Neural plasma

This paper presents a novel type of artificial neural network, called neural plasma, which is tailored for classification tasks involving few observations with a large number of variables. Neural plasma learns to adapt its classification confidence by generating artificial training data as a function of its confidence in previous decisions. In contrast to multilayer perceptrons and similar techniques, which are inspired by topological and operational aspects of biological neural networks, neural plasma is motivated by aspects of high-level behavior and reasoning in the presence of uncertainty. The basic principles of the proposed model apply to other supervised learning algorithms that provide explicit classification confidence values. The empirical evaluation of this new technique is based on benchmarking experiments involving data sets from biotechnology that are characterized by the small-n-large-p problem. The presented study exposes a comprehensive methodology and is seen as a first step in exploring different aspects of this methodology.IFIP International Conference on Artificial Intelligence in Theory and Practice - Neural NetsRed de Universidades con Carreras en Informática (RedUNCI

Instance-based concept learning from multiclass DNA microarray data

BACKGROUND: Various statistical and machine learning methods have been successfully applied to the classification of DNA microarray data. Simple instance-based classifiers such as nearest neighbor (NN) approaches perform remarkably well in comparison to more complex models, and are currently experiencing a renaissance in the analysis of data sets from biology and biotechnology. While binary classification of microarray data has been extensively investigated, studies involving multiclass data are rare. The question remains open whether there exists a significant difference in performance between NN approaches and more complex multiclass methods. Comparative studies in this field commonly assess different models based on their classification accuracy only; however, this approach lacks the rigor needed to draw reliable conclusions and is inadequate for testing the null hypothesis of equal performance. Comparing novel classification models to existing approaches requires focusing on the significance of differences in performance. RESULTS: We investigated the performance of instance-based classifiers, including a NN classifier able to assign a degree of class membership to each sample. This model alleviates a major problem of conventional instance-based learners, namely the lack of confidence values for predictions. The model translates the distances to the nearest neighbors into 'confidence scores'; the higher the confidence score, the closer is the considered instance to a pre-defined class. We applied the models to three real gene expression data sets and compared them with state-of-the-art methods for classifying microarray data of multiple classes, assessing performance using a statistical significance test that took into account the data resampling strategy. Simple NN classifiers performed as well as, or significantly better than, their more intricate competitors. CONCLUSION: Given its highly intuitive underlying principles – simplicity, ease-of-use, and robustness – the k-NN classifier complemented by a suitable distance-weighting regime constitutes an excellent alternative to more complex models for multiclass microarray data sets. Instance-based classifiers using weighted distances are not limited to microarray data sets, but are likely to perform competitively in classifications of high-dimensional biological data sets such as those generated by high-throughput mass spectrometry

Neural plasma

This paper presents a novel type of artificial neural network, called neural plasma, which is tailored for classification tasks involving few observations with a large number of variables. Neural plasma learns to adapt its classification confidence by generating artificial training data as a function of its confidence in previous decisions. In contrast to multilayer perceptrons and similar techniques, which are inspired by topological and operational aspects of biological neural networks, neural plasma is motivated by aspects of high-level behavior and reasoning in the presence of uncertainty. The basic principles of the proposed model apply to other supervised learning algorithms that provide explicit classification confidence values. The empirical evaluation of this new technique is based on benchmarking experiments involving data sets from biotechnology that are characterized by the small-n-large-p problem. The presented study exposes a comprehensive methodology and is seen as a first step in exploring different aspects of this methodology.IFIP International Conference on Artificial Intelligence in Theory and Practice - Neural NetsRed de Universidades con Carreras en Informática (RedUNCI

Low Cost IoT System for Solar Panel Power Monitoring

International audienceIn this work, we will present a low-cost system to monitor energy production from a solar panel. Based on simple devices, this solution made it possible to measure the current, voltage, power, and visualize them through an available and free IoT application called Node-Red. The project can be qualified having plenty of essential purposes : to be used in education field, research and even production monitoring in a photovoltaic system

Computationally efficient induction of classification rules with the PMCRI and J-PMCRI frameworks

In order to gain knowledge from large databases, scalable data mining technologies are needed. Data are captured on a large scale and thus databases are increasing at a fast pace. This leads to the utilisation of parallel computing technologies in order to cope with large amounts of data. In the area of classification rule induction, parallelisation of classification rules has focused on the divide and conquer approach, also known as the Top Down Induction of Decision Trees (TDIDT). An alternative approach to classification rule induction is separate and conquer which has only recently been in the focus of parallelisation. This work introduces and evaluates empirically a framework for the parallel induction of classification rules, generated by members of the Prism family of algorithms. All members of the Prism family of algorithms follow the separate and conquer approach.are increasing at a fast pace. This leads to the utilisation of parallel computing technologies in order to cope with large amounts of data. In the area of classification rule induction, parallelisation of classification rules has focused on the divide and conquer approach, also known as the Top Down Induction of Decision Trees (TDIDT). An alternative approach to classification rule induction is separate and conquer which has only recently been in the focus of parallelisation. This work introduces and evaluates empirically a framework for the parallel induction of classification rules, generated by members of the Prism family of algorithms. All members of the Prism family of algorithms follow the separate and conquer approach

Identifying and validating the presence of Guanine-Quadruplexes (G4) within the blood fluke parasite Schistosoma mansoni

Schistosomiasis is a neglected tropical disease that currently affects over 250 million individuals worldwide. In the absence of an immunoprophylactic vaccine and the recognition that mono-chemotherapeutic control of schistosomiasis by praziquantel has limitations, new strategies for managing disease burden are urgently needed. A better understanding of schistosome biology could identify previously undocumented areas suitable for the development of novel interventions. Here, for the first time, we detail the presence of G-quadruplexes (G4) and putative quadruplex forming sequences (PQS) within the Schistosoma mansoni genome. We find that G4 are present in both intragenic and intergenic regions of the seven autosomes as well as the sex-defining allosome pair. Amongst intragenic regions, G4 are particularly enriched in 3´ UTR regions. Gene Ontology (GO) term analysis evidenced significant G4 enrichment in the wnt signalling pathway (p<0.05) and PQS oligonucleotides synthetically derived from wnt-related genes resolve into parallel and anti-parallel G4 motifs as elucidated by circular dichroism (CD) spectroscopy. Finally, utilising a single chain anti-G4 antibody called BG4, we confirm the in situ presence of G4 within both adult female and male worm nuclei. These results collectively suggest that G4-targeted compounds could be tested as novel anthelmintic agents and highlights the possibility that G4-stabilizing molecules could be progressed as candidates for the treatment of schistosomiasi

Text mining of full-text journal articles combined with gene expression analysis reveals a relationship between sphingosine-1-phosphate and invasiveness of a glioblastoma cell line

BACKGROUND: Sphingosine 1-phosphate (S1P), a lysophospholipid, is involved in various cellular processes such as migration, proliferation, and survival. To date, the impact of S1P on human glioblastoma is not fully understood. Particularly, the concerted role played by matrix metalloproteinases (MMP) and S1P in aggressive tumor behavior and angiogenesis remains to be elucidated. RESULTS: To gain new insights in the effect of S1P on angiogenesis and invasion of this type of malignant tumor, we used microarrays to investigate the gene expression in glioblastoma as a response to S1P administration in vitro. We compared the expression profiles for the same cell lines under the influence of epidermal growth factor (EGF), an important growth factor. We found a set of 72 genes that are significantly differentially expressed as a unique response to S1P. Based on the result of mining full-text articles from 20 scientific journals in the field of cancer research published over a period of five years, we inferred gene-gene interaction networks for these 72 differentially expressed genes. Among the generated networks, we identified a particularly interesting one. It describes a cascading event, triggered by S1P, leading to the transactivation of MMP-9 via neuregulin-1 (NRG-1), vascular endothelial growth factor (VEGF), and the urokinase-type plasminogen activator (uPA). This interaction network has the potential to shed new light on our understanding of the role played by MMP-9 in invasive glioblastomas. CONCLUSION: Automated extraction of information from biological literature promises to play an increasingly important role in biological knowledge discovery. This is particularly true for high-throughput approaches, such as microarrays, and for combining and integrating data from different sources. Text mining may hold the key to unraveling previously unknown relationships between biological entities and could develop into an indispensable instrument in the process of formulating novel and potentially promising hypotheses