ArticlePloS one2012
Coupled analysis of in vitro and histology tissue samples to quantify structure-function relationship.
Article in PloS one, 2012. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
12 citing papers in PubMed.
- Data fusion by T3-PCA: A global model for the simultaneous analysis of coupled three-way and two-way real-valued data.The British journal of mathematical and statistical psychology · 2025Article
- Digital image analysis and machine learning-assisted prediction of neoadjuvant chemotherapy response in triple-negative breast cancer.Breast cancer research : BCR · 2024Article
- Nuclei Detection for 3D Microscopy With a Fully Convolutional Regression Network.IEEE access : practical innovations, open solutions · 2021Article
- Navigating the Functional Landscape of Transcription Factors via Non-Negative Tensor Factorization Analysis of MEDLINE Abstracts.Frontiers in bioengineering and biotechnology · 2017Article
- Turbo-SMT: Parallel Coupled Sparse Matrix-Tensor Factorizations and Applications.Statistical analysis and data mining · 2016Article
- PHENOTYPIC CHARACTERIZATION OF BREAST INVASIVE CARCINOMA VIA TRANSFERABLE TISSUE MORPHOMETRIC PATTERNS LEARNED FROM GLIOBLASTOMA MULTIFORME.Proceedings. IEEE International Symposium on Biomedical Imaging · 2016Article
- Stacked Predictive Sparse Decomposition for Classification of Histology Sections.International journal of computer vision · 2015Article
- Structure-revealing data fusion.BMC bioinformatics · 2014Article
- Invariant delineation of nuclear architecture in glioblastoma multiforme for clinical and molecular association.IEEE transactions on medical imaging · 2013Article
- Article
- Stacked Predictive Sparse Coding for Classification of Distinct Regions of Tumor Histopathology.Proceedings. IEEE International Conference on Computer Vision · 2013Article
- Turbo-SMT: Accelerating Coupled Sparse Matrix-Tensor Factorizations by 200×.Proceedings of the ... SIAM International Conference on Data Mining. SIAM International Conference on Data MiningArticle
Corrections and comments
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Authors and funding
3 authors.
Funding
Abstract
The structure/function relationship is fundamental to our understanding of biological systems at all levels, and drives most, if not all, techniques for detecting, diagnosing, and treating disease. However, at the tissue level of biological complexity we encounter a gap in the structure/function relationship: having accumulated an extraordinary amount of detailed information about biological tissues at the cellular and subcellular level, we cannot assemble it in a way that explains the correspondingly complex biological functions these structures perform. To help close this information gap we define here several quantitative temperospatial features that link tissue structure to its corresponding biological function. Both histological images of human tissue samples and fluorescence images of three-dimensional cultures of human cells are used to compare the accuracy of in vitro culture models with their corresponding human tissues. To the best of our knowledge, there is no prior work on a quantitative comparison of histology and in vitro samples. Features are calculated from graph theoretical representations of tissue structures and the data are analyzed in the form of matrices and higher-order tensors using matrix and tensor factorization methods, with a goal of differentiating between cancerous and healthy states of brain, breast, and bone tissues. We also show that our techniques can differentiate between the structural organization of native tissues and their corresponding in vitro engineered cell culture models.
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Registered trials
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