled Effects into PseAAC and Resolving Data Imbalance Issue
TL;DR: The results suggest that the predCitru-Site method is promising and can be used as a complementary technique for fast exploration of citrullination in arginine residue and shows significant improvement in the case of independent tests in all performance metrics.
Journal ArticleDOI
Computational systems biology in the big data era.
TL;DR: A report of the 6th IEEE International Conference on Systems Biology (IEEE ISB2012), 18-20 August, Xi'an, China.
Journal ArticleDOI
Exploring Drug Treatment Patterns Based on the Action of Drug and Multilayer Network Model
TL;DR: A network-based model was proposed to study the treatment patterns of drugs and was based on drug effects and a multilayer network model to overcome the shortcomings of single-layer networks and combined the network with information on drug activity.
Journal ArticleDOI
Predicting LncRNA Subcellular Localization Using Unbalanced Pseudo-k Nucleotide Compositions
TL;DR: It is demonstrated that the proposed predictor is feasible and powerful for the prediction of lncRNA subcellular localization, and clearly outperforms the existing state-of- the-art method.
Journal ArticleDOI
Screening of Prospective Plant Compounds as H1R and CL1R Inhibitors and Its Antiallergic Efficacy through Molecular Docking Approach
TL;DR: In this paper, a library of 8,500 phytochemicals was generated using MOE software and the structures of histamine-1 receptor and cysteinyl leukotriene receptor-1 were predicted by the homology modeling method through SWISS model.