Cancer classification using entropy analysis in fractional Fourier domain of gene expression profile

Guo, Zhipeng; Yi, Xin; Zhao, Yizhang

doi:10.1080/13102818.2017.1413596

Cited by 5 publications

(2 citation statements)

References 20 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In most implementations, this is achieved prior to the classification, through dimensionality reduction approaches driven by the evaluation of entropy in order to reduce feature redundancy [8][9][10]. However, there is a recently introduced approach [11] in which entropy is used to directly weight and rank FRFT coefficients, upon which further clustering approach is based.…”

Section: Entropy In Biostatisticsmentioning

confidence: 99%

Entropic Ranks: A Methodology for Enhanced, Threshold-Free, Information-Rich Data Partition and Interpretation

Lastic¹,

Liampa²,

Georgakilas³

et al. 2020

Preprint

View full text Add to dashboard Cite

Background: Traditional omic analysis relies on p-value and fold change as selection criteria. There is an ongoing debate on their effectiveness in delivering systemic and robust interpretation, due to their dependence on assumptions of conformity with various parametric distributions.Here, we propose a threshold-free selection method based on robust, non-parametric statistics, ensuring independence from the statistical distribution properties and broad applicability. Such methods could adapt to different initial data distributions, contrary to statistical techniques based on fixed thresholds. Methods: Our work extends the Rank Products methodology with a neutral selection method of high information-extraction capacity. We introduce the calculation of the RP distribution’s entropy to isolate the features of interest by their contribution to the distribution’s information content. The aim is a methodology performing threshold-free identification of the differentially expressed features, which are highly informative about the phenomenon under scrutiny. Conclusions: Applying the proposed method on microarray (transcriptomic and DNA methylation) and RNAseq count data of varying sizes and noise presence, we observe robust convergence for the different parameterisations to stable cutoff points. Functional analysis through BioInfoMiner and EnrichR was used to evaluate the information potency of the resulting feature lists. Overall, the derived functional terms provide a systemic description highly compatible with the results of traditional statistical hypothesis testing techniques. The methodology behaves consistently across different data types. The feature lists are compact and information-rich, indicating phenotypic aspects specific to the tissue and biological phenomenon i nvestigated. Selection by information content measures efficiently addresses problems, emerging from arbitrary thresholding, thus facilitating the full automation of the analysis.

show abstract

Section: Entropy In Biostatisticsmentioning

confidence: 99%

Entropic Ranks: A Methodology for Enhanced, Threshold-Free, Information-Rich Data Partition and Interpretation

Lastic¹,

Liampa²,

Georgakilas³

et al. 2020

Preprint

View full text Add to dashboard Cite

show abstract

“…In the last few years, entropy analysis has been shown to be an effective mechanism to assist doctors in medical problems [ 13 ]. For instance, the analysis of brain images can help the detection of some brain diseases [ 14 , 15 ].…”

Section: Introductionmentioning

confidence: 99%

Heartbeats Do Not Make Good Pseudo-Random Number Generators: An Analysis of the Randomness of Inter-Pulse Intervals

Ortiz-Martin

Picazo-Sanchez

Peris-López

et al. 2018

Entropy

View full text Add to dashboard Cite

Abstract:The proliferation of wearable and implantable medical devices has given rise to an interest in developing security schemes suitable for these systems and the environment in which they operate. One area that has received much attention lately is the use of (human) biological signals as the basis for biometric authentication, identification and the generation of cryptographic keys. The heart signal (e.g., as recorded in an electrocardiogram) has been used by several researchers in the last few years. Specifically, the so-called Inter-Pulse Intervals (IPIs), which is the time between two consecutive heartbeats, have been repeatedly pointed out as a potentially good source of entropy and are at the core of various recent authentication protocols. In this work, we report the results of a large-scale statistical study to determine whether such an assumption is (or not) upheld. For this, we have analyzed 19 public datasets of heart signals from the Physionet repository, spanning electrocardiograms from 1353 subjects sampled at different frequencies and with lengths that vary between a few minutes and several hours. We believe this is the largest dataset on this topic analyzed in the literature. We have then applied a standard battery of randomness tests to the extracted IPIs. Under the algorithms described in this paper and after analyzing these 19 public ECG datasets, our results raise doubts about the use of IPI values as a good source of randomness for cryptographic purposes. This has repercussions both in the security of some of the protocols proposed up to now and also in the design of future IPI-based schemes.

show abstract

The fractional Fourier transform as a biomedical signal and image processing tool: A review

Gómez-Echavarría

Ugarte

Tobón

2020

Biocybernetics and Biomedical Engineering

View full text Add to dashboard Cite

Cancer classification using entropy analysis in fractional Fourier domain of gene expression profile

Cited by 5 publications

References 20 publications

Entropic Ranks: A Methodology for Enhanced, Threshold-Free, Information-Rich Data Partition and Interpretation

Entropic Ranks: A Methodology for Enhanced, Threshold-Free, Information-Rich Data Partition and Interpretation

Heartbeats Do Not Make Good Pseudo-Random Number Generators: An Analysis of the Randomness of Inter-Pulse Intervals

The fractional Fourier transform as a biomedical signal and image processing tool: A review

Contact Info

Product

Resources

About