Fig 1.
Illustration of the BSBL-TSK-FS model architecture.
(A) Input and encoding module. The RNA sequence with length of 41bp was encoded into a matrix via the PSNP with 5-mer. (B) Fuzzification module. The model uses fuzzy system to process the data, then gets fuzzy feature , and applies
to the next module. (C) Block sparse Bayesian module. The sparse solution of the model is obtained by block sparse Bayes algorithm, and the parameter p is solved. (D) Prediction module. Identify m6A and non-m6A by predicting results.
Table 1.
Results of the BSBL-TSK-FS model on m6A datasets under 5-CV.
Fig 2.
(A–C) Venn diagram of data for different species.
(D) Sankey diagram of prediction results for 11 datasets. Straight lines represent correctly classified samples, curved lines represent incorrectly classified samples, and the stronger the lines, the larger the number of samples.
Fig 3.
Motif logo analysis on Human datasets.
(A–C) Probability Logos of positive samples analysis. (D–E) Probability Logos of positive and negative samples comparative analysis. It’s worth noting that kpLogo uses the “T” to represent the “U” in the RNA sequence.
Fig 4.
Motif logos in central sequential regions of Human datasets.
(A) Motif logos of the positive samples. (B) Motif logos of the negative samples. It’s worth noting that kpLogo uses the “T” to represent the “U” in the RNA sequence.
Fig 5.
(A) ROC curves of TSK-FS, SBL-TSK-FS and BSBL-TSK-FS models on m6A datasets.
(B) Visualization of feature spatial distribution by 3 methods.
Table 2.
Results of multiple methods on the mouse datasets as analyzed by 5-CV.
Table 3.
Results of multiple methods on the human datasets as analyzed by 5-CV.
Table 4.
Results of multiple methods on the rat datasets as analyzed by 5-CV.
Fig 6.
(A) Comparison results between the proposed method and 5 advanced methods on SN and SP indicators.
(B) Piano plots of MCC comparisons of six methods across 11 datasets. (C) Radar maps of six methods for MCC comparison on 11 data sets.
Fig 7.
Comparison of six methods on MCC, SN, SP, ACC and AUC indicators.
The larger and brighter the bubbles, the higher the value.
Table 5.
Comparison with models on mouse datasets via 5-CV.
Table 6.
Comparison with models on human datasets via 5-CV.
Table 7.
Comparison with models on rat datasets via 5-CV.
Table 8.
Performance comparison between different approaches on independent datasets from human tissues.
Table 9.
Performance comparison on the m5C datasets.
Fig 8.
Heap map showing ACCs of cross-species and cross-tissues prediction accuracies.
Table 10.
Summary of tissue-specific m6A datasets.
Table 11.
Summary of m6A independent datasets from human tissues.
Table 12.
Summary of m5C datasets.