Author response:

Public Reviews:

Reviewer #1 (Public review):

The author presents a new method for microRNA target prediction based on (1) a publicly available pretrained Sentence-BERT language model that the author fine-tunes using MeSH information and (2) downstream classification analysis for microRNA target prediction. In particular, the author's approach, named "miRTarDS", attempts to solve the microRNA target prediction problem by utilizing disease information (i.e., semantic similarity scores) from their language model. The author then compares the prediction performance with other sequence- and disease-based methods and attempts to show that miRTarDS is superior or at least comparable to existing methods. The author's general approach to this microRNA target prediction problem seems promising, but fails to demonstrate concrete computational evidence that miRTarDS outperforms other existing methods. The author's claim that disease information-based language models are sufficient is unfounded. The manuscript requires substantial rewriting and reorganization for readers with a strong background in biomedical research.

We appreciate the reviewer’s careful examination of modeling, benchmarking, and interpretation, and we are particularly encouraged that they found the proposed method promising. We will make corresponding revisions to the manuscript based on the reviewer’s comments.

A major issue related to the author's claim of computational advance of miRTarDS: The author does not introduce existing biomedical-specific language models, and does not compare them against miRTarDS's fine-tuned model. The performance of miRTarDS is largely dependent on the semantic embedding of disease terms. The author shows in Figure 5 that MeSH-based fine-tuning leads to a substantial improvement in MeSH-based correlation compared to the publicly available pretrained SBERT model "multi-qa-MiniLM-L6-cos-v1" without sacrificing a large amount of BIOSSES-based correlation. However, the author does not compare the performance of MeSH- and BIOSSES-based correlation with existing language models such as ChatGPT, BioBERT, PubMedBERT, and more. Also, the substantial improvement in MeSH-based correlation is a mere indication that the MeSH-based fine-tuning strategy was reasonable and not that it's superior to the publicly available pretrained SBERT model "multi-qa-MiniLM-L6-cos-v1".

We thank the reviewer for the constructive suggestions regarding the benchmarking of language models. We acknowledge that the performance of miRTarDS largely depends on the semantic embeddings of disease terms. So, in the revisions, I will: 1) conduct a literature review to introduce existing biomedical-specific language models, and 2) perform a horizontal comparison between our fine-tuned model and these existing models, to more comprehensively evaluate the model’s capabilities.

Another major issue is in the author's claim that disease-information from miRTarDS's language model is "sufficient" for accurate microRNA target prediction. Available microRNA targets with experimental evidence are largely biased for those with disease implications that have been reported in the biomedical literature. It's possible that their language model is biased by existing literature that has also been used to build microRNA target databases. Therefore, it is important that the author provides strong evidence that excludes the possibility of data leakage circularity. Similar concerns are prevalent across the manuscript, and so I highly recommend that the author reassess the evaluation frameworks and account for inflated performance, biased conclusions, and self-confirming results.

We thank the reviewer for the comment. We recognize that existing experimentally validated microRNA targets may be biased toward those reported in biomedical literature as disease‑related. To mitigate this bias, we attempted to extract predicted microRNA targets that share a very similar number of miRNA- and gene‑ disease entries as the experimentally validated microRNA targets using the K‑Nearest Neighbors (KNN) method. Then applied Positive‑Unlabeled (PU) Learning to classify the two groups. PU‑Learning is designed to address scenarios where only a subset of the training data is explicitly labeled as positive, while the remaining data are unlabeled—with the unlabeled set containing both potential positives and true negatives—which is highly suitable for the application context of this manuscript [1]. Preliminary results show that after applying the new data extraction and classification approach, model performance drops to around F1=0.73 (the MISIM method also shows a decline, with F1 around 0.58; detailed code is available on GitHub). The specific reasons for this require further investigation.

Last but not least, the manuscript requires a deeper and careful description and computational encoding of microRNA biology. I'd advise the author to include an expert in microRNA biology to improve the quality of this manuscript. For example, the author uses the pre-miRNA notation and replaces the mature miRNA notation to maintain computational encoding consistency across databases. However, the mature microRNA notation "the '-3p' or '-5p' is critical as the 3p and 5p mature microRNAs have different seed sequences and thus different mRNA targets. The 3p mature microRNA would most likely not target an mRNA targeted by the 5p mature microRNA.

We thank the reviewer for the critique and suggestion. We fully agree with the reviewer that the distinction between the 3p and 5p mature strands is critical for determining mRNA targeting, as they possess distinct seed sequences. In our study, we relied on the miRNA–disease associations provided by the HMDD database, which annotates interactions at the pre-miRNA level: “… the enriched functions of each mature miRNA are aggregated to the corresponding miRNA precursor.” [2] Furthermore, existing literature suggests that the pre-miRNA level can be appropriate and informative for disease association analyses: “Compared with the mature miRNA method, the pre-miRNA method is more useful for studying disease association.” [3] We also find that, in some cases, both strands cooperate to regulate the same or complementary pathways [4]. We acknowledge the reviewer’s point as an important consideration for future revision. We plan to consult or collaborate with biologists to enhance the quality of the manuscript in biology.

Reviewer #2 (Public review):

This study introduces a novel knowledge-driven approach, miRTarDS, which enables microRNA-Target Interaction (MTI) prediction by leveraging the disease association degree between a miRNA and its target gene. The core hypothesis is that this single feature is sufficient to distinguish experimentally validated functional MTIs from computationally predicted MTIs in a binary classification setting. To quantify the disease association, the authors fine-tuned a Sentence-BERT (SBERT) model to generate embeddings of disease descriptions and compute their semantic similarity. Using only this disease association feature, miRTarDS achieved an F1 score of 0.88 on the test set.

We thank the reviewers for their positive feedback, especially for their recognition of the novelty of this manuscript.

Strengths:

The primary strength is the innovative use of the disease association degree as an independent feature for MTI classification. In addition, this study successfully adapts and fine-tunes the Sentence-BERT (SBERT) model to quantify the semantic similarity between biomedical texts (disease descriptions). This approach establishes a critical pathway for integrating powerful language models and the vast growth in clinical/disease data into biochemical discovery, like MTI prediction.

We would like to thank the reviewer again for their positive feedback. We appreciate their recognition of the novelty of our work, as well as their acknowledgment that the proposed method paves the way for integrating language models with clinical/disease data into biochemical discovery.

Weaknesses:

The main weakness lies in its definition of the ground-truth dataset, which serves as a foundation for methodological evaluation. The study defines the Negative Set as computationally predicted MTIs that lack experimental evidence. However, the absence of experimental validation does not equate to non-functionality. Similarly, the miRAW sets are classified by whether the target and miRNA could form a stable duplex structure according to RNA structure prediction. This definition is biologically irrelevant, as duplex stability does not fully encapsulate the complex in vivo binding of miRNAs within the AGO protein complex.

We thank the reviewers for their constructive feedback. We have realized that treating predicted MTI as a negative class may pose some issues. Therefore, we have decided to adopt Positive Unlabeled (PU) Learning in subsequent updates. This classification method can be applied to datasets such as ours, which contain only positive classes and lack negative ones [1]. We used the miRAW dataset to enable a horizontal comparison of our method with traditional sequence-based prediction approaches. We acknowledge that miRAW may overlook some biological insights, and we plan to optimize the construction of test datasets in the future. Some preliminary explorations have already been conducted, and the relevant code is available on GitHub.

Furthermore, we will make the following revisions: 1) We will clearly specify the version of miRBase and incorporate more miRNA-related databases. 2) Conduct a further literature review on miRNA biological mechanisms to enhance the quality of the manuscript in biology. 3) Perform a more comprehensive evaluation of the model’s performance. 4) Attempt to identify some representative MTIs that have been overlooked by existing prediction tools but can be predicted by our proposed method.

References

(1) Li, F., Dong, S., Leier, A., Han, M., Guo, X., Xu, J., ... & Song, J. (2022). Positive-unlabeled learning in bioinformatics and computational biology: a brief review. Briefings in Bioinformatics, 23(1), bbab461.

(2) Huang, Z., Shi, J., Gao, Y., Cui, C., Zhang, S., Li, J., ... & Cui, Q. (2019). HMDD v3. 0: a database for experimentally supported human microRNA–disease associations. Nucleic acids research, 47(D1), D1013-D1017.

(3) Wang, H., & Ho, C. (2023). The human pre-miRNA distance distribution for exploring disease association. International Journal of Molecular Sciences, 24(2), 1009.

(4) Mitra, R., Adams, C. M., Jiang, W., Greenawalt, E., & Eischen, C. M. (2020). Pan-cancer analysis reveals cooperativity of both strands of microRNA that regulate tumorigenesis and patient survival. Nature Communications, 11(1), 968.

Figures and data

This figure illustrates the workflow of our research.

Example of BIOSSES dataset.

Disease descriptor ‘Cardiovascular Infections’ and ‘Out-of-Hospital Cardiac Arrest’ hierarchical structure in MeSH tree.

Summary of MTIs and Source Databases Employed in the Study

Example of MeSHDS dataset.

Model performance benchmark.

F1 Scores for Different SBERT Models and Machine Learning Classifiers

Performance Comparison of miRNA Target Prediction Methods

Performance Comparison of Using Different Feature

This figure presents the average frequency distribution of similarity values between miRNA-associated diseases and gene-associated diseases for both experimentally validated MTIs and predicted MTIs.