Call for Paper

Special Issue on “Artificial Intelligence in Omics”

We are pleased to announce a special issue, to be published in the Winter of 2022, on “Artificial Intelligence in Omics” in the journal Genomics, Proteomics & Bioinformatics (GPB).

In recent years, artificial intelligence (AI) has enabled breakthroughs across diverse biomedical fields, such as protein structure prediction, disease diagnosis, and drug discovery. AI is a powerful approach for solving complex problems in the analysis and interpretation of omics data as well as multi-omic and medical data integration. This GPB special issue aims to provide a forum for advances in the development and application of AI-based tools in omics.

Topics of the special issue include, but are not limited to:

  • AI-based models, methods, and software for the processing, analysis, visualization, and interpretation of omics data

  • AI-based algorithms for the integrative analysis of omics, clinical, and health data, such as medical imaging, electronic health record, social media, and epidemiological data

  • AI-based platforms for improving disease diagnosis, precision medicine, and patient care

  • AI-based approaches for protein structure prediction and drug discovery

  • AI-driven rational design, such as de novo DNA or protein design

We welcome manuscripts presenting original research studies and reviews. Deadline for submissions to this special issue is May 31, 2022. Manuscripts should be prepared according to the Guide for Authors (https://www.journals.elsevier.com/genomics-proteomics-and-bioinformatics) and the manuscript template (http://gpb.big.ac.cn). Manuscripts should be submitted online at https://www.editorialmanager.com/GPB. Please select “SI: AI in Omics” when submitting your manuscript to this special issue.

Guest editors for this special issue are Dr. Feng Gao (Tianjin University), Dr. Yi Xing (Children's Hospital of Philadelphia & University of Pennsylvania), and Dr. Kun Huang (Indiana University School of Medicine).

For further information, please contact us at:

Dr. Feng Gao (fgao@tju.edu.cn);

Dr. Yi Xing (xingyi@email.chop.edu);

Dr. Kun Huang (kunhuang@iu.edu);

Editorial Office (editor@big.ac.cn).

 

Artificial Intelligence in Omics专辑征稿

近年来，人工智能(Artificial Intelligence；简称AI)在蛋白质结构预测、疾病诊断、药物发现等生物医学领域取得了突破性进展，成为解决组学数据分析和阐释、多组学和医学数据整合等复杂问题的“利器”。为推动人工智能在组学中的发展和应用，Genomics, Proteomics & Bioinformatics (GPB)拟于2022年冬天出版Artificial Intelligence in Omics专辑。

专辑Guest Editors:

高峰 天津大学 

        fgao@tju.edu.cn

黄昆 印第安纳大学医学院 

        kunhuang@iu.edu

邢毅 费城儿童医院&宾夕法尼亚大学 

        xingyi@email.chop.edu

专辑的主题包括但不限于：

  • 基于AI的模型、方法和软件，用于组学数据处理、分析、可视化和阐释

  • 基于AI的算法，用于整合分析组学、临床和健康数据，如医学影像、电子病历、社交媒体和流行病学数据

  • 基于AI的平台，用于改善疾病诊断、精准医学和患者治疗

  • 基于AI的方法，用于蛋白质结构预测和药物发现

  • AI驱动的理性设计，如DNA或蛋白质的从头设计

欢迎大家提交原创研究和综述稿件，本期专辑的投稿截止日期是2022年5月31日。文章类型和格式请参见期刊主页的作者指南(https://www.journals.elsevier.com/genomics-proteomics-and-bioinformatics)和稿件模板(http://gpb.big.ac.cn)。所有稿件请网上提交(https://www.editorialmanager.com/GPB)。提交稿件时文章类型(Article Type)请选择“SI: AI in Omics”；具体稿件类型请在cover letter 注明。

Guest Editors简介

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高峰：天津大学理学院教授，主要从事微生物生物信息学与合成生物学研究，现担任天津大学生物信息中心主任，Faculty of 1000 (现 Faculty Opinions)专家等。


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黄昆：印第安纳大学医学院，印第安纳大学讲席教授，主要从事基于机器学习与计算机视觉方法的计算病理学与整合基因组学研究，现为美国医学与生物工程学院(AIMBE)会士。


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邢毅：宾夕法尼亚大学医学院教授，费城儿童医院Francis West Lewis讲席教授。主要从事计算生物学、RNA基因组学、人类遗传学、精准医学和肿瘤免疫学等研究。现为费城儿童医院计算与基因组医学中心主任。