专刊介绍

随着基因组学、转录组学、蛋白质组学、代谢组学、宏基因组学、临床化学等技术的进步，产生了海量的电子健康记录(Electronic Health Records，EHR)、能量代谢追踪、成像、分子数据等，高维计算、人工智能和机器学习等方面研究不断深入，个体及群体的分子组学研究已经达到了前所未有的多维度和多尺度。当前，转化表型组学(Translational Phenomics)通过与其他组学（如基因组学、转录组学、蛋白质组学、代谢组学等）和相关学科相结合，有助于开展多组学测量，测一切之可测，以共同促进个体全周期生命健康发展。

本专刊鼓励提交整合了创新性的和最先进的表型组学技术的相关应用型原创研究论文及综述等，包括临床肿瘤、衰老、神经退行性疾病、代谢性疾病等方面研究。

感兴趣的主题包括但不限于:

1. 融入多组学技术的临床队列研究，以改进疾病诊断、治疗和预后等；

2. 使用两种或更多技术手段评价新型治疗方案、新药或药物再利用的临床试验；

3. 整合多组学数据的新算法，便于疾病风险分层与阻抗测量；

4. 开发新型标志物，从而更好地量化和监测健康和疾病状态；

5. 与生物标志物相关的表观遗传原创研究；

6. 基于表观遗传修饰的药物研发新进展；

7. 通过转化表型组学探究个性化治疗和疾病机制研究的新技术；

8. 深度计算、人工智能与机器学习在转化表型组学中的应用。

客座编辑

Leroy Hood 院士

Phenome Health首席执行官，美国系统生物学研究所(ISB)首席战略官兼联合创始人，美国国家科学院院士，美国国家工程院院士，美国国家医学院院士，美国艺术与科学院院士，中国科学院外籍院士

田强 教授

上海国际人类表型组研究院系统生物学教授、主任; 美国系统生物学研究所(ISB)客座教授

张剑 主任医师

复旦大学附属肿瘤医院，肿瘤内科主任医师

方海 研究员

上海交通大学医学院附属瑞金医院，转化医学国家重大科技基础设施（上海）- 瑞金基地，研究员

魏巍 助理教授

美国系统生物学研究所(ISB)，助理教授

乔妞 助理研究员

上海交通大学医学院附属瑞金医院，转化医学国家重大科技基础设施（上海）- 瑞金基地，助理研究员

截稿日期：2024年9月30日

专刊详细说明请见：

https://link.springer.com/collections/cdgeiiiafe

投稿指南详见：

https://www.springer.com/journal/43657/submission-guidelines

备注：投稿时在“Additional information”标签下选择专刊“Translational Phenomics”，并在Cover Letter里注明投稿专刊“Translational Phenomics”。

如有相关问题，请联系特邀编委负责人（田强教授：qiang.tian@systemsbiology.org）或期刊编辑部（phenomics@fudan.edu.cn）。

We are pleased to invite you to submit articles, reviews, protocols, brief communications and correspondence to the special issue entitled 'Translational Phenomics' of Phenomics, a new high-quality journal from Springer Nature, which is dedicated to publishing the finest articles on the emerging field of phenomics.

Technological advances in genomics, proteomics, metabolomics, metagenomics, and clinical chemistry have enabled molecular rendering of human bodies in unprecedented dimension and granularity, elevating clinical studies onto new height. With accumulating electronic health records (EHR), physical activity tracking, imaging, molecular data, coupled with high dimensional computing, AI and machine learning, deep phenotyping has exhibited demonstrable advantages over single-dimensional measurement in medical research by profiling diseased patients or healthy individuals for the benefit of human health.

Collective efforts in which clinical studies accompanied and enhanced by multi-omics testing integrated with digital data analysis in cross-sectional and/or longitudinal axes toward improved clinical outcome-- this strategy is making inroads into heightened understanding of disease mechanisms, optimized drug or therapeutic responses, and ultimately extended healthy lifespan.

This special issue encourages translational studies that integrate innovative and state-of-the-art phenomic technologies into clinical scenarios, including clinical oncology, aging, neurodegenerative diseases, metabolic diseases. etc. The topics covered in this issue include, but are not limited to:

- Clinical cohort studies incorporating multi-omics technologies for improved diagnostics, prognostics, and therapeutics;

- Clinical trials employing two or more technological platforms to evaluate new therapeutic regimes, new or repurposed drugs;

- New algorithm for multi-omics data integration for stratification of medical conditions and impedance matching with treatment;

- Develop new biological index for better quantification and monitoring of health and diseases;

- New research results of epigenetic changes as biomarkers for disease risk, diagnosis, prognosis, or response in clinical routine or clinical trials;

- New advances in preclinical and clinical research of drugs based on epigenetic modifications;

- Advanced technologies embracing systems thinking and multi-modal strategies, to address unmet needs in translational medicine towards personalized treatment strategies and better understanding of disease mechanisms, including: single-cell or spatial multi-omics sequencing, novel imaging modalities, deep immune phenotyping, somatic cell gene editing, innovative tissue and organ models, new chemistries, e.g. peptoids, PROTACs.

- Computation, AI and machine learning in translational medicine：

1) Use machine learning to predict diseases early by analyzing multi-omics data, electronic health records, and clinical information.

2) Develop AI tools to assist healthcare professionals in diagnosing diseases, suggesting treatments, and predicting patient responses.

3) Harness AI to speed up drug discovery through predicting drug interactions, finding potential therapeutic compounds, and optimizing drug candidates.

4) Use AI to build models that identify cancer subtypes, predict treatment responses, and personalize cancer therapies.

5) Develop computational methods to combine data from genomics, proteomics, metabolomics, and clinical records to understand a patient's overall health and disease status.

Submission deadline: 30 September 2024

Submission instructions

Before submitting your manuscript, please ensure you have carefully read the Submission Guidelines for Phenomics. The complete manuscript should be submitted online through: https://www.editorialmanager.com/pnmc.

To ensure that you submit to the correct special issue, please select the appropriate special issue title under the 'Additional information' tab upon submission. In addition, indicate within your cover letter that you wish your manuscript to be considered as part of the special issue 'Translational Phenomics'.

All submissions will undergo rigorous peer review and accepted articles will be published within the journal as a collection.

For any questions about a potential submission of this special issue, please contact Dr Qiang TIAN (qiang.tian@systemsbiology.org) or the editorial office of Phenomics (phenomics@fudan.edu.cn) using ‘Translational Phenomics’ as the email title.