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Biological-World AI

A special issue of Applied Sciences (ISSN 2076-3417). This special issue belongs to the section "Biomedical Engineering".

Deadline for manuscript submissions: closed (31 July 2023) | Viewed by 1702

Special Issue Editors


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Guest Editor
College of Informatics, Huazhong Agricultural University, Wuhan, China
Interests: bioinformatics; biological big data; epigenetics; artificial intelligence and machine learning

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Guest Editor
College of Informatics, Huazhong Agricultural University, Wuhan, China
Interests: DNA methylation; predicted model; sequence complexity

E-Mail Website
Guest Editor
College of Informatics, Huazhong Agricultural University, Wuhan 430070, China
Interests: bioinformatics; computational biomedicine; network medicine; graph learning; machine/deep learning; biomedical big data mining
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Biological-world AI is interest to mathematicians, statisticians and computer scientists who apply their work to biological problems. This issue of Applied Sciences aims to provide broad coverage of the use of Computing and Artificial Intelligence by mathematicians and statisticians (including machine learning, deep learning, reinforcement learning, computer vision, natural language processing, and genetic and evolutionary computing), and by scientists who apply their work to biological problems. This Special Issue welcomes papers on approaches to these biological AI problems that can rapidly detect, characterize, predict and accommodate novelty in life science, biology and biomedicine, focusing on large data acquisition, analysis and curation. There are countless opportunities for computing and artificial intelligence to augment human capabilities and knowledge in fields such as scientific discovery, healthcare, medical diagnostics and agriculture.

TOPICS

  1. Computational biology of molecular structure, function and evolution;
  2. Computational systems biology;
  3. Next generation sequencing and high-throughput methods;
  4. Cheminformatics and pharmacogenomics;
  5. Computational methods and bioinformatics of disease;
  6. Biomedical and health informatics;
  7. Deep learning modeling in plant sciences.

Dr. Yaping Fang
Prof. Dr. Xuehai Hu
Prof. Dr. Wen Zhang
Guest Editors

Manuscript Submission Information

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Published Papers (1 paper)

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Review

24 pages, 1967 KiB  
Review
Can Semantics Uncover Hidden Relations between Neurodegenerative Diseases and Artistic Behaviors?
by Adam Koletis, Pavlos Bitilis, Nikolaos Zafeiropoulos and Konstantinos Kotis
Appl. Sci. 2023, 13(7), 4287; https://doi.org/10.3390/app13074287 - 28 Mar 2023
Cited by 1 | Viewed by 1470
Abstract
Semantics play a crucial role in organizing domain knowledge, schematizing it, and modeling it into classes of objects and relationships between them. Knowledge graphs (KGs) use semantic models to integrate and represent different types of data. This study aimed to systematically review related [...] Read more.
Semantics play a crucial role in organizing domain knowledge, schematizing it, and modeling it into classes of objects and relationships between them. Knowledge graphs (KGs) use semantic models to integrate and represent different types of data. This study aimed to systematically review related work on the topics of ontologies for neurodegenerative diseases (NDs), ontology-based expert systems for NDs, and the artistic behavior of ND patients. The utilization of ontologies allows for a more comprehensive understanding of the progression and etiology of NDs, the structure and function of the brain, and the artistic expression associated with these diseases. The data collected from ND patients highlights the presence of cases where artistic expression can be linked to the disease. By developing fuzzy ontologies for NDs and incorporating them into expert systems, early detection and monitoring can be supported. Through our systematic review, we identify and discuss open issues and challenges in understanding the relationship between ND patients and their artistic behavior. We also conclude that ontology-based expert systems hold immense potential in uncovering hidden correlations between these two. Further research in this area has the potential to address key research questions and provide deeper insights. Full article
(This article belongs to the Special Issue Biological-World AI)
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