Ivo D. Dinov

The paper introduces a Digital Homecage system for long-term, high-resolution behavioral and physiological monitoring in mice, revealing circadian patterns and enabling longitudinal analysis of chronic conditions.

The paper introduces a machine learning method to detect brain connectomic biomarkers differentiating Alzheimer's disease and mild cognitive impairment using FDG-PET data.

This study quantifies 4D nuclear morphology changes in human astrocytes treated with valproic acid, showing significant size and shape differences by day 7.

This paper introduces an improved version of the CBDA algorithm for handling high-dimensional multisource datasets, validated on synthetic and real-world health data.

The paper develops a 3D morphometric analysis pipeline for cell nuclei and nucleoli, achieving high classification accuracy in prostate cancer and fibroblast cells.

The paper compares nuclear structure classification methods using 2D microscopy data from a 3D dataset, evaluating classification algorithms on raw intensity images and binary masks.

The paper introduces a 3D cell nuclear morphology dataset and evaluates classification algorithms using voxel-based morphometric measures.

The paper observes rotational 3D mechanogenomic Turing patterns in Caco-2 cells during differentiation, suggesting a link between actomyosin dynamics and gene morphogen-driven morphogenesis.

BDQC identifies data outliers and anomalies in large-scale genomic pipelines without domain knowledge, improving data quality and enabling new insights.