ArticlePloS one2013
Surprisal analysis of transcripts expression levels in the presence of noise: a reliable determination of the onset of a tumor phenotype.
Article in PloS one, 2013. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers.
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Who cites it
13 citing papers in PubMed, 22 citations in OpenAlex.
- Signalling Entropy Across Measurement Scales: A Compositional Dilution Lemma and Cross-Modality Invariance for Information-Theoretic Analysis of Cancer Transcriptomes.Entropy (Basel, Switzerland) · 2026Article
- SCA: recovering single-cell heterogeneity through information-based dimensionality reduction.Genome biology · 2023Article
- Fame through surprise: How fame-seeking mass shooters diversify their attacks.Proceedings of the National Academy of Sciences of the United States of America · 2023Article
- Overcoming resistance to BRAFNPJ precision oncology · 2021Article
- TMEA: A Thermodynamically Motivated Framework for Functional Characterization of Biological Responses to System Acclimation.Entropy (Basel, Switzerland) · 2020Article
- Thermodynamic energetics underlying genomic instability and whole-genome doubling in cancer.Proceedings of the National Academy of Sciences of the United States of America · 2020Article
- Exploring Alzheimer's Disease Molecular Variability via Calculation of Personalized Transcriptional Signatures.Biomolecules · 2020Article
- Metabolic, Physiological, and Transcriptomics Analysis of Batch Cultures of the Green MicroalgaCells · 2019Article
- Intracellular redox potential is correlated with miRNA expression in MCF7 cells under hypoxic conditions.Proceedings of the National Academy of Sciences of the United States of America · 2019Article
- Personalized disease signatures through information-theoretic compaction of big cancer data.Proceedings of the National Academy of Sciences of the United States of America · 2018Article
- Statistical thermodynamics of transcription profiles in normal development and tumorigeneses in cohorts of patients.European biophysics journal : EBJ · 2015Article
- Surprisal analysis characterizes the free energy time course of cancer cells undergoing epithelial-to-mesenchymal transition.Proceedings of the National Academy of Sciences of the United States of America · 2014Article
- miRNA and mRNA cancer signatures determined by analysis of expression levels in large cohorts of patients.Proceedings of the National Academy of Sciences of the United States of America · 2013Article
Corrections and comments
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Authors and funding
2 authors at 1 institution in 2 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Towards a reliable identification of the onset in time of a cancer phenotype, changes in transcription levels in cell models were tested. Surprisal analysis, an information-theoretic approach grounded in thermodynamics, was used to characterize the expression level of mRNAs as time changed. Surprisal Analysis provides a very compact representation for the measured expression levels of many thousands of mRNAs in terms of very few - three, four - transcription patterns. The patterns, that are a collection of transcripts that respond together, can be assigned definite biological phenotypic role. We identify a transcription pattern that is a clear marker of eventual malignancy. The weight of each transcription pattern is determined by surprisal analysis. The weight of this pattern changes with time; it is never strictly zero but it is very low at early times and then rises rather suddenly. We suggest that the low weights at early time points are primarily due to experimental noise. We develop the necessary formalism to determine at what point in time the value of that pattern becomes reliable. Beyond the point in time when a pattern is deemed reliable the data shows that the pattern remain reliable. We suggest that this allows a determination of the presence of a cancer forewarning. We apply the same formalism to the weight of the transcription patterns that account for healthy cell pathways, such as apoptosis, that need to be switched off in cancer cells. We show that their weight eventually falls below the threshold. Lastly we discuss patient heterogeneity as an additional source of fluctuation and show how to incorporate it within the developed formalism.
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