Biology subjects

Pedrielli, G.

Publications and source records attributed to Pedrielli, G..

3 recordsLinked to original sources

bioRxiv2026

Mitigating Family Effects in RNA Secondary-Structure Prediction with Latent-Space Continual Learning

Accurate RNA secondary-structure prediction remains difficult despite decades of thermodynamics-based algorithms and the advent of deep-learning architectures (convolutional networks, Transformers, diffusion models). In fact, the datasets that pair RNA sequences with secondary-structure labels are often low-quality, noisy, and family-imbalanced, which limits out-of-distribution generalization and exacerbates catastrophic forgetting when new data regimes are introduced. We propose a continual-learning approach based on Lifelong Bayesian Optimization (LBO), RNAFOLBO, that treats each class of RNAs obtained from latent-space clustering as a sequential task and jointly orchestrates training and hyperparameter selection of heterogeneous models (UFold, RNA-FM, RNADiffFold), while preserving prior knowledge. Concretely, we apply LBO to 15 clusters obtained by clustering RNAStrAlign in the latent space of RNAGenesis, a model specialized in contextual representation learning and latent-space structuring, achieving a mean F 1 per cluster of 0.931 (with a range of 0.177). These results surpass the strongest one-shot baseline and mitigate forgetting without full retraining. The gains persist as additional clusters are introduced. Overall, RNAFOLBO delivers higher and more stable performance and practical scalability for integrating new RNA clusters or families, enabling more robust and transferable RNA secondary-structure prediction.

synthetic biology↗

bioRxiv2024

Predicting RNA Sequence-Structure Likelihood via Structure-Aware Deep Learning

MotivationThe active functionalities of RNA are recognized to heavily dependent on the structure and sequence. Therefore, A model that can accurately evaluate a design by giving RNA sequence-structure pairs would be a valuable tool for many researchers. Machine learning methods have been explored to develop such tools, showing promising results. However, two key issues remain. Firstly, the performance of machine learning models is affected by the features used to characterize RNA. Currently, there is no consensus on which features are the most effective for characterizing RNA sequence-structure pairs. Secondly, most existing machine learning methods extract features describing entire RNA molecule. We argue that it is essential to define additional features that characterize nucleotides and specific sections of RNA structure to enhance the overall efficacy of the RNA design process. ResultsWe develop two deep learning models for evaluating RNA sequence-structure pairs. The first model, NU-ResNet, uses a convolutional neural network architecture that solves the aforementioned problems by explicitly encoding RNA sequence-structure information into a 3D matrix. Building upon NU-ResNet, our second deep learning model, NUMO-ResNet, incorporates additional information derived from the characterizations of RNA, specifically the 2D folding motifs. In this work, we introduce an automated method to extract these motifs based on fundamental secondary structure descriptions. To assess the robustness of our models, we conduct 10-fold cross validation. Furthermore, we evaluate the performance of both models on two independent testing datasets. Our proposed models demonstrate excellent performance across both datasets and surpass the performance of the ENTRNA approach. Availability and ImplementationThe corresponding source code and data for this research is available at https://github.com/yzhou617/NU-ResNet_and_NUMO-ResNet. ContactGiulia.Pedrielli@asu.edu Supplementary informationSupplementary data are available at Bioinformatics online.

bioinformatics↗

bioRxiv2021

ExpertRNA: A new framework for RNA structure prediction

Ribonucleic acid (RNA) is a fundamental biological molecule that is essential to all living organisms, performing a versatile array of cellular tasks. The function of many RNA molecules is strongly related to the structure it adopts. As a result, great effort is being dedicated to the design of efficient algorithms that solve the "folding problem": given a sequence of nucleotides, return a probable list of base pairs, referred to as the secondary structure prediction. Early algorithms have largely relied on finding the structure with minimum free energy. However, the predictions rely on effective simplified free energy models that may not correctly identify the correct structure as the one with the lowest free energy. In light of this, new, data-driven approaches that not only consider free energy, but also use machine learning techniques to learn motifs have also been investigated, and have recently been shown to outperform free energy based algorithms on several experimental data sets. In this work, we introduce the new ExpertRNA algorithm that provides a modular framework which can easily incorporate an arbitrary number of rewards (free energy or non-parametric/data driven) and secondary structure prediction algorithms. We argue that this capability of ExpertRNA has the potential to balance out different strengths and weaknesses of state-of-the-art folding tools. We test the ExpertRNA on several RNA sequence-structure data sets, and we compare the performance of ExpertRNA against a state-of-the-art folding algorithm. We find that ExpertRNA produces, on average, more accurate predictions than the structure prediction algorithm used, thus validating the promise of the approach.

bioinformatics↗