AI for Life: Trends in Artificial Intelligence for Biotechnology

HOLZINGER, Andreas, Katharina KEIBLINGER, Petr HOLUB, Kurt ZATLOUKAL and Heimo MÜLLER. AI for Life: Trends in Artificial Intelligence for Biotechnology. NEW BIOTECHNOLOGY. NETHERLANDS: ELSEVIER, 2023, vol. 74, No 1, p. 16-24. ISSN 1871-6784. Available from: https://dx.doi.org/10.1016/j.nbt.2023.02.001.

Basic information

• Original name: AI for Life: Trends in Artificial Intelligence for Biotechnology
• Authors: HOLZINGER, Andreas, Katharina KEIBLINGER, Petr HOLUB (203 Czech Republic, guarantor, belonging to the institution), Kurt ZATLOUKAL and Heimo MÜLLER.
• Edition: NEW BIOTECHNOLOGY, NETHERLANDS, ELSEVIER, 2023, 1871-6784.

Other information

• Original language: English
• Type of outcome: Article in a journal
• Field of Study: 10201 Computer sciences, information science, bioinformatics
• Country of publisher: Germany
• Confidentiality degree: is not subject to a state or trade secret
• WWW: ScienceDirect
• Impact factor: Impact factor: 5.400 in 2022
• RIV identification code: RIV/00216224:14610/23:00130298
• Organization unit: Institute of Computer Science
• Doi: http://dx.doi.org/10.1016/j.nbt.2023.02.001
• UT WoS: 000936300300001
• Keywords in English: AI; biotechology; explainable artificial intelligence; XAI; life sciences; provenance

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.

Links

• 101079183, interní kód MU: Name: BioMedAI TWINNING

Investor: European Union, Widening participation and strengthening the European Research Area