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Artificial Intelligence and Genomics


We solicit manuscripts for a topical collection on "AI and Genomics" in Human Genomics.  As you know, human genomics has become one of the most active areas of cutting-edge life sciences and grown to be one of the largest generators of data. In addition to the value of greatly enhanced experimental examination and validation, human genomics relies on emerging, powerful computational approaches, such as Big Data analyses and artificial intelligence (AI) including network science, machine learning, deep learning, text mining, knowledge-based database construction, and even quantum computing. The collection would also be a home for articles focusing on practical applications.

We welcome original articles as well as review papers. Please indicate in your cover letter that your article is intended for the topical collection on "AI and Genomics” and select the collection upon submission. Submissions can also retrospectively be assigned to the collection. Please notify the Journal Editorial Office accordingly.  

We look forward to receiving high-quality submissions of significance that can make further contributions to the field of human genomics.

Guest Editors

Kirill A. Veselkov, Imperial College, London, UK; Takashi Gojobori, King Abdullah University of Science and Technology, Thuwal, Saudi Arabia; David van Dijk, Yale University, New Haven, CT, USA

  1. Phenylketonuria (PKU) is caused by mutations in the phenylalanine hydroxylase (PAH) gene. Our study aimed to predict the phenotype using the allelic genotype.

    Authors: Yang Fang, Jinshuang Gao, Yaqing Guo, Xiaole Li, Enwu Yuan, Erfeng Yuan, Liying Song, Qianqian Shi, Haiyang Yu, Dehua Zhao and Linlin Zhang
    Citation: Human Genomics 2023 17:34
  2. Cuproptosis, as a copper-induced mitochondrial cell death, has attracted extensive attention recently, especially in cancer. Although some key regulatory genes have been identified in cuproptosis, the related ...

    Authors: Shichao Liu, Shoucai Zhang, Yingjie Liu, XiaoRong Yang and Guixi Zheng
    Citation: Human Genomics 2023 17:22
  3. Long-read sequencing technologies have the potential to overcome the limitations of short reads and provide a comprehensive picture of the human genome. However, the characterization of repetitive sequences by...

    Authors: Ko Ikemoto, Hinano Fujimoto and Akihiro Fujimoto
    Citation: Human Genomics 2023 17:21
  4. SpliceAI is an open-source deep learning splicing prediction algorithm that has demonstrated in the past few years its high ability to predict splicing defects caused by DNA variations. However, its outputs pr...

    Authors: Jean-Madeleine de Sainte Agathe, Mathilde Filser, Bertrand Isidor, Thomas Besnard, Paul Gueguen, Aurélien Perrin, Charles Van Goethem, Camille Verebi, Marion Masingue, John Rendu, Mireille Cossée, Anne Bergougnoux, Laurent Frobert, Julien Buratti, Élodie Lejeune, Éric Le Guern…
    Citation: Human Genomics 2023 17:7
  5. Skin cutaneous melanoma (SKCM) is one of the most highly prevalent and complicated malignancies. Glycolysis and cholesterogenesis pathways both play important roles in cancer metabolic adaptations. The main ai...

    Authors: Enchong Zhang, Yijing Chen, Shurui Bao, Xueying Hou, Jing Hu, Oscar Yong Nan Mu, Yongsheng Song and Liping Shan
    Citation: Human Genomics 2021 15:53
  6. The field of pharmacogenomics focuses on the way a person’s genome affects his or her response to a certain dose of a specified medication. The main aim is to utilize this information to guide and personalize ...

    Authors: Maria-Theodora Pandi, Maria Koromina, Iordanis Tsafaridis, Sotirios Patsilinakos, Evangelos Christoforou, Peter J. van der Spek and George P. Patrinos
    Citation: Human Genomics 2021 15:51
  7. Recent efforts in the field of nutritional science have allowed the discovery of disease-beating molecules within foods based on the commonality of bioactive food molecules to FDA-approved drugs. The pioneerin...

    Authors: Guadalupe Gonzalez, Shunwang Gong, Ivan Laponogov, Michael Bronstein and Kirill Veselkov
    Citation: Human Genomics 2021 15:33
  8. In this paper, we introduce a network machine learning method to identify potential bioactive anti-COVID-19 molecules in foods based on their capacity to target the SARS-CoV-2-host gene-gene (protein-protein) ...

    Authors: Ivan Laponogov, Guadalupe Gonzalez, Madelen Shepherd, Ahad Qureshi, Dennis Veselkov, Georgia Charkoftaki, Vasilis Vasiliou, Jozef Youssef, Reza Mirnezami, Michael Bronstein and Kirill Veselkov
    Citation: Human Genomics 2021 15:1