Numerical Integration of SDEs: A Short Tutorial

Introduction to numerical methods to simulate systems of stochastic differential equations (SDEs) both in Ito and Stratonovich scheme

GeneNetWeaver: in silico benchmark generation and performance profiling of network inference methods

Motivation: Over the last decade, numerous methods have been developed for inference of regulatory networks from gene expression data. However, accurate and systematic evaluation of these methods is hampered by the difficulty of constructing adequate benchmarks and the lack of tools for a differentiated analysis of network predictions on such benchmarks. Results: Here, we describe a novel and comprehensive method for in silico benchmark generation and performance profiling of network inference methods available to the community as an open-source software called GeneNetWeaver (GNW). In addition to the generation of detailed dynamical models of gene regulatory networks to be used as benchmarks, GNW provides a network motif analysis that reveals systematic prediction errors, thereby indicating potential ways of improving inference methods. The accuracy of network inference methods is evaluated using standard metrics such as precision-recall and receiver operating characteristic curves. We show how GNW can be used to assess the performance and identify the strengths and weaknesses of six inference methods. Furthermore, we used GNW to provide the international Dialogue for Reverse Engineering Assessments and Methods (DREAM) competition with three network inference challenges (DREAM3, DREAM4 and DREAM5). Availability: GNW is available at http://gnw.sourceforge.net along with its Java source code, user manual and supporting data. Supplementary information: Supplementary data are available at Bioinformatics online. Contact: [email protected]

Generating Realistic In Silico Gene Networks for Performance Assessment of Reverse Engineering Methods

Reverse engineering methods are typically first tested on simulated data from in silico networks, for systematic and efficient performance assessment, before an application to real biological networks. In this paper we present a method for generating biologically plausible in silico networks, which allow realistic performance assessment of network inference algorithms. Instead of using random graph models, which are known to only partly capture the structural properties of biological networks, we generate network structures by extracting modules from known biological interaction networks. Using the yeast transcriptional regulatory network as a test case, we show that extracted modules have a biologically plausible connectivity because they preserve functional and structural properties of the original network. Our method was selected to generate the "gold standard" networks for the gene network reverse engineering challenge of the third DREAM conference (Dialogue on Reverse Engineering Assessment and Methods, Cambridge, MA, 2008)

A continuously benchmarked and crowdsourced challenge for rapid development and evaluation of models to predict COVID-19 diagnosis and hospitalization

Importance: Machine learning could be used to predict the likelihood of diagnosis and severity of illness. Lack of COVID-19 patient data has hindered the data science community in developing models to aid in the response to the pandemic. Objectives: To describe the rapid development and evaluation of clinical algorithms to predict COVID-19 diagnosis and hospitalization using patient data by citizen scientists, provide an unbiased assessment of model performance, and benchmark model performance on subgroups. Design, Setting, and Participants: This diagnostic and prognostic study operated a continuous, crowdsourced challenge using a model-to-data approach to securely enable the use of regularly updated COVID-19 patient data from the University of Washington by participants from May 6 to December 23, 2020. A postchallenge analysis was conducted from December 24, 2020, to April 7, 2021, to assess the generalizability of models on the cumulative data set as well as subgroups stratified by age, sex, race, and time of COVID-19 test. By December 23, 2020, this challenge engaged 482 participants from 90 teams and 7 countries. Main Outcomes and Measures: Machine learning algorithms used patient data and output a score that represented the probability of patients receiving a positive COVID-19 test result or being hospitalized within 21 days after receiving a positive COVID-19 test result. Algorithms were evaluated using area under the receiver operating characteristic curve (AUROC) and area under the precision recall curve (AUPRC) scores. Ensemble models aggregating models from the top challenge teams were developed and evaluated. Results: In the analysis using the cumulative data set, the best performance for COVID-19 diagnosis prediction was an AUROC of 0.776 (95% CI, 0.775-0.777) and an AUPRC of 0.297, and for hospitalization prediction, an AUROC of 0.796 (95% CI, 0.794-0.798) and an AUPRC of 0.188. Analysis on top models submitting to the challenge showed consistently better model performance on the female group than the male group. Among all age groups, the best performance was obtained for the 25- to 49-year age group, and the worst performance was obtained for the group aged 17 years or younger. Conclusions and Relevance: In this diagnostic and prognostic study, models submitted by citizen scientists achieved high performance for the prediction of COVID-19 testing and hospitalization outcomes. Evaluation of challenge models on demographic subgroups and prospective data revealed performance discrepancies, providing insights into the potential bias and limitations in the models

From Genes to Organisms:Bioinformatics System Models and Software

The expression of genes is controlled by regulatory networks, which performspecific functions in a cell. Gene networks play a crucial role in the development of multicellular organisms by precisely coordinating spatial and temporal gene expression patterns during different developmental stages. Unravelling and modelling these networks is of key importance to gain eventually a complete understanding of developmental processes and genetically related diseases. In this thesis, we present a comprehensive framework for reverse engineering gene regulatory networks, which required the development of many methods in very diverse research fields. A second important contribution is their implementation as extensible, userfriendly and open source computational toolsa. Over the last decade, numerous methods have been developed for inference of regulatory networks fromgene expression data. However, relatively little effort has been put into evaluating the performance of those methods due to the difficulty of constructing adequate benchmarks and the lack of tools for a differentiated analysis of network predictions on such benchmarks. Here, we describe a novel and comprehensive method for in silico benchmark generation and performance profiling of network inference methods available to the community as an open-source software called GeneNetWeaver (GNW). In addition to the generation of detailed dynamical models of gene regulatory networks to be used as benchmarks, GNWprovides a networkmotif analysis that reveals systematic prediction errors, thereby indicating potential ways of improving inference methods. The accuracy of network inference methods is evaluated using standard metrics such as precision-recall and receiver operating characteristic (ROC) curves. Furthermore, we used GNW to provide the international DREAM (Dialogue for Reverse Engineering Assessments andMethods) competition with three network inference challenges (DREAM3, DREAM4, and DREAM5). In the context of the DREAM competition, 91 teams submitted about 900 network predictions to evaluate the performance of their methods on GNW-generated benchmarks. Today, the accuracy of more than 25,000 gene network reconstructions have been evaluated by GNWusers. Gene regulatory networks are often organized into groups, modules or community of related genes and proteins carrying out specific biological functions. Here, we also address the rational decomposition of (reconstructed) biological networks into function modules. We presentan extensible and modular framework for community structure detection in networks called Jmod. Jmod implements state-of-the-art community structure detection methods including Newman’s spectral algorithm and a genetic algorithm-basedmodularity optimization method that we developed. The performance of these methods has been evaluated on biological and in silico networks. The application of thesemethods is actually not limited to gene regulatory networks as they can also provide insight into the community structure of neural, social, and technological networks, for instance. However, modularity optimization methods are known to be affected by a resolution limit that makes them fail to detect small communities in large networks. Although several attempts have been proposed to overcome this limitation of modularity based methods, none of them solves it in a satisfactorily manner. Therefore, a community voting method was developed and implemented for combining multiple partitions obtained using our GA-based method into one partition more robust and reliable than the individual partitions. We have shown that this approach successfully overcome the resolution limit. Furthermore, our method is best performer along with another method in a comparative analysis that profiled the performance of twelve state-of-the-art community structure detection algorithms. The reconstruction of a developmental gene network in its spatial context remains a considerable challenge. One of the reason is that this process requires tremendous amount of spatial and temporal gene expression data, which are usually available in very limited quantities due to the inherent difficulty in measuring gene expression in an entire organism. Another contribution of this thesis is the development of an image processing application named WingJ for unsupervised and systematic quantification of the developing Drosophila wing, which is a classical model for studying the genetic control of tissue size, shape and patterning. First, a parametric model of the morphology or structure of the Drosophila wing is inferred from fluorescence images. The segmentation method is based on the design ofmultiple image processing detection modules, each focusing on the extraction of a specific feature of the wing structure including its orientation. The approach was later extended to the detection of the Drosophila embryo. The inferred structure model was then used as a convenient coordinate system for measuring gene and protein expression levels. An important feature of the obtained expression maps is that they can be used to compare domains of expression in differentiated systems, for example to visualize the difference in patterns of gene activity between wild type and mutant wings or in wings imaged at different time points during development. Moreover, a robust, multiscale quantitative description of the developing wing is obtained by combining morphological and gene expression information from multiple wings, completed by the output of an automatic cell nuclei detection method that we have developed. We have used the above method to automatically generate robust quantitative descriptions of wild-type and mutant (pent deficient) Drosophila wings imaged at 80, 90, 100, and 110 hours after egg laying. Furthermore, we have shown that these quantitative descriptions can be used to unravel the regulatory interactions of a six-gene wing developmental network

Optimisation d'un moteur synchrone à l'aide des Algorithmes Génétiques

On s'applique ici à étudier et caractériser les Algorithmes Génétiques (AG) qui appartiennent à la famille des algorithmes évolutionnaires. L'objectif de ce projet est de mettre en place un programme permettant d'optimiser un système réel tel qu'un moteur à l'aide des AG, et d'en comparer les résultats avec ceux obtenus avec des techniques d'optimisation dites classiques