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Updated: May 12, 2026

Sample Preparation for Mass Spectrometry-based Identification of RNA-binding Regions
Published on: September 28, 2017
Accessibility in proteins and RNAs interactions prediction with machine learning: are we overlooking non-experts?
Bruno R Florentino1,2, Robson P Bonidia1,2,3, André C P L F de Carvalho1,2
1Institute of Mathematical and Computer Sciences, University of São Paulo, Avenida Trabalhador São-Carlense 400, Centro, São Carlos, SP 13560-924, Brazil.
Predicting biological sequence interactions is crucial for understanding life processes. This study reviews AI tools for sequence interaction prediction, focusing on accessibility for non-expert researchers.
Area of Science:
- Bioinformatics
- Computational Biology
- Genomics
Background:
- The exponential growth of biomolecular data necessitates advanced methods for analyzing molecular interactions.
- Predicting interactions between DNA, RNA, and proteins is vital for understanding gene regulation and immune responses.
- Artificial Intelligence (AI) has shown promise in identifying novel biological sequence interactions.
Purpose of the Study:
- To investigate state-of-the-art AI tools for biological sequence interaction prediction.
- To assess the public accessibility and usability of these tools for researchers, particularly non-experts in AI.
- To guide users in selecting appropriate tools based on their expertise and data.
Main Methods:
- Systematic review of recent studies on biological sequence interaction prediction.
- Analysis of tool availability, focusing on public accessibility and user-friendliness.
- Compilation and discussion of input requirements and output types for various computational tools.
- Evaluation of accessibility features, from web servers to end-to-end frameworks.
Main Results:
- A significant gap exists between the number of AI-driven prediction studies and the availability of user-friendly, accessible tools.
- Tools vary widely in accessibility, from simple web servers to complex frameworks requiring significant computational expertise.
- Many available tools offer limited automation or require specialized knowledge for implementation and use.
- Understanding tool input/output is crucial for effective application in diverse research scenarios.
Conclusions:
- There is a pressing need for more accessible and user-friendly AI tools for biological sequence interaction prediction.
- Researchers should carefully consider tool accessibility and their own expertise when selecting computational solutions.
- Future development should prioritize user-friendly interfaces and comprehensive documentation to broaden tool adoption.
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