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Machine Learning and Entropy Based Methods for Biomedical Data Analytics...
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Machine Learning and Entropy Based Methods for Biomedical Data Analytics and Modeling

A special issue of Entropy (ISSN 1099-4300). This special issue belongs to the section "Entropy and Biology".

Deadline for manuscript submissions: closed (30 November 2023) | Viewed by 15652

Special Issue Editor

Prof. Dr. Gastone C. Castellani

E-Mail Website
Guest Editor
Department of Experimental, Diagnostic and Specialty Medicine, University of Bologna, 40138 Bologna, Italy
Interests: artificial intelligence; machine learning; multiomic data; complex systems; neural networks; biophysics
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Biomedical systems are generating a huge variety of multiomics big data, including genomics, proteomics and imaging (radiomics and pathomics). The analysis and modeling of these data require advanced methods, often borrowed from artificial intelligence and statistical learning, ranging from dimensionality reduction to synthetic data generation and stochastic methods.

Biomedical data pose interesting challenges to data analysts and modelers because the data quality and quantity are often limiting factors and the variety of experimental design can be relevant.

Another open problem in this field is how to integrate the huge number of different scales present in these type of data. The involved scales can range from molecules to single cells, tissues, organs, individuals and populations.

This Special Issue aims to be a forum for the presentation of new and improved techniques of machine learning, information theory, and modeling for complex biomedical systems. In particular, the analysis and interpretation of real-world natural and engineered complex systems with the help of statistical tools based on Shannon information theory fall within the scope of this Special Issue.

Prof. Dr. Gastone C. Castellani
Guest Editor

Manuscript Submission Information

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Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Entropy is an international peer-reviewed open access monthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • multiomics
  • dimensionality reduction
  • synthetic data generation
  • complex biomedical systems
  • machine learning

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Published Papers (6 papers)

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Research

14 pages, 757 KiB  
Article
Cell Decision Making through the Lens of Bayesian Learning
by Arnab Barua and Haralampos Hatzikirou
Entropy 2023, 25(4), 609; https://doi.org/10.3390/e25040609 - 3 Apr 2023
Cited by 5 | Viewed by 2979
Abstract
Cell decision making refers to the process by which cells gather information from their local microenvironment and regulate their internal states to create appropriate responses. Microenvironmental cell sensing plays a key role in this process. Our hypothesis is that cell decision-making regulation is [...] Read more.
Cell decision making refers to the process by which cells gather information from their local microenvironment and regulate their internal states to create appropriate responses. Microenvironmental cell sensing plays a key role in this process. Our hypothesis is that cell decision-making regulation is dictated by Bayesian learning. In this article, we explore the implications of this hypothesis for internal state temporal evolution. By using a timescale separation between internal and external variables on the mesoscopic scale, we derive a hierarchical Fokker–Planck equation for cell-microenvironment dynamics. By combining this with the Bayesian learning hypothesis, we find that changes in microenvironmental entropy dominate the cell state probability distribution. Finally, we use these ideas to understand how cell sensing impacts cell decision making. Notably, our formalism allows us to understand cell state dynamics even without exact biochemical information about cell sensing processes by considering a few key parameters. Full article
(This article belongs to the Special Issue Machine Learning and Entropy Based Methods for Biomedical Data Analytics and Modeling)
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23 pages, 2394 KiB  
Article
Random Walk Approximation for Stochastic Processes on Graphs
by Stefano Polizzi, Tommaso Marzi, Tommaso Matteuzzi, Gastone Castellani and Armando Bazzani
Entropy 2023, 25(3), 394; https://doi.org/10.3390/e25030394 - 21 Feb 2023
Cited by 1 | Viewed by 3101
Abstract
We introduce the Random Walk Approximation (RWA), a new method to approximate the stationary solution of master equations describing stochastic processes taking place on graphs. Our approximation can be used for all processes governed by non-linear master equations without long-range interactions and with [...] Read more.
We introduce the Random Walk Approximation (RWA), a new method to approximate the stationary solution of master equations describing stochastic processes taking place on graphs. Our approximation can be used for all processes governed by non-linear master equations without long-range interactions and with a conserved number of entities, which are typical in biological systems, such as gene regulatory or chemical reaction networks, where no exact solution exists. For linear systems, the RWA becomes the exact result obtained from the maximum entropy principle. The RWA allows having a simple analytical, even though approximated, form of the solution, which is global and easier to deal with than the standard System Size Expansion (SSE). Here, we give some theoretically sufficient conditions for the validity of the RWA and estimate the order of error calculated by the approximation with respect to the number of particles. We compare RWA with SSE for two examples, a toy model and the more realistic dual phosphorylation cycle, governed by the same underlying process. Both approximations are compared with the exact integration of the master equation, showing for the RWA good performances of the same order or better than the SSE, even in regions where sufficient conditions are not met. Full article
(This article belongs to the Special Issue Machine Learning and Entropy Based Methods for Biomedical Data Analytics and Modeling)
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23 pages, 5243 KiB  
Article
BRAQUE: Bayesian Reduction for Amplified Quantization in UMAP Embedding
by Lorenzo Dall’Olio, Maddalena Bolognesi, Simone Borghesi, Giorgio Cattoretti and Gastone Castellani
Entropy 2023, 25(2), 354; https://doi.org/10.3390/e25020354 - 14 Feb 2023
Cited by 2 | Viewed by 2512
Abstract
Single-cell biology has revolutionized the way we understand biological processes. In this paper, we provide a more tailored approach to clustering and analyzing spatial single-cell data coming from immunofluorescence imaging techniques. We propose Bayesian Reduction for Amplified Quantization in UMAP Embedding (BRAQUE) as [...] Read more.
Single-cell biology has revolutionized the way we understand biological processes. In this paper, we provide a more tailored approach to clustering and analyzing spatial single-cell data coming from immunofluorescence imaging techniques. We propose Bayesian Reduction for Amplified Quantization in UMAP Embedding (BRAQUE) as an integrative novel approach, from data preprocessing to phenotype classification. BRAQUE starts with an innovative preprocessing, named Lognormal Shrinkage, which is able to enhance input fragmentation by fitting a lognormal mixture model and shrink each component towards its median, in order to help further the clustering step in finding more separated and clear clusters. Then, BRAQUE’s pipeline consists of a dimensionality reduction step performed using UMAP, and a clustering performed using HDBSCAN on UMAP embedding. In the end, clusters are assigned to a cell type by experts, using effects size measures to rank markers and identify characterizing markers (Tier 1), and possibly characterize markers (Tier 2). The number of total cell types in one lymph node detectable with these technologies is unknown and difficult to predict or estimate. Therefore, with BRAQUE, we achieved a higher granularity than other similar algorithms such as PhenoGraph, following the idea that merging similar clusters is easier than splitting unclear ones into clear subclusters. Full article
(This article belongs to the Special Issue Machine Learning and Entropy Based Methods for Biomedical Data Analytics and Modeling)
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13 pages, 549 KiB  
Article
Reconstruction of the Temporal Correlation Network of All-Cause Mortality Fluctuation across Italian Regions: The Importance of Temperature and Among-Nodes Flux
by Guido Gigante and Alessandro Giuliani
Entropy 2023, 25(1), 21; https://doi.org/10.3390/e25010021 - 23 Dec 2022
Cited by 1 | Viewed by 1586
Abstract
All-cause mortality is a very coarse grain, albeit very reliable, index to check the health implications of lifestyle determinants, systemic threats and socio-demographic factors. In this work, we adopt a statistical-mechanics approach to the analysis of temporal fluctuations of all-cause mortality, focusing on [...] Read more.
All-cause mortality is a very coarse grain, albeit very reliable, index to check the health implications of lifestyle determinants, systemic threats and socio-demographic factors. In this work, we adopt a statistical-mechanics approach to the analysis of temporal fluctuations of all-cause mortality, focusing on the correlation structure of this index across different regions of Italy. The correlation network among the 20 Italian regions was reconstructed using temperature oscillations and traveller flux (as a function of distance and region’s attractiveness, based on GDP), allowing for a separation between infective and non-infective death causes. The proposed approach allows monitoring of emerging systemic threats in terms of anomalies of correlation network structure. Full article
(This article belongs to the Special Issue Machine Learning and Entropy Based Methods for Biomedical Data Analytics and Modeling)
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9 pages, 340 KiB  
Article
Slope Entropy Characterisation: The Role of the δ Parameter
by Mahdy Kouka and David Cuesta-Frau
Entropy 2022, 24(10), 1456; https://doi.org/10.3390/e24101456 - 12 Oct 2022
Cited by 6 | Viewed by 1862
Abstract
Many time series entropy calculation methods have been proposed in the last few years. They are mainly used as numerical features for signal classification in any scientific field where data series are involved. We recently proposed a new method, Slope Entropy (SlpEn), based [...] Read more.
Many time series entropy calculation methods have been proposed in the last few years. They are mainly used as numerical features for signal classification in any scientific field where data series are involved. We recently proposed a new method, Slope Entropy (SlpEn), based on the relative frequency of differences between consecutive samples of a time series, thresholded using two input parameters, γ and δ. In principle, δ was proposed to account for differences in the vicinity of the 0 region (namely, ties) and, therefore, was usually set at small values such as 0.001. However, there is no study that really quantifies the role of this parameter using this default or other configurations, despite the good SlpEn results so far. The present paper addresses this issue, removing δ from the SlpEn calculation to assess its real influence on classification performance, or optimising its value by means of a grid search in order to find out if other values beyond the 0.001 value provide significant time series classification accuracy gains. Although the inclusion of this parameter does improve classification accuracy according to experimental results, gains of 5% at most probably do not support the additional effort required. Therefore, SlpEn simplification could be seen as a real alternative. Full article
(This article belongs to the Special Issue Machine Learning and Entropy Based Methods for Biomedical Data Analytics and Modeling)
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12 pages, 2140 KiB  
Article
Effectiveness of Biologically Inspired Neural Network Models in Learning and Patterns Memorization
by Lorenzo Squadrani, Nico Curti, Enrico Giampieri, Daniel Remondini, Brian Blais and Gastone Castellani
Entropy 2022, 24(5), 682; https://doi.org/10.3390/e24050682 - 12 May 2022
Cited by 2 | Viewed by 2474
Abstract
Purpose: In this work, we propose an implementation of the Bienenstock–Cooper–Munro (BCM) model, obtained by a combination of the classical framework and modern deep learning methodologies. The BCM model remains one of the most promising approaches to modeling the synaptic plasticity of neurons, [...] Read more.
Purpose: In this work, we propose an implementation of the Bienenstock–Cooper–Munro (BCM) model, obtained by a combination of the classical framework and modern deep learning methodologies. The BCM model remains one of the most promising approaches to modeling the synaptic plasticity of neurons, but its application has remained mainly confined to neuroscience simulations and few applications in data science. Methods: To improve the convergence efficiency of the BCM model, we combine the original plasticity rule with the optimization tools of modern deep learning. By numerical simulation on standard benchmark datasets, we prove the efficiency of the BCM model in learning, memorization capacity, and feature extraction. Results: In all the numerical simulations, the visualization of neuronal synaptic weights confirms the memorization of human-interpretable subsets of patterns. We numerically prove that the selectivity obtained by BCM neurons is indicative of an internal feature extraction procedure, useful for patterns clustering and classification. The introduction of competitiveness between neurons in the same BCM network allows the network to modulate the memorization capacity of the model and the consequent model selectivity. Conclusions: The proposed improvements make the BCM model a suitable alternative to standard machine learning techniques for both feature selection and classification tasks. Full article
(This article belongs to the Special Issue Machine Learning and Entropy Based Methods for Biomedical Data Analytics and Modeling)
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