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Related Concept Videos

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Sometimes we want to see how people change over time, as in studies of human development and lifespan. When we test the same group of individuals repeatedly over an extended period of time, we are conducting longitudinal research. Longitudinal research is a research design in which data-gathering is administered repeatedly over an extended period of time. For example, we may survey a group of individuals about their dietary habits at age 20, retest them a decade later at age 30, and then again...
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Longitudinal studies are also widely used in other medical and social science fields. For instance, in cardiovascular research, they can monitor patients' health over decades to identify risk factors for heart disease, such as high cholesterol or smoking, and evaluate the long-term effectiveness of preventive measures. Similarly, in mental health studies, researchers might follow individuals from adolescence into adulthood to understand the development and progression of conditions like...
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Genomics is the science of genomes: it is the study of all the genetic material of an organism. In humans, the genome consists of information carried in 23 pairs of chromosomes in the nucleus, as well as mitochondrial DNA. In genomics, both coding and non-coding DNA is sequenced and analyzed. Genomics allows a better understanding of all living things, their evolution, and their diversity. It has a myriad of uses: for example, to build phylogenetic trees, to improve productivity and...
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Databases to Efficiently Manage Medium Sized, Low Velocity, Multidimensional Data in Tissue Engineering
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Longitudinal big biological data in the AI era.

Adil Mardinoglu1,2, Hasan Turkez3, Minho Shong4

  • 1Science for Life Laboratory, KTH - Royal Institute of Technology, Stockholm, Sweden. adilm@scilifelab.se.

Molecular Systems Biology
|August 6, 2025
PubMed
Summary
This summary is machine-generated.

Artificial intelligence (AI) and systems biology leverage big biological data for health insights. This approach integrates multi-omics, clinical, and environmental data to advance precision medicine and AI-driven healthcare.

Keywords:
Artificial IntelligenceDigital TwinsLongitudinal Multi-omics DataPrecision MedicineSystems Biology

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Area of Science:

  • Computational biology
  • Genomics
  • Systems biology

Background:

  • Big biological data integrates multi-omics, clinical, wearable, imaging, diet, drug, and environmental factors.
  • Advancements in omics technologies and computational power enable complex biological data analysis.
  • Longitudinal and multi-layered data are essential for understanding whole-body functions in health and disease.

Purpose of the Study:

  • To review AI and systems biology applications in multi-omics data integration and interpretation.
  • To highlight the role of big biological data in creating digital twins and discovering biomarkers/drug targets.
  • To discuss the incorporation of big biological data into clinical practice for AI-driven healthcare.

Main Methods:

  • Review of recent AI and systems biology applications in multi-omics data analysis.
  • Examination of global multi-omics datasets to understand biological layer interactions over time.
  • Analysis of big biological data integration for clinical decision support systems.

Main Results:

  • AI and systems biology facilitate the creation of digital twins and identification of novel biomarkers and drug targets.
  • Multi-omics datasets reveal temporal interactions across biological layers, enhancing precision health.
  • Incorporating big biological data is crucial for developing AI-driven clinical decision support systems.

Conclusions:

  • Big biological data, integrated via AI and systems biology, is foundational for precision medicine.
  • The development of AI-driven hospitals requires robust clinical decision support systems.
  • A paradigm shift towards AI and systems biology-based healthcare models is essential.