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The process of breathing involves the periodic intake and expulsion of air, known as the respiratory cycle, which typically lasts about five seconds. Modeling the volume of air inhaled into the lungs as a function of time provides insight into both the dynamics and efficiency of pulmonary ventilation. This volume is determined by integrating the airflow rate over time, which captures the cumulative effect of air entering the lungs.Sinusoidal Model of AirflowAirflow during respiration is not...
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Hydrostatic force is a fluid's total force at rest on a surface. For a horizontal surface submerged at a fixed depth, the pressure is constant and calculated as the product of fluid density, gravitational acceleration, and depth. In the case of a vertical dam wall submerged in water, this force is not evenly distributed due to the increasing pressure with depth. This variation arises from the cumulative weight of the water above each point. Integration is used to account for the continuous...
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Rotational equilibrium provides a natural framework for defining the center of mass of a system. For a plank balanced on a pivot with two unequal masses, equilibrium is achieved when the net torque about the pivot is zero. Torque is defined as the product of a force and its perpendicular distance from the pivot. When the torques due to all forces cancel, the pivot coincides with the center of mass of the system.For a system composed of several discrete point masses, the center of mass lies at...
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Related Experiment Video

Updated: Feb 7, 2026

Author Spotlight: Cost-Effective Transcriptomic Drug Screening - Unlocking New Targets
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Reproducible and Multi-Study Transcriptomic Integration with disint, Disease Integration and Clustering Toolkit, and

Yi Cong1, Naoki Osada1, Toshinori Endo1

  • 1Laboratory of Information Biology, Information Science and Technology, Hokkaido University, Sapporo, Japan.

Omics : a Journal of Integrative Biology
|February 6, 2026
PubMed
Summary

We developed disint, a novel Python framework for integrating large transcriptomic datasets across diseases. This toolkit enables standardized analysis, improving reproducibility and facilitating drug repositioning discovery.

Keywords:
big datadata analysis toolsdiseasomedrug repositioningintegrative biologysystems biologytranscriptome integration

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Area of Science:

  • Bioinformatics
  • Computational Biology
  • Genomics

Background:

  • Integrating large-scale transcriptomic datasets across diverse diseases presents significant challenges due to inconsistent preprocessing and lack of standardized analytical frameworks.
  • Existing pipelines often involve manual parameter tuning and fragmented scripts, hindering cross-dataset comparability and downstream interpretation.

Purpose of the Study:

  • To develop an open-source Python framework, disint (disease integration and clustering toolkit), for standardized cross-dataset transcriptomic data integration, embedding, and clustering.
  • To implement a prototype downstream module, disease reposition, for extracting disease-specific gene signatures and identifying potential drug repositioning candidates.

Main Methods:

  • The disint framework employs housekeeping gene-based normalization and disease-specific log2 fold-change computation.
  • It features automated Uniform Manifold Approximation and Projection (UMAP) hyperparameter optimization and adaptive K-means clustering.
  • A downstream module analyzes gene signatures for shared components and drug repositioning potential.

Main Results:

  • The framework was validated on 28 transcriptomic datasets, covering 34 disease categories and 386 samples (255 patient, 131 healthy).
  • It processed a total of 194,182 genes, demonstrating scalability and reproducibility.
  • The results underscore the framework's utility in extracting meaningful disease signatures and exploring therapeutic opportunities.

Conclusions:

  • The disint framework offers a standardized, reproducible, and scalable solution for integrating and analyzing large-scale transcriptomic data across diseases.
  • Its validated performance highlights its translational versatility for disease signature analysis and drug repositioning.
  • This toolkit addresses critical challenges in big data integration within biomedical research.