Integrative Multi‐Omics and Routine Blood Analysis Using Deep Learning: Cost‐Effective Early Prediction of Chronic Disease Risks

Chronic noncommunicable diseases (NCDS) are often characterized by gradual onset and slow progression, but the difficulty in early prediction remains a substantial health challenge worldwide. This study aims to explore the interconnectedness of disease occurrence through multi-omics studies and validate it in large-scale electronic health records. In response, the research examined multi-omics data from 160 sub-healthy individuals at high altitude and then a deep learning model called Omicsformer is developed for detailed analysis and classification of routine blood samples. Omicsformer adeptly identified potential risks for nine diseases including cancer, cardiovascular conditions, and psychiatric conditions. Analysis of risk trajectories from 20 years of large clinical patients confirmed the validity of the group in preclinical risk assessment, revealing trends in increased disease risk at the time of onset. Additionally, a straightforward NCDs risk prediction system is developed, utilizing basic blood test results. This work highlights the role of multiomics analysis in the prediction of chronic disease risk, and the development and validation of predictive models based on blood routine results can help advance personalized medicine and reduce the cost of disease screening in the community.

Authors

Institutions

Publication Details

Journal
Advanced Science
Published
2025-04-02
DOI
https://doi.org/10.1002/advs.202412775
Citations
9
Primary Topic
Bioinformatics and Genomic Networks
Type
article
Field-Weighted Citation Impact
7.25

Funders

Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Integrative Multi‐Omics and Routine Blood Analysis Using Deep Learning: Cost‐Effective Early Prediction of Chronic Disease Risks

Yi Jiang, Zhibin Dong, Kaikai Guo, Xiaojing Zhao et al.
9 citations
Advanced Science
Bioinformatics and Genomic Networks
7.25
article

Integrative Multi‐Omics and Routine Blood Analysis Using Deep Learning: Cost‐Effective Early Prediction of Chronic Disease Risks

Yi Jiang, Zhibin Dong, Kaikai Guo, Xiaojing Zhao, Dongjin Wang, Chongyou Rao, Kunlun He, Dayu Hu, Chenghui Zhao, Shihui Fu, Hebin Che, En Zhu, Qin Zhong, Meng Liu, Siwei Wang, Chunlei Liu, Pei Li, Xinwang Liu, Zhihan Wang, Suyuan Liu, Juntao Gao
article en
9 citations

Abstract

Chronic noncommunicable diseases (NCDS) are often characterized by gradual onset and slow progression, but the difficulty in early prediction remains a substantial health challenge worldwide. This study aims to explore the interconnectedness of disease occurrence through multi-omics studies and validate it in large-scale electronic health records. In response, the research examined multi-omics data from 160 sub-healthy individuals at high altitude and then a deep learning model called Omicsformer is developed for detailed analysis and classification of routine blood samples. Omicsformer adeptly identified potential risks for nine diseases including cancer, cardiovascular conditions, and psychiatric conditions. Analysis of risk trajectories from 20 years of large clinical patients confirmed the validity of the group in preclinical risk assessment, revealing trends in increased disease risk at the time of onset. Additionally, a straightforward NCDs risk prediction system is developed, utilizing basic blood test results. This work highlights the role of multiomics analysis in the prediction of chronic disease risk, and the development and validation of predictive models based on blood routine results can help advance personalized medicine and reduce the cost of disease screening in the community.

Advanced ScienceVol. 12(22)
National University of Defense Technology (CN), West China Medical Center of Sichuan University (CN), Chinese PLA General Hospital (CN), Nanjing Drum Tower Hospital (CN), The Affiliated Yongchuan Hospital of Chongqing Medical University (CN), Hainan 301 Hospital (CN), Chongqing Medical University (CN), Tsinghua University (CN)
National Natural Science Foundation of China, Ministry of Science and Technology of the People's Republic of China, General Hospital of People’s Liberation Army, National Key Research and Development Program of China
Good health and well-being
Openalex Percentile: Top 3%
Bioinformatics and Genomic Networks
7.25
DOIOpen Access PDFOpenAlex
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.