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Holzinger, A; Keiblinger, K; Holub, P; Zatloukal, K; Müller, H.
AI for life: Trends in artificial intelligence for biotechnology.
N Biotechnol. 2023; 74:16-24 Doi: 10.1016/j.nbt.2023.02.001
Web of Science PubMed FullText FullText_MUG

 

Führende Autor*innen der Med Uni Graz
Holzinger Andreas
Co-Autor*innen der Med Uni Graz
Müller Heimo
Zatloukal Kurt
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Abstract:
Due to popular successes (e.g., ChatGPT) Artificial Intelligence (AI) is on everyone's lips today. When advances in biotechnology are combined with advances in AI unprecedented new potential solutions become available. This can help with many global problems and contribute to important Sustainability Development Goals. Current examples include Food Security, Health and Well-being, Clean Water, Clean Energy, Responsible Consumption and Production, Climate Action, Life below Water, or protect, restore and promote sustainable use of terrestrial ecosystems, sustainably manage forests, combat desertification, and halt and reverse land degradation and halt biodiversity loss. AI is ubiquitous in the life sciences today. Topics include a wide range from machine learning and Big Data analytics, knowledge discovery and data mining, biomedical ontologies, knowledge-based reasoning, natural language processing, decision support and reasoning under uncertainty, temporal and spatial representation and inference, and methodological aspects of explainable AI (XAI) with applications of biotechnology. In this pre-Editorial paper, we provide an overview of open research issues and challenges for each of the topics addressed in this special issue. Potential authors can directly use this as a guideline for developing their paper.
Find related publications in this database (using NLM MeSH Indexing)
Artificial Intelligence - administration & dosage
Biotechnology - administration & dosage
Data Mining - administration & dosage
Ecosystem - administration & dosage
Knowledge Bases - administration & dosage

Find related publications in this database (Keywords)
Artificial Intelligence
Biotechnology
Deep Learning
Digital Transformation
Machine Learning
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