MRM-BERT: a novel deep neural network predictor of multiple RNA modifications by fusing BERT representation and sequence features

Figures & data

Figure 1. The deep learning framework of MRM-BERT.

Figure 2. Comparison of prediction performance of different sequence encodings combinations. the performance shown in this figure was evaluated on the validation set of the K-fold cross-validation & testing partitioning strategy. A. Boxplots of AUC and accuracy comparing XGBoost models combining different number of encodings for AtoI modification prediction. B. Boxplots of AUC and accuracy comparing XGBoost models combining different number of encodings for psi modification prediction. C. Boxplots of AUC and accuracy comparing XGBoost models combining different number of encodings for m [1]A modification prediction. D. Boxplots of AUC and accuracy comparing XGBoost models combining different number of encodings for m [6]A modification prediction. E. Boxplots of AUC and accuracy comparing XGBoost models combining different number of encodings for m [5]C modification prediction. F. The best-performing feature combinations and their worst AUC rankings (at worst within 15, or at worst within 30) across all modification types.

Table 1. The AUC of the MRM-BERT model versus models using individual deep neural network module.

Model	m^6A	m^5C	Am	Cm	Gm	Um	m^5U	m^1A	m^6Am	m^7G	AtoI	Psi
CNN	0.8705	0.9268	0.9166	0.9421	0.9181	0.9208	0.9496	0.9364	0.9154	0.7742	0.7422	0.7652
BERT	0.8993	0.8775	0.9388	0.9197	0.9418	0.9045	0.9518	0.9505	0.9264	0.6786	0.8409	0.8407
CNN+BERT	0.9004	0.9018	0.9404	0.9321	0.9450	0.9187	0.9549	0.9510	0.9267	0.7430	0.8426	0.8452

The performance shown in this table was evaluated on the validation set of the K-fold cross-validation & testing partitioning strategy.

Table 2. Performance metrics of MRM-BERT on the test set from the K-fold cross-validation & testing partitioning strategy.

Mertrics	m^6A	m^5C	Am	Cm	Gm	Um	m^5U	m^1A	m^6Am	m^7G	AtoI	Psi
Acc	0.8505 ±0.0100	0.9440 ±0.0331	0.9566 ±0.0294	0.9567 ±0.0129	0.9784 ±0.0102	0.9429 ±0.0260	0.9606 ±0.0175	0.9336 ±0.0099	0.8923 ±0.0266	0.8859 ±0.0579	0.9230 ±0.0297	0.7522 ±0.0331
Sn	0.8737 ±0.0104	0.9245 ±0.0416	0.9324 ±0.0497	0.9517 ±0.0145	0.9669 ±0.0155	0.9340 ±0.0272	0.9460 ±0.0236	0.9288 ±0.0072	0.8339 ±0.0526	0.8589 ±0.0607	0.8693 ±0.0608	0.6498 ±0.1207
Sp	0.8274 ±0.0197	0.9632 ±0.0253	0.9789 ±0.0121	0.9602 ±0.0166	0.9873 ±0.0101	0.9511 ±0.0316	0.9709 ±0.0139	0.9377 ±0.0124	0.9550 ±0.0139	0.9107 ±0.0684	0.9631 ±0.0142	0.8369 ±0.0940
MCC	0.7020 ±0.0195	0.8887 ±0.0657	0.9141 ±0.0575	0.9107 ±0.0264	0.9562 ±0.0207	0.8859 ±0.0520	0.9187 ±0.0362	0.8664 ±0.0199	0.7927 ±0.0475	0.7729 ±0.1167	0.8437 ±0.0597	0.5071 ±0.0604
AUC	0.9246 ±0.0066	0.9827 ±0.0122	0.9815 ±0.0152	0.9794 ±0.0072	0.9933 ±0.0076	0.9789 ±0.0119	0.9873 ±0.0054	0.9731 ±0.0050	0.9544 ±0.0054	0.9373 ±0.0398	0.9715 ±0.0163	0.8492 ±0.0238
F1	0.8535 ±0.0089	0.9422 ±0.0346	0.9532 ±0.0329	0.9473 ±0.0153	0.9750 ±0.0118	0.9404 ±0.0268	0.9520 ±0.0214	0.9282 ±0.0105	0.8884 ±0.0304	0.8786 ±0.0614	0.9053 ±0.0394	0.6992 ±0.0655

Table 3. Comparison of the AUC results on the original test set of MultiRM’s fixed validation and testing partitioning strategy.

AUC	m^6A	m^5C	Am	Cm	Gm	Um	m^5U	m^1A	m^6Am	m^7G	Atol	Psi
CNN+LSTM+Attention	0.7272	0.7732	0.6088	0.7872	0.9304	0.8004	0.8592	0.7548	0.8324	0.4968	0.5116	0.6452
HMM+LSTM+Attention	0.7472	0.8892	0.6984	0.8352	0.8942	0.8916	0.9404	0.8404	0.9276	0.5284	0.5908	0.7216
MultiRM	0.8558	0.9056	0.7892	0.8604	0.9256	0.8780	0.9480	0.7792	0.8912	0.6772	0.6698	0.8528
MRM-BERT	0.8912	0.9072	0.9248	0.9344	0.9164	0.9524	0.9776	0.9460	0.9520	0.6852	0.8744	0.8861

The performance shown in this table was evaluated on the original test set of MultiRM’s fixed validation & testing partitioning strategy. For MultiRM and its variants, the performance metrics are from its original literature.

Figure 3. A sample prediction result from the online MRM-BERT webserver.

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Data availability statement

The data that support the findings of this study are openly available in FigShare at doi: 10.6084/m9.figshare.24873195, reference number 24,873,195. The source code of this study is openly available in Github at https://github.com/abhhba999/MRM-BERT