Edit distance, also known as Levenshtein distance, is an essential way to compare two strings that proved to be particularly useful in the analysis of genetic sequences and natural language processing. However, edit distance is a discrete function that is known to be hard to optimize. This fact hampers the use of this metric in Machine Learning. Even as simple algorithm as K-means fails to cluster a set of sequences using edit distance if they are of variable length and abundance. In this paper we propose a novel metric - soft edit distance (SED), which is a smooth approximation of edit distance. It is differentiable and therefore it is possible to optimize it with gradient methods. Similar to original edit distance, SED as well as its derivatives can be calculated with recurrent formulas at polynomial time. We prove usefulness of the proposed metric on synthetic datasets and clustering of biological sequences.

Despite striking successes in identifying novel biomarkers for improved patient stratification and predicting disease progression, numerous challenges remain in the effective integration and exploitation of multiomic data in biomedical applications beyond cancer, for which most bioinformatics strategies are developed and validated. That focus on cancer severely limits the effective development and advancement of algorithms in machine learning and artificial intelligence that do not suffer degraded out-of-domain performance. Generalizability and interpretability of models, however, are also required for robust insights that may translate into clinical practice. Work across different independent datasets is critical for establishing models robust towards unwanted variation in assays, protocols, and cohort populations. Disease-specific context like ethnicity, socioeconomic background, sex, lifestyle, disease phase, and tissue type also strongly affect molecular profiles. We here discuss atherosclerotic cardiovascular disease (ASCVD) as a high-impact non-cancer use case for the challenges remaining in the development and application of the latest bioinformatics approaches to multiomics data integration. ASCVD remains the leading cause of death globally. Disease aetiology, progression, and therapy outcome depend on a complex interplay of genetic, environmental, and lifestyle factors. Integrating these diverse data types effectively remains a challenge but holds transformative potential for personalized medicine. Discovery and access to data of sufficient diversity and extent form key bottlenecks. We here compile a first comprehensive overview of key data sets in ASCVD to complement the established cancer-focused resources as a foundation for future effective development and application of state-of-the-art bioinformatics tools for multiomic data integration.

MOTIVATION: MicroRNAs (miRNAs) are crucial regulators of gene expression, but the precise mechanisms governing their binding to target sites remain unclear. A major contributing factor to this is the lack of unbiased experimental datasets for training accurate prediction models. While recent experimental advances have provided numerous miRNA-target interactions, these are solely positive interactions. Generating negative examples in silico is challenging and prone to introducing biases, such as the miRNA frequency class bias identified in this work. Biases within datasets can compromise model generalization, leading models to learn dataset-specific artifacts rather than true biological patterns. RESULTS: We introduce a novel methodology for negative sample generation that effectively mitigates the miRNA frequency class bias. Using this methodology, we curate several new, extensive datasets and benchmark several state-of-the-art methods on them. We find that a simple convolutional neural network model, retrained on some of these datasets, is able to outperform state-of-the-art methods reaching average precision scores between 0.81 and 0.86 in test datasets. This highlights the potential for leveraging unbiased datasets to achieve improved performance in miRNA binding site prediction. To facilitate further research and lower the barrier to entry for machine learning researchers, we provide an easily accessible Python package, miRBench, for dataset retrieval, sequence encoding, and the execution of state-of-the-art models. AVAILABILITY AND IMPLEMENTATION: The miRBench Python package is accessible at https://github.com/katarinagresova/miRBench/releases/tag/v1.0.1.

In this paper, we improve the results based on a Neural Network-based model that predicts an enzyme (Aldose Reductase) inhibitory activity of a group of compounds. The improvement is due to the judicial selection of ensembles of trained Neural Networks to contribute to the final model. The method is validated on a family of compounds that is different from the families which were used in the training of the model. The results confirm an accurate, chemical-family-independent method that can predict Aldose Reductase inhibitory activity with excellent accuracy.

Abstract Motivation: Experimental evidence has accumulated showing that microRNA (miRNA) binding sites within protein coding sequences (CDSs) are functional in controlling gene expression. Results: Here we report a computational analysis of such miRNA target sites, based on features extracted from existing mammalian high-throughput immunoprecipitation and sequencing data. The analysis is performed independently for the CDS and the 3′-untranslated regions (3′-UTRs) and reveals different sets of features and models for the two regions. The two models are combined into a novel computational model for miRNA target genes, DIANA-microT-CDS, which achieves higher sensitivity compared with other popular programs and the model that uses only the 3′-UTR target sites. Further analysis indicates that genes with shorter 3′-UTRs are preferentially targeted in the CDS, suggesting that evolutionary selection might favor additional sites on the CDS in cases where there is restricted space on the 3′-UTR. Availability: The results of DIANA-microT-CDS are available at www.microrna.gr/microT-CDS Contact: [email protected]; [email protected] Supplementary information: Supplementary data are available at Bioinformatics online.

UNLABELLED: MicroRNAs (miRNAs) are a class of short endogenously expressed RNA molecules that regulate gene expression by binding directly to the messenger RNA of protein coding genes. They have been found to confer a novel layer of genetic regulation in a wide range of biological processes. Computational miRNA target prediction remains one of the key means used to decipher the role of miRNAs in development and disease. Here we introduce the basic idea behind the experimental identification of miRNA targets and present some of the most widely used computational miRNA target identification programs. The review includes an assessment of the prediction quality of these programs and their combinations. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

SUMMARY: DIANA-mirPath is a web-based computational tool developed to identify molecular pathways potentially altered by the expression of single or multiple microRNAs. The software performs an enrichment analysis of multiple microRNA target genes comparing each set of microRNA targets to all known KEGG pathways. The combinatorial effect of co-expressed microRNAs in the modulation of a given pathway is taken into account by the simultaneous analysis of multiple microRNAs. The graphical output of the program provides an overview of the parts of the pathway modulated by microRNAs, facilitating the interpretation and presentation of the analysis results. AVAILABILITY: The software is available at http://microrna.gr/mirpath and is free for all users with no login or download requirement.

BACKGROUND: MicroRNAs are small endogenously expressed non-coding RNA molecules that regulate target gene expression through translation repression or messenger RNA degradation. MicroRNA regulation is performed through pairing of the microRNA to sites in the messenger RNA of protein coding genes. Since experimental identification of miRNA target genes poses difficulties, computational microRNA target prediction is one of the key means in deciphering the role of microRNAs in development and disease. RESULTS: DIANA-microT 3.0 is an algorithm for microRNA target prediction which is based on several parameters calculated individually for each microRNA and combines conserved and non-conserved microRNA recognition elements into a final prediction score, which correlates with protein production fold change. Specifically, for each predicted interaction the program reports a signal to noise ratio and a precision score which can be used as an indication of the false positive rate of the prediction. CONCLUSION: Recently, several computational target prediction programs were benchmarked based on a set of microRNA target genes identified by the pSILAC method. In this assessment DIANA-microT 3.0 was found to achieve the highest precision among the most widely used microRNA target prediction programs reaching approximately 66%. The DIANA-microT 3.0 prediction results are available online in a user friendly web server at http://www.microrna.gr/microT.