Batch effect removal methods for microarray gene expression data integration: a survey.

Genomic data integration is a key goal to be achieved towards large-scale genomic data analysis. This process is very challenging due to the diverse sources of information resulting from genomics experiments. In this work, we review methods designed to combine genomic data recorded from microarray gene expression (MAGE) experiments. It has been acknowledged that the main source of variation between different MAGE datasets is due to the so-called 'batch effects'. The methods reviewed here perform data integration by removing (or more precisely attempting to remove) the unwanted variation associated with batch effects. They are presented in a unified framework together with a wide range of evaluation tools, which are mandatory in assessing the efficiency and the quality of the data integration process. We provide a systematic description of the MAGE data integration methodology together with some basic recommendation to help the users in choosing the appropriate tools to integrate MAGE data for large-scale analysis; and also how to evaluate them from different perspectives in order to quantify their efficiency. All genomic data used in this study for illustration purposes were retrieved from InSilicoDB http://insilico.ulb.ac.be.

Machine learning-based atrial fibrillation detection and onset prediction using QT-dynamicity.

Objective. This study examines the value of ventricular repolarization using QT dynamicity for two different types of atrial fibrillation (AF) prediction.Approach. We studied the importance of QT-dynamicity (1) in the detection and (2) the onset prediction (i.e. forecasting) of paroxysmal AF episodes using gradient-boosted decision trees (GBDT), an interpretable machine learning technique. We labeled 176 paroxysmal AF onsets from 88 patients in our unselected Holter recordings database containing paroxysmal AF episodes. Raw ECG signals were delineated using a wavelet-based signal processing technique. A total of 44 ECG features related to interval and wave durations and amplitude were selected and the GBDT model was trained with a Bayesian hyperparameters selection for various windows. The dataset was split into two parts at the patient level, meaning that the recordings from each patient were only present in either the train or test set, but not both. We used 80% on the database for the training and the remaining 20% for the test of the trained model. The model was evaluated using 5-fold cross-validation.Main results.The mean age of the patients was 75.9 ± 11.9 (range 50-99), the number of episodes per patient was 2.3 ± 2.2 (range 1-11), and CHA2DS2-VASc score was 2.9 ± 1.7 (range 1-9). For the detection of AF, we obtained an area under the receiver operating curve (AUROC) of 0.99 (CI 95% 0.98-0.99) and an accuracy of 95% using a 30 s window. Features related to RR intervals were the most influential, followed by those on QT intervals. For the AF onset forecast, we obtained an AUROC of 0.739 (0.712-0.766) and an accuracy of 74% using a 120s window. R wave amplitude and QT dynamicity as assessed by Spearman's correlation of the QT-RR slope were the best predictors.Significance. The QT dynamicity can be used to accurately predict the onset of AF episodes. Ventricular repolarization, as assessed by QT dynamicity, adds information that allows for better short time prediction of AF onset, compared to relying only on RR intervals and heart rate variability. Communication between the ventricles and atria is mediated by the autonomic nervous system (ANS). The variations in intraventricular conduction and ventricular repolarization changes resulting from the influence of the ANS play a role in the initiation of AF.

Autonomic nervous system assessment using heart rate variability.

The role of the autonomic nervous system in the onset of supraventricular and ventricular arrhythmias is well established. It can be analysed by the spontaneous behaviour of the heart rate with ambulatory ECG recordings, through heart rate variability measurements. Input of heart rate variability parameters into artificial intelligence models to make predictions regarding the detection or forecast of rhythm disorders is becoming routine and neuromodulation techniques are now increasingly used for their treatment. All this warrants a reappraisal of the use of heart rate variability for autonomic nervous system assessment.Measurements performed over long periods such as 24H-variance, total power, deceleration capacity, and turbulence are suitable for estimating the individual basal autonomic status. Spectral measurements performed over short periods provide information on the dynamics of systems that disrupt this basal balance and may be part of the triggers of arrhythmias, as well as premature atrial or ventricular beats. All heart rate variability measurements essentially reflect the modulations of the parasympathetic nervous system which are superimposed on the impulses of the adrenergic system. Although heart rate variability parameters have been shown to be useful for risk stratification in patients with myocardial infarction and patients with heart failure, they are not part of the criteria for prophylactic implantation of an intracardiac defibrillator, because of their high variability and the improved treatment of myocardial infarction. Graphical methods such as Poincaré plots allow quick screening of atrial fibrillation and are set to play an important role in the e-cardiology networks. Although mathematical and computational techniques allow manipulation of the ECG signal to extract information and permit their use in predictive models for individual cardiac risk stratification, their explicability remains difficult and making inferences about the activity of the ANS from these models must remain cautious.

IRIDIA-AF, a large paroxysmal atrial fibrillation long-term electrocardiogram monitoring database.

Atrial fibrillation (AF) is the most common sustained heart arrhythmia in adults. Holter monitoring, a long-term 2-lead electrocardiogram (ECG), is a key tool available to cardiologists for AF diagnosis. Machine learning (ML) and deep learning (DL) models have shown great capacity to automatically detect AF in ECG and their use as medical decision support tool is growing. Training these models rely on a few open and annotated databases. We present a new Holter monitoring database from patients with paroxysmal AF with 167 records from 152 patients, acquired from an outpatient cardiology clinic from 2006 to 2017 in Belgium. AF episodes were manually annotated and reviewed by an expert cardiologist and a specialist cardiac nurse. Records last from 19 hours up to 95 hours, divided into 24-hour files. In total, it represents 24 million seconds of annotated Holter monitoring, sampled at 200 Hz. This dataset aims at expanding the available options for researchers and offers a valuable resource for advancing ML and DL use in the field of cardiac arrhythmia diagnosis.

Unlocking the potential of publicly available microarray data using inSilicoDb and inSilicoMerging R/Bioconductor packages.

BACKGROUND: With an abundant amount of microarray gene expression data sets available through public repositories, new possibilities lie in combining multiple existing data sets. In this new context, analysis itself is no longer the problem, but retrieving and consistently integrating all this data before delivering it to the wide variety of existing analysis tools becomes the new bottleneck. RESULTS: We present the newly released inSilicoMerging R/Bioconductor package which, together with the earlier released inSilicoDb R/Bioconductor package, allows consistent retrieval, integration and analysis of publicly available microarray gene expression data sets. Inside the inSilicoMerging package a set of five visual and six quantitative validation measures are available as well. CONCLUSIONS: By providing (i) access to uniformly curated and preprocessed data, (ii) a collection of techniques to remove the batch effects between data sets from different sources, and (iii) several validation tools enabling the inspection of the integration process, these packages enable researchers to fully explore the potential of combining gene expression data for downstream analysis. The power of using both packages is demonstrated by programmatically retrieving and integrating gene expression studies from the InSilico DB repository [https://insilicodb.org/app/].

inSilicoDb: an R/Bioconductor package for accessing human Affymetrix expert-curated datasets from GEO.

Microarray technology has become an integral part of biomedical research and increasing amounts of datasets become available through public repositories. However, re-use of these datasets is severely hindered by unstructured, missing or incorrect biological samples information; as well as the wide variety of preprocessing methods in use. The inSilicoDb R/Bioconductor package is a command-line front-end to the InSilico DB, a web-based database currently containing 86 104 expert-curated human Affymetrix expression profiles compiled from 1937 GEO repository series. The use of this package builds on the Bioconductor project's focus on reproducibility by enabling a clear workflow in which not only analysis, but also the retrieval of verified data is supported.

Role of the autonomic nervous system and premature atrial contractions in short-term paroxysmal atrial fibrillation forecasting: Insights from machine learning models.

BACKGROUND: Machine learning and deep learning techniques are now used extensively for atrial fibrillation (AF) screening, but their use for AF crisis forecasting has yet to be assessed in a clinical context. AIMS: To assess the value of two machine learning algorithms for the short-term prediction of paroxysmal AF episodes. METHODS: We conducted a retrospective study from an outpatient clinic. We developed a deep neural network model that was trained for a supervised binary classification, differentiating between RR interval variations that precede AF onset and RR interval variations far from any AF. We also developed a random forest model to obtain forecast results using heart rate variability variables, with and without premature atrial complexes. RESULTS: In total, 10,484 Holter electrocardiogram recordings were screened, and 250 analysable AF onsets were labelled. The deep neural network model was able to distinguish if a given RR interval window would lead to AF onset in the next 30 beats with a sensitivity of 80.1% (95% confidence interval 78.7-81.6) at the price of a low specificity of 52.8% (95% confidence interval 51.0-54.6). The random forest model indicated that the main factor that precedes the start of a paroxysmal AF episode is autonomic nervous system activity, and that premature complexes add limited additional information. In addition, the onset of AF episodes is preceded by cyclical fluctuations in the low frequency/high frequency ratio of heart rate variability. Each peak is itself followed by an increase in atrial extrasystoles. CONCLUSIONS: The use of two machine learning algorithms for the short-term prediction of AF episodes allowed us to confirm that the main cause of AF crises lies in an imbalance in the autonomic nervous system, and not premature atrial contractions, which are, however, required as a final firing trigger.

Autocatalyses.

Autocatalysis is a fundamental concept, used in a wide range of domains. From the most general definition of autocatalysis, that is, a process in which a chemical compound is able to catalyze its own formation, several different systems can be described. We detail the different categories of autocatalyses and compare them on the basis of their mechanistic, kinetic, and dynamic properties. It is shown how autocatalytic patterns can be generated by different systems of chemical reactions. With the notion of autocatalysis covering a large variety of mechanistic realizations with very similar behaviors, it is proposed that the key signature of autocatalysis is its kinetic pattern expressed in a mathematical form.

Automatic configuration of the Cassandra database using irace.

Database systems play a central role in modern data-centered applications. Their performance is thus a key factor in the efficiency of data processing pipelines. Modern database systems expose several parameters that users and database administrators can configure to tailor the database settings to the specific application considered. While this task has traditionally been performed manually, in the last years several methods have been proposed to automatically find the best parameter configuration for a database. Many of these methods, however, use statistical models that require high amounts of data and fail to represent all the factors that impact the performance of a database, or implement complex algorithmic solutions. In this work we study the potential of a simple model-free general-purpose configuration tool to automatically find the best parameter configuration of a database. We use the irace configurator to automatically find the best parameter configuration for the Cassandra NoSQL database using the YCBS benchmark under different scenarios. We establish a reliable experimental setup and obtain speedups of up to 30% over the default configuration in terms of throughput, and we provide an analysis of the configurations obtained.

Software replica of minimal living processes.

There is a long tradition of software simulations in theoretical biology to complement pure analytical mathematics which are often limited to reproduce and understand the self-organization phenomena resulting from the non-linear and spatially grounded interactions of the huge number of diverse biological objects. Since John Von Neumann and Alan Turing pioneering works on self-replication and morphogenesis, proponents of artificial life have chosen to resolutely neglecting a lot of materialistic and quantitative information deemed not indispensable and have focused on the rule-based mechanisms making life possible, supposedly neutral with respect to their underlying material embodiment. Minimal life begins at the intersection of a series of processes which need to be isolated, differentiated and duplicated as such in computers. Only software developments and running make possible to understand the way these processes are intimately interconnected in order for life to appear at the crossroad. In this paper, I will attempt to set out the history of life as the disciples of artificial life understand it, by placing these different lessons on a temporal and causal axis, showing which one is indispensable to the appearance of the next and how does it connect to the next. I will discuss the task of artificial life as setting up experimental software platforms where these different lessons, whether taken in isolation or together, are tested, simulated, and, more systematically, analyzed. I will sketch some of these existing software platforms: chemical reaction networks, Varela's autopoietic cellular automata, Ganti's chemoton model, whose running delivers interesting take home messages to open-minded biologists.

Energetic and entropic analysis of mirror symmetry breaking processes in a recycled microreversible chemical system.

Understanding how biological homochirality emerged remains a challenge for the researchers interested in the origin of life. During the last decades, stable nonracemic steady states of nonequilibrium chemical systems have been discussed as a possible response to this problem. In line with this framework, a description of recycled systems was provided in which stable products can be activated back to reactive compounds. The dynamical behavior of such systems relies on the presence of a source of energy, leading to the continuous maintaining of unidirectional reaction loops. A full thermodynamic study of recycled systems, composed of microreversible reactions only, is presented here, showing how the energy is transferred and distributed through the system, leading to cycle competitions and the stabilization of asymmetric states.

Streamlining pre- and intra-hospital care for patients with severe trauma: a white paper from the European Critical Care Foundation.

PURPOSE: Major trauma remains a significant cause of morbidity and mortality in the developed and developing world. In 2013, nearly 5 million people worldwide died from their injuries, and almost 1 billion individuals sustained injuries that warranted some type of healthcare, accounting for around 10% of the global burden of disease in general. Behind the statistics, severe trauma takes a major toll on individuals, their families and healthcare systems. Management of the patient with severe trauma requires multiple interventions in a highly time-sensitive context and fragmentation of care, characterised by loss of information and time among disciplines, departments and individuals, both outside the hospital and within it, is frequent. Outcomes may be improved by better streamlining of pre- and intra-hospital care. METHODS: We describe the basis for development of a multi-stakeholder consortium by the European Critical Care Foundation working closely with a number of European Scientific Societies to address and overcome problems of fragmentation in the care of patients with severe trauma. RESULT: The consortium will develop and introduce an information management system adapted to severe trauma, which will integrate continuous monitoring of vital parameters and point-of-care diagnostics. The key innovation of the project is to harness the power of information technologies and artificial intelligence to provide computer-enhanced clinical evaluation and decision-support to streamline the multiple points at which information and time are potentially lost. CONCLUSIONS: The severe trauma management platform thus created could have multiple benefits beyond its immediate use in managing the care of injured patients.

MetaClustering: discovery of the different sample clusterings in gene expression data.

Clustering of the samples is a standard procedure for the analysis of gene expression data, for instance to discover cancer subtypes. However, more than one biologically meaningful clustering can exist, depending on the genes chosen. We propose here to group the genes in function of the clustering of the samples they fit. This allows to determine directly the different clusterings of the samples present in the data. As a clustering is a structure, genes belonging to the same group are functions of the same structure. Hence, the determination of groups of genes which support the same clustering could also be viewed as the detection of non-linearly linked genes. MetaClustering was applied successfully to simulated data. It also recovered the known clustering of real cancer data, which was impossible using the complete set of genes. Finally, it clustered together cell-cycle genes, showing its ability to group genes related in a non-linear way.

Genealogical Trees of Scientific Papers.

Many results have been obtained when studying scientific papers citations databases in a network perspective. Articles can be ranked according to their current in-degree and their future popularity or citation counts can even be predicted. The dynamical properties of such networks and the observation of the time evolution of their nodes started more recently. This work adopts an evolutionary perspective and proposes an original algorithm for the construction of genealogical trees of scientific papers on the basis of their citation count evolution in time. The fitness of a paper now amounts to its in-degree growing trend and a "dying" paper will suddenly see this trend declining in time. It will give birth and be taken over by some of its most prevalent citing "offspring". Practically, this might be used to trace the successive published milestones of a research field.

Topology analysis of social networks extracted from literature.

In a world where complex networks are an increasingly important part of science, it is interesting to question how the new reading of social realities they provide applies to our cultural background and in particular, popular culture. Are authors of successful novels able to reproduce social networks faithful to the ones found in reality? Is there any common trend connecting an author's oeuvre, or a genre of fiction? Such an analysis could provide new insight on how we, as a culture, perceive human interactions and consume media. The purpose of the work presented in this paper is to define the signature of a novel's story based on the topological analysis of its social network of characters. For this purpose, an automated tool was built that analyses the dialogs in novels, identifies characters and computes their relationships in a time-dependent manner in order to assess the network's evolution over the course of the story.

Integration and cross-validation of high-throughput gene expression data: comparing heterogeneous data sets.

Data analysis--not data production--is becoming the bottleneck in gene expression research. Data integration is necessary to cope with an ever increasing amount of data, to cross-validate noisy data sets, and to gain broad interdisciplinary views of large biological data sets. New Internet resources may help researchers to combine data sets across different gene expression platforms. However, noise and disparities in experimental protocols strongly limit data integration. A detailed review of four selected studies reveals how some of these limitations may be circumvented and illustrates what can be achieved through data integration.

The connections between the frustrated chaos and the intermittency chaos in small Hopfield networks.

In a previous paper we introduced the notion of frustrated chaos occurring in Hopfield networks [Neural Networks 11 (1998) 1017]. It is a dynamical regime which appears in a network when the global structure is such that local connectivity patterns responsible for stable oscillatory behaviors are intertwined, leading to mutually competing attractors and unpredictable itinerancy among brief appearance of these attractors. Frustration destabilizes the network and provokes an erratic 'wavering' among the orbits that characterize the same network when it is connected in a non-frustrated way. In this paper, through a detailed study of the bifurcation diagram given for some connection weights, we will show that this frustrated chaos belongs to the family of intermittency type of chaos, first described by Berge et al. [Order within chaos (1984)] and Pomeau and Manneville [Commun. Math. Phys. 74 (1980) 189]. Indeed, the transition to chaos is a critical one, and all along the bifurcation diagram, in any chaotic window, the duration of the intermittent cycles, between two chaotic bursts, grows as an invert ratio of the connection weight. Specific to this regime are the intermittent cycles easily identifiable as the non-frustrated regimes obtained by altering the values of these same connection weights. We will more specifically show that anywhere in the bifurcation diagram, a chaotic window always lies between two oscillatory regimes, and that the resulting chaos is a merging of, among others, the cycles at both ends. The strength (i.e. the duration of its oscillatory phase before the chaotic burst) of the first cycle decreases while the regime tends to stabilize into the second cycle (with the strength of this second cycle increasing) that will finally get the control. Since in our study, the bifurcation diagram concerns the same connection weights responsible for the learning mechanism of the Hopfield network, we will discuss the relations existing between bifurcation, learning and control of chaos. We will show that, in some cases, the addition of a slower Hebbian learning mechanism onto the Hopfield networks makes the resulting global dynamics to drive the network into a stable oscillatory regime, through a succession of intermittent and quasiperiodic regimes. Finally, we will present a series of possible logical steps to manually construct a frustrated network.

Dynamical and Mechanistic Reconstructive Approaches of T Lymphocyte Dynamics: Using Visual Modeling Languages to Bridge the Gap between Immunologists, Theoreticians, and Programmers.

Dynamic modeling of lymphocyte behavior has primarily been based on populations based differential equations or on cellular agents moving in space and interacting each other. The final steps of this modeling effort are expressed in a code written in a programing language. On account of the complete lack of standardization of the different steps to proceed, we have to deplore poor communication and sharing between experimentalists, theoreticians and programmers. The adoption of diagrammatic visual computer language should however greatly help the immunologists to better communicate, to more easily identify the models similarities and facilitate the reuse and extension of existing software models. Since immunologists often conceptualize the dynamical evolution of immune systems in terms of "state-transitions" of biological objects, we promote the use of unified modeling language (UML) state-transition diagram. To demonstrate the feasibility of this approach, we present a UML refactoring of two published models on thymocyte differentiation. Originally built with different modeling strategies, a mathematical ordinary differential equation-based model and a cellular automata model, the two models are now in the same visual formalism and can be compared.

Default cascades in complex networks: topology and systemic risk.

The recent crisis has brought to the fore a crucial question that remains still open: what would be the optimal architecture of financial systems? We investigate the stability of several benchmark topologies in a simple default cascading dynamics in bank networks. We analyze the interplay of several crucial drivers, i.e., network topology, banks' capital ratios, market illiquidity, and random vs targeted shocks. We find that, in general, topology matters only--but substantially--when the market is illiquid. No single topology is always superior to others. In particular, scale-free networks can be both more robust and more fragile than homogeneous architectures. This finding has important policy implications. We also apply our methodology to a comprehensive dataset of an interbank market from 1999 to 2011.