ReviewSLAS discovery : advancing life sciences R & D2017
Biologically Relevant Heterogeneity: Metrics and Practical Insights.
Review in SLAS discovery : advancing life sciences R & D, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 48 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
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.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
48 citing papers in PubMed.
- Reverse bioengineering of the liver: developmental principles for next-generation liver-on-a-chip systems.Communications biology · 2026Review
- Compact Tabletop Magnetic Resonance Elastography for Mapping Soft Tissue Viscoelasticity.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2026Article
- A multilevel hierarchical framework for quantification of experimental heterogeneity in population snapshot data.PLoS computational biology · 2026Article
- Spatial Porosity as a Diagnostic Predictor of Conductivity Collapse in Patient-Specific Radiofrequency Ablation of Liver Tumors.Diagnostics (Basel, Switzerland) · 2026Article
- Microfluidics-enabled proteomic profiling reveal iron-driven immune evasion by an antimicrobial-resistant pathogen.Cell reports methods · 2026Article
- Article
- Single-cell hit calling in high-content imaging screens with Buscar.bioRxiv : the preprint server for biology · 2026Article
- Pseudogene Coexpression Networks Reveal a Robust Prognostic Signature for Pediatric B-ALL Survival.Cancer research communications · 2026Article
- Liquid Biopsy-Based Biomolecular Alterations for the Diagnosis of Triple-Negative Breast Cancer in Adults: A Scoping Review.Diagnostics (Basel, Switzerland) · 2026Review
- Non-Coding RNA Biomarkers in Prostate Cancer: Evidence Mapping and In Silico Characterization.Life (Basel, Switzerland) · 2026Article
- Integrated Patient Digital and Biomimetic Twins for Precision Medicine: A Perspective.Seminars in liver disease · 2025Review
- The Concept of the Optimal Bioscaffold: Parameters, Problems, and Their Resolution Through Additive Manufacturing.Biomedicines · 2025Review
- Validation of microphysiological systems for interpreting patient heterogeneity requires robust reproducibility analytics and experimental metadata.Cell reports methods · 2025Review
- Applications of Artificial Intelligence, Deep Learning, and Machine Learning to Support the Analysis of Microscopic Images of Cells and Tissues.Journal of imaging · 2025Review
- ESPClust: unsupervised identification of modifiers for the effect size profile in omics association studies.Bioinformatics (Oxford, England) · 2025Article
- Mesoscale heterogeneity is a critical determinant for spiral pattern formation in developing social amoeba.Scientific reports · 2025Article
- The effects of cryopreservation on PBMCs transcriptome profile.Frontiers in immunology · 2025Article
- Cancer phenomics research hotspots and development trends: a bibliometric analysis from 2000 to 2023.Discover oncology · 2024Article
- EpiCHAOS: a metric to quantify epigenomic heterogeneity in single-cell data.Genome biology · 2024Article
- SPACe: an open-source, single-cell analysis of Cell Painting data.Nature communications · 2024Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
9 authors.
Funding
Abstract
Heterogeneity is a fundamental property of biological systems at all scales that must be addressed in a wide range of biomedical applications, including basic biomedical research, drug discovery, diagnostics, and the implementation of precision medicine. There are a number of published approaches to characterizing heterogeneity in cells in vitro and in tissue sections. However, there are no generally accepted approaches for the detection and quantitation of heterogeneity that can be applied in a relatively high-throughput workflow. This review and perspective emphasizes the experimental methods that capture multiplexed cell-level data, as well as the need for standard metrics of the spatial, temporal, and population components of heterogeneity. A recommendation is made for the adoption of a set of three heterogeneity indices that can be implemented in any high-throughput workflow to optimize the decision-making process. In addition, a pairwise mutual information method is suggested as an approach to characterizing the spatial features of heterogeneity, especially in tissue-based imaging. Furthermore, metrics for temporal heterogeneity are in the early stages of development. Example studies indicate that the analysis of functional phenotypic heterogeneity can be exploited to guide decisions in the interpretation of biomedical experiments, drug discovery, diagnostics, and the design of optimal therapeutic strategies for individual patients.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.