As the availability of big biomedical data advances, there is a growing need of university students trained professionally on analyzing these data and correctly interpreting their results. We propose here a study plan for a master's degree course on biomedical data science, by describing our experience during the last academic year. In our university course, we explained how to find an open biomedical dataset, how to correctly clean it and how to prepare it for a computational statistics or machine learning phase. By doing so, we introduce common health data science terms and explained how to avoid common mistakes in the process. Moreover, we clarified how to perform an exploratory data analysis (EDA) and how to reasonably interpret its results. We also described how to properly execute a supervised or unsupervised machine learning analysis, and now to understand and interpret its outcomes. Eventually, we explained how to validate the findings obtained. We illustrated all these steps in the context of open science principles, by suggesting to the students to use only open source programming languages (R or Python in particular), open biomedical data (if available), and open access scientific articles (if possible). We believe our teaching proposal can be useful and of interest for anyone wanting to start to prepare a course on biomedical data science.

Evolutionary algorithms are effective techniques for optimizing non-linear and complex high-dimensional problems. However, most of them require a precise fine-tuning of their functioning settings to achieve satisfactory results. In this work, we propose a modified version of an evolutionary approach called the Evolutionary Algorithm for COmplex-process oPtimization (EACOP), designed to have a limited number of hyper-parameters. The base version of EACOP (bEACOP) combines different strategies, including the scatter search methodology, local searches, and a novel combination method based on path relinking to balance the exploration and exploitation phases. Our improved version (iEACOP) intensifies the exploration phase to escape from suboptimal search space areas where, on the contrary, bEACOP gets stuck. Our results show that iEACOP outperforms bEACOP on 27 out of 29 CEC 2017 test suite benchmark functions, exhibiting comparable performance against the three best algorithms of the CEC 2017 competition on single-objective bound-constrained real-parameter numerical optimization. The source code of bEACOP and iEACOP will be made publicly available on GitHub upon acceptance.

Multi-objective optimization algorithms might struggle in finding optimal dominating solutions, especially in real-case scenarios where problems are generally characterized by non-separability, non-differentiability, and multi-modality issues. An effective strategy that already showed to improve the outcome of optimization algorithms consists in manipulating the search space, in order to explore its most promising areas. In this work, starting from a Pareto front identified by an optimization strategy, we exploit Local Bubble Dilation Functions (LBDFs) to manipulate a locally bounded region of the search space containing non-dominated solutions. We tested our approach on the benchmark functions included in the DTLZ and WFG suites, showing that the Pareto front obtained after the application of LBDFs is most of the time characterized by an increased hyper-volume value. Our results confirm that LBDFs are an effective means to identify additional non-dominated solutions that can improve the quality of the Pareto front.

Global optimization problems are among the most complex and widespread tasks in Computer Science. The capability of finding the global optimum is often hindered by many features-e.g., multi-modality, noisiness and non-differentiability- of the fitness landscape related to the problem. To overcome such issues, Dilation Functions (DFs) can be used to perform problem-dependent manipulations of the fitness landscape to “expand” promising regions and “compress” less promising regions. Since in many real-world scenarios the knowledge of the problem characteristics to handcraft tailored DFs is lacking, two automatic approaches to evolve ad-hoc DFs have been proposed and assessed on benchmark problems. One approach is a two-layered method that leverages an Evolution Strategies (ES) and a self-tuning variant of Particle Swarm optimization to evolve a DF. The other approach uses Genetic Programming (GP) to evolve a set of tailored DFs for each dimension of the search space. In this work, we introduce Evolutionary LBDFs (EvLBDFs), a novel approach based on ES to evolve Local Bubble Dilation Functions, a family of DFs that locally dilate hyper-spherical bounded regions of the search space. Moreover, we compare these approaches to solve the Parameter Estimation (PE) problems of two bio-chemical systems. Our results highlight that all three approaches evolved DFs that simplified the PE landscapes. The GP-based approach outperformed the other approaches on the PE problem with the higher number of kinetic parameters to infer.

pyFUME is a python package for the automatic estimation of fuzzy inference systems. Fuzzy models are considered among the most interpretable, understandable, and transparent methods that are currently available, making them ideal for the development of Interpretable AI systems. Such models are suitable for the creation of decision support systems in extremely sensitive domains where the right to an explanation is particularly important, like medicine and healthcare. pyFUME can automatically estimate the antecedent sets and the consequent parameters of a Takagi-Sugeno fuzzy model directly from data, and deliver an executable fuzzy model implemented with the Simpful python library. The main limitation of pyFUME was that it was not well-equipped to deal with purely categorical, non-ordinal variables since it used distance metrics suitable for continuous variables to cluster the data for determining the fuzzy model’s structure. In this paper, we introduce a new version of pyFUME that supports mixed (i.e., continuous and categorical) data sets, relying on a novel version of fuzzy Cprototypes clustering. Our results show that our new approach is effective, leading to better fitting with respect to models based only on continuous features. We also present alternative plotting methods tailored for categorical variables, which improves the overall interpretability of the estimated discrete fuzzy sets.

Solving optimization problems is one of the most complex and widespread task in Computer Science. In many scenarios, finding the global optimum of a function is hampered by several features that characterize the fitness landscapes, such as noisiness, multi-modality, non-convexity, non-separability, and non-differentiability. In order to facilitate the optimization process, a variety of methods have been proposed to manipulate either the search space or the fitness landscape. Among these, Dilation Functions (DFs) were introduced to expand regions of the search space that are characterized by promising fitness values. In this work, we extend the family of DFs by introducing Local Bubble Dilation Functions (LBDFs), a novel approach that generates local distortions bounded by hyper-spheres. By performing an appropriate mapping of the search space, LBDFs can improve the optimization performance, since they expand and reveal the promising regions around the global optimum, while leaving the rest of the fitness landscape untouched. The additional advantage of LBDFs, with respect to DFs, is that different dilations can be applied to each dimension of the search space, which is useful in the case of asymmetric landscapes. In order to show the benefits of local dilations, we executed several tests on the Michalewicz benchmark function, with different settings for the LBDFs. Our results show that a properly designed LBDF can lead to statistically significant better results than using vanilla optimization. Finally, we investigated the use of LBDFs to facilitate the solution of the parameter estimation problem in Systems Biology by analyzing the landscape related to a stochastic model of enzyme kinetics.

BACKGROUND: Genome-wide reconstructions of metabolism opened the way to thorough investigations of cell metabolism for health care and industrial purposes. However, the predictions offered by Flux Balance Analysis (FBA) can be strongly affected by the choice of flux boundaries, with particular regard to the flux of reactions that sink nutrients into the system. To mitigate possible errors introduced by a poor selection of such boundaries, a rational approach suggests to focus the modeling efforts on the pivotal ones. METHODS: In this work, we present a methodology for the automatic identification of the key fluxes in genome-wide constraint-based models, by means of variance-based sensitivity analysis. The goal is to identify the parameters for which a small perturbation entails a large variation of the model outcomes, also referred to as sensitive parameters. Due to the high number of FBA simulations that are necessary to assess sensitivity coefficients on genome-wide models, our method exploits a master-slave methodology that distributes the computation on massively multi-core architectures. We performed the following steps: (1) we determined the putative parameterizations of the genome-wide metabolic constraint-based model, using Saltelli's method; (2) we applied FBA to each parameterized model, distributing the massive amount of calculations over multiple nodes by means of MPI; (3) we then recollected and exploited the results of all FBA runs to assess a global sensitivity analysis. RESULTS: We show a proof-of-concept of our approach on latest genome-wide reconstructions of human metabolism Recon2.2 and Recon3D. We report that most sensitive parameters are mainly associated with the intake of essential amino acids in Recon2.2, whereas in Recon 3D they are associated largely with phospholipids. We also illustrate that in most cases there is a significant contribution of higher order effects. CONCLUSION: Our results indicate that interaction effects between different model parameters exist, which should be taken into account especially at the stage of calibration of genome-wide models, supporting the importance of a global strategy of sensitivity analysis.