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==Others==
=== Emerging AI-driven hybrid roles ===
The increasing integration of artificial intelligence and machine learning into bioinformatics has given rise to hybrid professional roles that combine expertise in biology, computational sciences, and AI engineering.<ref>{{cite journal|last=Chicco|first=Davide|title=Ten quick tips for machine learning in computational biology|journal=Bioinformatics|volume=36|issue=20|pages=5404–5410|year=2020|doi=10.1093/bioinformatics/btaa684}}</ref><ref>{{cite news|last=Reuters|title=EvolutionaryScale lands $142 mln to advance AI in biology|date=2024-06-25|publisher=Reuters}}</ref>
These roles extend traditional bioinformatics by incorporating software development skills, cloud computing, and AI model deployment, and are becoming more common in academic research, biotechnology, and pharmaceutical industries.
Examples of such emerging roles include:
* BioAI Software Engineer - focuses on developing AI-powered applications for genomics, proteomics, and molecular modeling.<ref>{{cite web|title=BioAI Software Engineer Roadmap|url=https://bioaisoftware.engineer|access-date=2025-08-13}}</ref>
* Computational Genomics Engineer - builds algorithms and pipelines for large-scale genomic data processing.
* AI Drug Discovery Scientist - uses machine learning for compound screening, target identification, and predictive modeling.
* Biomedical Data Scientist - integrates multi-omics datasets with clinical and imaging data for translational research.
These hybrid positions are expected to expand as AI models - such as transformer-based architectures and large language models - are increasingly applied to biological sequence analysis, protein structure prediction, and multi-omics integration.<ref>{{cite journal|last=Helleckes|first=LM|title=Machine learning in bioprocess development: From promise to practice|journal=arXiv|year=2022}}</ref>
===Literature analysis===
{{main|Text mining|Biomedical text mining}}
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