HIV-1 tropism prediction by the XGboost and HMM methods

Abstract

Human Immunodeficiency Virus 1 (HIV-1) co-receptor usage, called tropism, is associated with disease progression towards AIDS. Furthermore, the recently developed and developing drugs against co-receptors CCR5 or CXCR4 open a new thought for HIV-1 therapy. Thus, knowledge about tropism is critical for illness diagnosis and regimen prescription. To improve tropism prediction accuracy, we developed two novel methods, the extreme gradient boosting based XGBpred and the hidden Markov model based HMMpred. Both XGBpred and HMMpred achieved higher specificities (72.56% and 72.09%) than the state-of-the-art methods Geno2pheno (61.6%) and G2p_str (68.60%) in a 10-fold cross validation test at the same sensitivity of 93.73%. Moreover, XGBpred had more outstanding performances (with AUCs 0.9483, 0.9464) than HMMpred (0.8829, 0.8774) on the Hivcopred and Newdb (created in this work) datasets containing larger proportions of hard-to-predict dual tropic samples in the X4-using tropic samples. Therefore, we recommend the use of our novel method XGBpred to predict tropism. The two methods and datasets are available via http://spg.med.tsinghua.edu.cn:23334/XGBpred/. In addition, our models identified that positions 5, 11, 13, 18, 22, 24, and 25 were correlated with HIV-1 tropism.

Quantitative profiling of N6-methyladenosine at single-base resolution in stem-differentiating xylem of Populus trichocarpa using Nanopore direct RNA sequencing

“…TP, FP, TN, and FN represented true positives, false positives, true negatives, and false negatives, respectively. The receiver operating characteristic (ROC) curve and the area under the curve (AUC) were used to measure a predictive power [33]. In this study, the input dataset was raw signals from Nanopore direct RNA sequencing.…”

The value of CT radiomics in differentiating mycoplasma pneumoniae pneumonia from streptococcus pneumoniae pneumonia with similar consolidation in children

“…Further more, Wang et al [27] conducted a similar classification task their CNN-AvgFea-Norm3-based RF method achieved an AUC of 0.856 and an accuracy of 0.820, which was 0.069 loss in AUC and 0.070 loss in accuracy compared with our classifier. This may be related that our ROI includes more information XGBoost radiomics classifer is used widely by scientists for solving realworld scale problems with limited resources [28][29][30][31]. The building model and following validition results proved that XGBoost radiomics classifer showed a good value in differentiation of mycoplasma from pneumococcal pneumonia in children, consistent with their different pathological basis.…”

DENA: training an authentic neural network model using Nanopore sequencing data of Arabidopsis transcripts for detection and quantification of N6-methyladenosine on RNA

“…We used the following parameters for evaluating our model: where TP, FP, TN, FN, and F1 are true positives, false positives, true negatives, false negatives, and F1 score, respectively. We also measure a predictive power of DENA using the area under the curve (AUC) and the receiver operating characteristic curve (ROC) [ 45 ]. For DENA model, the input dataset was raw signals from Nanopore direct RNA sequencing.…”