Deep Recurrent Neural Network-Based Identification of Precursor microRNAs
MicroRNAs (miRNAs) are small non-coding ribonucleic acids (RNAs) which play key roles in post-transcriptional gene regulation. Direct identification of mature miRNAs is infeasible due to their short lengths, and researchers instead aim at identifying precursor miRNAs (pre-miRNAs). Many of the known pre-miRNAs have distinctive stem-loop secondary structure, and structure-based filtering is usually the first step to predict the possibility of a given sequence being a pre-miRNA. To identify new pre-miRNAs that often have non-canonical structure, however, we need to consider additional features other than structure. To obtain such additional characteristics, existing computational methods rely on manual feature extraction, which inevitably limits the efficiency, robustness, and generalization of computational identification. To address the limitations of existing approaches, we propose a pre-miRNA identification method that incorporates (1) a deep recurrent neural network (RNN) for automated feature learning and classification, (2) multimodal architecture for seamless integration of prior knowledge (secondary structure), (3) an attention mechanism for improving long-term dependence modeling, and (4) an RNN-based class activation mapping for highlighting the learned representations that can contrast pre-miRNAs and non-pre-miRNAs. In our experiments with recent benchmarks, the proposed approach outperformed the compared state-of-the-art alternatives in terms of various performance metrics.
The paper presents an LSTM model with an attention mechanism for classifying whether an RNA molecule is a pre-microRNA from its sequence and secondary structure. Class weights are incorporated into log-loss to account for class imbalance in the datasets used. The proposed method is extensively evaluated against 5 other existing methods on 3 datasets, and is shown to outperform the existing methods in most cases. The paper then attempts to give some insight into the features that are important for achieving good performance. First, by showing that secondary structures are largely responsible, but sequence features give a small boost, and second, by interpreting the attention weights using an adapted version of class activation mapping (proposed in an earlier CVPR paper). Results using different architectures and hyperparameters are also shown.
The paper presents an interesting application of LSTMs to biological data, and has some novel elements for model interpretation. The main issue I have with this paper is in how the method is evaluated. If the goal is to find new pre-microRNAs, especially those that have noncanonical structures (as stated in the abstract) special care must be taken in the construction of the training and test sets to make sure the reported performance will be reflective of performance on this discovery task. This is why in ref [10] describing miRBoost, methods are evaluated based on ability to predict completely new pre-microRNAs that were not previously found in previous versions of miRBase (included as 'test' dataset in this paper). While holding out 10% of the dataset as a test set may seem to also be sufficient, it results in a far smaller evaluation set of positives (1/5 - 1/10) than using the 'test' dataset, as well as possibly allowing the models to learn structural features of newly discovered RNAs, some of which may have been randomly placed in the training set.
An even stricter evaluation would use "structurally nonhomologous training and test data sets", as proposed in Rivas et al. (PMID:22194308), that discusses the importance of doing so for properly evaluating methods on the related RNA secondary structure prediction task. If a model is able to perform well on this stricter evaluation, where test RNA sequences are structurally different from training sequences, one can then be more confident in the ability to discover structurally novel pre-microRNAs.
A related comment on the evaluation is that the AUROC metric is unsuitable for tasks where only performance on the positive class is important (as in this case) and the area under the precision-recall curve (AUPR), or even better, the area under the precision-recall-gain curve (AUPRG; Flach and Kull NIPS 2015) should be used.
The adaptation of the class activation mapping method to the LSTM model is interesting and the paper hints that it could indeed be useful in uncovering new biological insight. It would strengthen the section if features common to the negatively predicted examples could be discussed as well.
The attention mechanism seemed to make a big difference to the performance - it would be interesting if it was possible to dissect how/why this is occurring in the model (and on this task). Does the attention mechanism make a bigger difference for longer sequences? And is it possible to show some of the long range dependencies that the attention mechanism picks up?
Other questions for the authors:
- In section 4.3 were the chosen examples actually correctly classified as positives and negatives respectively?
- Line 175 - how were the dropout rates selected empirically? Using cross validation?
- In the discussion it is mentioned that additional data modes can be added as separate network branches. Why was this approach not taken for the structure data and instead an explicit encoding that simultaneously considers structure and sequence was used?
Typos:
Line 91: "long-term dependency" -> "long-term dependencies"
Table 1: "Positive exmaples" -> "Positive examples"
Table 2 caption: "under curve of receiver operation characteristic" -> "area under receiver operating characteristic curve"
Table 3: microPred (test), cross-species, 0.985 should be highlighted instead
Figure 4 caption: "RNN ouputs" -> "RNN outputs"
Line 237: "technqiue" -> "technique"
Reference 10 should be Tran et al. not Tempel et al.