Elena Tsiporkova

Fusing time series expression data through hybrid aggregation and hierarchical merge

SUMMARY A novel integration approach targeting the combination of multi-experiment time series expression data is proposed. A recursive hybrid aggregation algorithm is initially employed to extract a set of genes, which are eventually of interest for the biological phenomenon under study. Next, a hierarchical merge procedure is specifically developed for the purpose of fusing together the multiple-experiment expression pro.les of the selected genes. This employs dynamic time warping alignment techniques in order to account adequately for the potential phase shift between the different experiments. We subsequently demonstrate that the resulting gene expression pro.les consistently re.ect the behavior of the original expression pro.les in the different experiments. SUPPLEMENTARY INFORMATION Supplementary data are available athttp://www.tu-plovdiv.bg/Container/bi/DataIntegration/

A formal concept analysis approach to consensus clustering of multi-experiment expression data

BackgroundPresently, with the increasing number and complexity of available gene expression datasets, the combination of data from multiple microarray studies addressing a similar biological question is gaining importance. The analysis and integration of multiple datasets are expected to yield more reliable and robust results since they are based on a larger number of samples and the effects of the individual study-specific biases are diminished. This is supported by recent studies suggesting that important biological signals are often preserved or enhanced by multiple experiments. An approach to combining data from different experiments is the aggregation of their clusterings into a consensus or representative clustering solution which increases the confidence in the common features of all the datasets and reveals the important differences among them.ResultsWe propose a novel generic consensus clustering technique that applies Formal Concept Analysis (FCA) approach for the consolidation and analysis of clustering solutions derived from several microarray datasets. These datasets are initially divided into groups of related experiments with respect to a predefined criterion. Subsequently, a consensus clustering algorithm is applied to each group resulting in a clustering solution per group.These solutions are pooled together and further analysed by employing FCA which allows extracting valuable insights from the data and generating a gene partition over all the experiments. In order to validate the FCA-enhanced approach two consensus clustering algorithms are adapted to incorporate the FCA analysis. Their performance is evaluated on gene expression data from multi-experiment study examining the global cell-cycle control of fission yeast. The FCA results derived from both methods demonstrate that, although both algorithms optimize different clustering characteristics, FCA is able to overcome and diminish these differences and preserve some relevant biological signals.ConclusionsThe proposed FCA-enhanced consensus clustering technique is a general approach to the combination of clustering algorithms with FCA for deriving clustering solutions from multiple gene expression matrices. The experimental results presented herein demonstrate that it is a robust data integration technique able to produce good quality clustering solution that is representative for the whole set of expression matrices.

A Multi-purpose Time Series Data Standardization Method

This work proposes a novel multi-purpose data standardization method inspired by gene-centric clustering approaches. The clustering is performed via template matching of expression profiles employing Dynamic Time Warping (DTW) alignment algorithm to measure the similarity between the profiles. In this way, for each gene profile a cluster consisting of a varying number of neighboring gene profiles (determined by the degree of similarity) is identified to be used in the subsequent standardization phase. The standardized profiles are extracted via a recursive aggregation algorithm, which reduces each cluster of neighboring expression profiles to a singe profile. The proposed data standardization method is validated on gene expression time series data coming from a study examining the global cell-cycle control of gene expression in fission yeast Schizosaccharomyces pombe.

Analysis of Multiple DNA Microarray Datasets

In contrast to conventional clustering algorithms, where a single dataset is used to produce a clustering solution, we introduce herein a MapReduce approach for clustering of datasets generated in multiple-experiment settings. It is inspired by the map-reduce functions commonly used in functional programming and consists of two distinctive phases. Initially, the selected clustering algorithm is applied (mapped) to each experiment separately. This produces a list of different clustering solutions, one per experiment. These are further transformed (reduced) by portioning the cluster centers into a single clustering solution. The obtained partition is not disjoint in terms of the different participating genes, and it is further analyzed and refined by applying formal concept analysis.

Tool support for technology scouting using online sources

This paper describes a prototype of a software tool implementing an entity resolution method for topic-centered expert identification based on bottom-up mining of online sources. The tool extracts and unifies information extracted from a variety of online sources and subsequently builds a repository of user profiles to be used for technology scouting purposes.

A Collaborative Decision Support Platform for Product Release Definition

In this paper, we propose a collaborative decision support platform that supports the product manager in defining the contents of a product release. The platform allows interactive and collaborative decision making by facilitating the exchange of information about product features among individual autonomous stakeholders, providing reputation-enhanced collaboration, ensuring a positive collaboration atmosphere by avoiding public stakeholder ratings and reconciling individual goals with group decisions.

A semantic model of events for integrating photovoltaic monitoring data

Solar plants typically consist of several thousands of passive photovoltaic modules that are connected via thousands of string boxes to hundreds of inverters. In addition, a solar plant has meteo-sensors, power meters and control switches. All these components continuously generate data that is collected by monitoring systems or SCADA systems on-site. From there onwards, this data is pushed to remote analysis servers. The optimal exploitation of this data is hampered by a lack of harmonisation and standardisation in the photovoltaic domain. The data-generating components originate from several different manufacturers, models and versions, and their output is thus not easily commensurable. Crucial for this paper is the fact that conceptually identical failure events are not logged with the same identifying labels. Therefore, every analysis of monitoring system data coming from photovoltaic plants needs an initial integration step to resolve this labeling issue. Our proposal is to facilitate the integration with semantic modelling by means of creating a photovoltaic event ontology with an SWRL reasoning layer.

Semantic-Aware Expert Partitioning

In this paper, we present a novel semantic-aware clustering approach for partitioning of experts represented by lists of keywords. A common set of all different keywords is initially formed by pooling all the keywords of all the expert profiles. The semantic distance between each pair of keywords is then calculated and the keywords are partitioned by using a clustering algorithm. Each expert is further represented by a vector of membership degrees of the expert to the different clusters of keywords. The Euclidean distance between each pair of vectors is finally calculated and the experts are clustered by applying a suitable partitioning algorithm.

An integrative clustering approach combining particle swarm optimization and formal concept analysis

In this article we propose an integrative clustering approach for analysis of gene expression data across multiple experiments, based on Particle Swarm Optimization (PSO) and Formal Concept Analysis (FCA). In the proposed algorithm, the available microarray experiments are initially divided into groups of related datasets with respect to a predefined criterion. Subsequently, a hybrid clustering algorithm, based on PSO and k-means clustering, is applied to each group of experiments separately. This produces a list of different clustering solutions, one per each group. These clustering solutions are pooled together and further analyzed by employing FCA which allows to extract valuable insights from the data and generate a gene partition over the whole set of experiments. The performance of the proposed clustering algorithm is evaluated on time series expression data obtained from a study examining the global cell-cycle control of gene expression in fission yeast Schizosaccharomyces pombe. The obtained experimental results demonstrate that the proposed integrative algorithm allows to generate a unique and robust gene partition over several different microarray datasets.

Data-driven Relevancy Estimation for Event Logs Exploration and Preprocessing.

With the realization of the industrial IoT, more and more industrial assets are continuously monitored by loggers that report events (states, warnings and failures) occurring in or around these devices. Unfortunately, the amount of events in these event logs prevent an efficient exploration, visualization and advanced exploitation of this data. Therefore, a method that could estimate the relevancy of an event is crucial. In this paper, we propose 10 methods, inspired from various research fields, to estimate event relevancy. These methods have been benchmarked on two industrial datasets composed of event logs from two photovoltaic plants. We have demonstrated that a combination of methods can detect irrelevant events (which can correspond to up to 90% of the data). Hence, this is a promising preprocessing step that can help domain experts to explore the logs in a more efficient way and can optimize the performance of analytical methods by reducing the training dataset size without losing information.