Biology subjects

Bonidia, R. P.

Publications and source records attributed to Bonidia, R. P..

3 recordsLinked to original sources

bioRxiv2025

BioPrediction-PPI: Simplifying the Prediction of Protein-Protein Interactions through Artificial Intelligence

Proteins are essential in biological processes, primarily through their interactions with other molecules, including proteins. These interactions are crucial for cellular functions and maintaining life. Predicting Protein-Protein Interactions (PPIs) is very important, although challenging, for understanding cellular functions and diseases. This paper presents BioPrediction-PPI, a new end-to-end Machine Learning (ML) framework for PPI prediction, automating the entire process from feature extraction to interpretability, with no manual intervention required. BioPrediction-PPI stands as one of the few end-to-end models that do not rely on deep learning, enabling the automated use of trained models on new data. It offers interpretability graphs for creating auditable models and has been evaluated through comparative experiments with 30 previous studies, using 15 datasets. Additionally, the interpretability graphs offer valuable insights for model evaluation and experimental design, facilitating informed decision-making. BioPrediction-PPI demonstrates competitive performance across multiple datasets, even without the use of deep learning, and is a transparent, white-box model that can be easily used by biologists and practitioners without a background in computer science. The proposed framework has the potential to accelerate research in biology and related fields by making AI-driven tools more accessible.

bioinformatics↗

bioRxiv2020

MathFeature: Feature Extraction Package for Biological Sequences Based on Mathematical Descriptors

Machine learning algorithms have been very successfully applied to extract new and relevant knowledge from biological sequences. However, the predictive performance of these algorithms is largely affected by how the sequences are represented. Thereby, the main challenge is how to numerically represent a biological sequence in a numeric vector with an efficient mathematical expression. Several feature extraction techniques have been proposed for biological sequences, where most of them are available in feature extraction packages. However, there are relevant approaches that are not available in existing packages, techniques based on mathematical descriptors, e.g., Fourier, entropy, and graphs. Therefore, this paper presents a new package, named MathFeature, which implements mathematical descriptors able to extract relevant information from biological sequences. MathFeature provides 20 approaches based on several studies found in the literature, e.g., multiple numeric mappings, genomic signal processing, chaos game theory, entropy, and complex networks. MathFeature also allows the extraction of alternative features, complementing the existing packages. Availability and implementationMathFeature is freely available at https://bonidia.github.io/MathFeature/ or https://github.com/Bonidia/MathFeature Contactbonidia@usp.br, rpbonidia@gmail.com

bioinformatics↗

bioRxiv2020

Feature Extraction Approaches for Biological Sequences: A Comparative Study of Mathematical Models

The number of available biological sequences has increased significantly in recent years due to various genomic sequencing projects, creating a huge volume of data. Consequently, new computational methods are needed to analyze and extract information from these sequences. Machine learning methods have shown broad applicability in computational biology and bioinformatics. The utilization of machine learning methods has helped to extract relevant information from various biological datasets. However, there are still several obstacles that motivate new algorithms and pipeline proposals, mainly involving feature extraction problems, in which extracting significant discriminatory information from a biological set is challenging. Considering this, our work proposes to study and analyze a feature extraction pipeline based on mathematical models (Numerical Mapping, Fourier, Entropy, and Complex Networks). As a case study, we analyze Long Non-Coding RNA sequences. Moreover, we divided this work into two studies, e.g., (I) we assessed our proposal with the most addressed problem in our review, e.g., lncRNA vs. mRNA; (II) we tested its generalization on different classification problems, e.g., circRNA vs. lncRNA. The experimental results demonstrated three main contributions: (1) An in-depth study of several mathematical models; (2) a new feature extraction pipeline and (3) its generalization and robustness for distinct biological sequence classification.

bioinformatics↗