Detailed Information on Publication Record
2023
AI for Life: Trends in Artificial Intelligence for Biotechnology
HOLZINGER, Andreas, Katharina KEIBLINGER, Petr HOLUB, Kurt ZATLOUKAL, Heimo MÜLLER et. al.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
Language
English
Type of outcome
Článek v odborném periodiku
Field of Study
10201 Computer sciences, information science, bioinformatics
Country of publisher
Germany
Confidentiality degree
není předmětem státního či obchodního tajemství
References:
Impact factor
Impact factor: 5.400 in 2022
RIV identification code
RIV/00216224:14610/23:00130298
Organization unit
Institute of Computer Science
UT WoS
000936300300001
Keywords in English
AI; biotechology; explainable artificial intelligence; XAI; life sciences; provenance
Změněno: 20/3/2024 17:20, doc. RNDr. Petr Holub, Ph.D.
Abstract
V originále
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 |
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