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.

The potential of microarray gene expression (MAGE) data is only partially explored due to the limited number of samples in individual studies. This limitation can be surmounted by merging or integrating data sets originating from independent MAGE experiments, which are designed to study the same biological problem. However, this process is hindered by batch effects that are study-dependent and result in random data distortion; therefore numerical transformations are needed to render the integration of different data sets accurate and meaningful. Our contribution in this paper is two-fold. First we propose GENESHIFT, a new nonparametric batch effect removal method based on two key elements from statistics: empirical density estimation and the inner product as a distance measure between two probability density functions; second we introduce a new validation index of batch effect removal methods based on the observation that samples from two independent studies drawn from a same population should exhibit similar probability density functions. We evaluated and compared the GENESHIFT method with four other state-of-the-art methods for batch effect removal: Batch-mean centering, empirical Bayes or COMBAT, distance-weighted discrimination, and cross-platform normalization. Several validation indices providing complementary information about the efficiency of batch effect removal methods have been employed in our validation framework. The results show that none of the methods clearly outperforms the others. More than that, most of the methods used for comparison perform very well with respect to some validation indices while performing very poor with respect to others. GENESHIFT exhibits robust performances and its average rank is the highest among the average ranks of all methods used for comparison.

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/].

A plenitude of feature selection (FS) methods is available in the literature, most of them rising as a need to analyze data of very high dimension, usually hundreds or thousands of variables. Such data sets are now available in various application areas like combinatorial chemistry, text mining, multivariate imaging, or bioinformatics. As a general accepted rule, these methods are grouped in filters, wrappers, and embedded methods. More recently, a new group of methods has been added in the general framework of FS: ensemble techniques. The focus in this survey is on filter feature selection methods for informative feature discovery in gene expression microarray (GEM) analysis, which is also known as differentially expressed genes (DEGs) discovery, gene prioritization, or biomarker discovery. We present them in a unified framework, using standardized notations in order to reveal their technical details and to highlight their common characteristics as well as their particularities.

Abstract 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. Availability: inSilicoDb is available as part of the Bioconductor project. There is a companion web interface that can be used for browsing available datasets before importing them into R/Bioconductor (http://insilico.ulb.ac.be). Contact: [email protected]

While fixed point dynamics is still the predominant regime used for information processing, recent brain observations and computational results suggest more and more the importance/necessity to include and rely on more complex dynamics. Independently, since their introduction sixty years ago, cell assemblies are still a powerful substrate for brain information processing. Here, the first part of this paper aims to conciliate these two evidences by investigating the possibility to encode content addressable information in pre-encoded cell assemblies characterized by complex dynamics. As an expected outcome, after stimulus offset, the information is maintained in the attractor of the cell assembly. As a less expected outcome, when the system is fed with ambiguous stimuli, it will continuously iterate across the possible attractors (instead to settle down to a specific one). In the second part of the paper, based on biologically plausible mechanisms, a novel unsupervised algorithm for online cell assemblies creation is proposed. The procedure involves simultaneously, a fast hebbian/anti-hebbian learning of the network's recurrent connections for the creation of new cell assemblies, and a slower feedback signal which stabilizes the cell assemblies by learning the feedforward input connections. Results show that the obtained cell assemblies exhibit similar behavior as the pre-encoded ones. Finally, we propose that this model could be working for the cognitive map formation of multiple place fields in the CA3 network when the rat is facing a new environment.

Many cognitive tasks require the ability to maintain and manipulate simultaneously several chunks of information. Numerous neurobiological observations have reported that this ability, known as the working memory, is strongly associated with the activity of the prefrontal cortex. Furthermore, during resting state, the spontaneous activity of the cortex exhibits exquisite spatiotemporal patterns sharing similar features with the ones observed during specific memory tasks. Here, we propose a computational model of the prefrontal cortex within the framework of the cell assembly theory. In that framework, ‘a chunk of information’ refers to an associative memory and consists of an ensemble of neurons which activates coherently due to their strong interconnections. Our model consists of a recurrent network of cells whose dynamics results from the interplay between the membrane potential and the theta local field potential.

How information and more particularly memories are represented in brain dynamics is still an open question. By using, a recurrent network receiving a stimulus dependent external input, the author have demonstrated that the use of limit cycle attractors encompass in many aspects the limitations of fixed points attractors and gives better correspondence with neurophysiological facts. A main outcome of this perspective is the apparition of chaotic trajectories: instead of the overwhelming presence of spurious attractors, chaotic dynamics shows up when facing ambiguous situation. Contrary to intuition, many studies reported that noise can have beneficial effects in dynamical systems. Inline with these studies, it is demonstrated here how stochastic noise can make converge the chaotic trajectories to the expected limit cycle attractors and accordingly can improve consequently the retrieval performance. This noise induced retrieval enhancement is very dependent of the type of chaotic dynamics which is function of how information is coded.

Thirty years ago, hippocampal cells firing at selective location in the environment were discovered, with a major impact in our understanding of memory formation. Recently, "grid cells", firing when the rat is located at any of the vertices of grid fields covering the environment, have been discovered in the medial enthorinal cortex. These cells are supposed to be responsible for the organization of hippocampal place cells. Computational models have already validated this theory. However, multiple grid scales were assumed which does not follow anatomical evidences. We propose here a model relying on the phase precession dynamics: when the rat crosses a field of a particular grid cell, its spikes shift to earlier phases of the theta rhythm. We demonstrate how this dynamics can reliably explain the organization of place cells without relying on multiple grid scales. Moreover, hippocampal place cell phase precession results from grid cell phase precession.

This letter aims at studying the impact of iterative Hebbian learning algorithms on the recurrent neural network's underlying dynamics. First, an iterative supervised learning algorithm is discussed. An essential improvement of this algorithm consists of indexing the attractor information items by means of external stimuli rather than by using only initial conditions, as Hopfield originally proposed. Modifying the stimuli mainly results in a change of the entire internal dynamics, leading to an enlargement of the set of attractors and potential memory bags. The impact of the learning on the network's dynamics is the following: the more information to be stored as limit cycle attractors of the neural network, the more chaos prevails as the background dynamical regime of the network. In fact, the background chaos spreads widely and adopts a very unstructured shape similar to white noise. Next, we introduce a new form of supervised learning that is more plausible from a biological point of view: the network has to learn to react to an external stimulus by cycling through a sequence that is no longer specified a priori. Based on its spontaneous dynamics, the network decides "on its own" the dynamical patterns to be associated with the stimuli. Compared with classical supervised learning, huge enhancements in storing capacity and computational cost have been observed. Moreover, this new form of supervised learning, by being more "respectful" of the network intrinsic dynamics, maintains much more structure in the obtained chaos. It is still possible to observe the traces of the learned attractors in the chaotic regime. This complex but still very informative regime is referred to as "frustrated chaos."

Seminal observations performed by Skarda and Freeman (1987) on the olfactory bulb of rabbits during cognitive tasks have suggested to locate the basal state of behavior in the network's spatio-temporal dynamics. Following these neurophysiological observations, the authors have investigated in previous papers the possibility to store external stimuli in spatio-temporal dynamical attractors of recurrent neural networks. To this aim, an efficient learning algorithm, based on a time asymmetric Hebbian mechanism, has been proposed. The underlying idea is to obtain - as much as possible - a natural i.e. unconstrained mapping between the external stimuli and the spontaneous internal dynamics of the network. The dynamical regime called "frustrated chaos" by the authors appears to play a substantial role in the establishment of this mapping. In this paper, adopting a symbolic coding of the output, new investigations are performed on the presence and the importance of spurious data. It is shown how the presence of chaos contributes to stop their proliferation.

This paper aims at studying the impact of an Hebbian learning algorithm on the recurrent neural network's underlying dynamics. Two different kinds of learning are compared in order to encode information in the attractors of the Hopfield neural net: the storing of static patterns and the storing of cyclic patterns. We show that if the storing of static patterns leads to a reduction of the potential dynamics following the learning phase, the learning of cyclic patterns tends to increase the dimension of the potential attractors instead. In fact, such learning may be used as an extra "route to chaos": the more cycles to be learned, the more the network shows as spontaneous dynamics a form of chaotic itinerancy among brief oscillatory periods. These results are in line with the observations made by Freeman in the olfactory bulb of the rabbit: cycles are used to store information and the chaotic dynamics appears as the background regime composed of those cyclic "memory bags". It confirms precedent papers in which it was observed that huge encoding capacity in term of cyclic attractors implies strong presence of chaos.

The learning impact, of an iterative supervised Hebbian learning algorithm, on a recurrent neural network's underlying dynamics has been discussed in a previous paper. It was argued that these results are in line with the observations made by Freeman in the olfactory bulb of the rabbit: cycles are used to store information and the chaotic dynamics appears as the background regime composed of those cyclic "memory bags". However, to get closer to a biological point of view, this paper introduces an unsupervised version of this Hebbian algorithm. As a direct result, both the storing capacity and the content addressability of the learned networks are greatly enhanced. Furthermore, stunning dynamical results are observed: if the learning process increases the dimension of the potential attractors, however, less chaoticity is found than in a supervised learning process. Moreover, chaos obtained looks more structured, made from brief itinerancy among learned cycles.

So far, recurrent networks, when adopting fixed point dynamics, show a very poor encoding capacity. However, these same networks, when preferentially maintained in chaotic dynamics, can encode an enormous amount of information in their cyclic attractors and this boosts their encoding capacity. It has been described in a previous paper a simple way to encode such information by robustly associating each vector in a N-dimensional space with one "symbolic" cyclic attractor. The main message was the monotonous increase of chaotic spontaneous regimes as a function of the number of attractors to learn. However, no algorithm was provided to adjust the connection's weight in order to encode a given input set. For this purpose, this paper revisits the classical gradient-based BPTT learning algorithm. It shows that this algorithm gives poor results and furthermore that by using it the "chaoticity" of the network dampens strongly, hence it's encoding capacity.

This papers aims at experimentally confirming in very small Hopfield networks that chaos becomes the spontaneous dynamics of a network, when the number of inputs to be learned and stored in its dynamical attractors is superior to the size of this network. The spontaneous dynamics of the network are shown to increase in complexity by increasing the size of the learning set. The type of chaos exploited to code these inputs is related with the frustrated chaos described in previous papers. When, following a brute-force but robust learning of the inputs, the network is presented with a series of unlearned ambiguous input, experimental results show how chaotic regimes become the natural response of the network, reflecting in its frustrated dynamics the inherent ambiguity of the input. A live demonstration can be shown where the rhythms associated with the dynamics can be heard. This experimental work might give additional support to the Skarda and Freeman strong intuition that chaos should play an important role in the storage and the search capacities of our brains.