Computational analysis of the synergy among multiple interacting genes

Diseases such as cancer are often related to collaborative effects involving interactions of multiple genes within complex pathways, or to combinations of multiple SNPs. To understand the structure of such mechanisms, it is helpful to analyze genes in terms of the purely cooperative, as opposed to independent, nature of their contributions towards a phenotype. Here, we present an information-theoretic analysis that provides a quantitative measure of the multivariate synergy and decomposes sets of genes into submodules each of which contains synergistically interacting genes. When the resulting computational tools are used for the analysis of gene expression or SNP data, this systems-based methodology provides insight into the biological mechanisms responsible for disease

Machining centre performance monitoring with calibrated artefact probing

Maintaining high levels of geometric accuracy in five-axis machining centres is of critical importance to many industries and applications. Numerous methods for error identification have been developed in both the academic and industrial fields; one commonly-applied technique is artefact probing, which can reveal inherent system errors at minimal cost and does not require high skill levels to perform. The primary focus of popular commercial solutions is on confirming machine capability to produce accurate workpieces, with the potential for short-term trend analysis and fault diagnosis through interpretation of the results by an experienced user. This paper considers expanding the artefact probing method into a performance monitoring system, benefitting both the onsite Maintenance Engineer and visiting specialist Engineer with accessibility of information and more effective means to form insight. A technique for constructing a data-driven tolerance threshold is introduced, describing the normal operating condition and helping protect against unwarranted settings induced by human error. A multifunctional graphical element is developed to present the data trends with tolerance threshold integration to maintain relevant performance context, and an automated event detector to highlight areas of interest or concern. The methods were developed on a simulated, demonstration dataset; then applied without modification to three case studies on data acquired from currently operating industrial machining centres to verify the methods. The data-driven tolerance threshold and event detector methods were shown to be effective at their respective tasks, and the merits of the multifunctional graphical display are presented and discussed

Classification of fibroglandular tissue distribution in the breast based on radiotherapy planning CT

Accurate segmentation of breast tissues is required for a number of applications such as model based deformable registration in breast radiotherapy. The accuracy of breast tissue segmentation is affected by the spatial distribution (or pattern) of fibroglandular tissue (FT). The goal of this study was to develop and evaluate texture features, determined from planning computed tomography (CT) data, to classify the spatial distribution of FT in the breas

Using machine learning to speed up manual image annotation: application to a 3D imaging protocol for measuring single cell gene expression in the developing C. elegans embryo

<p>Abstract</p> <p>Background</p> <p>Image analysis is an essential component in many biological experiments that study gene expression, cell cycle progression, and protein localization. A protocol for tracking the expression of individual <it>C. elegans </it>genes was developed that collects image samples of a developing embryo by 3-D time lapse microscopy. In this protocol, a program called StarryNite performs the automatic recognition of fluorescently labeled cells and traces their lineage. However, due to the amount of noise present in the data and due to the challenges introduced by increasing number of cells in later stages of development, this program is not error free. In the current version, the error correction (<it>i.e</it>., editing) is performed manually using a graphical interface tool named AceTree, which is specifically developed for this task. For a single experiment, this manual annotation task takes several hours.</p> <p>Results</p> <p>In this paper, we reduce the time required to correct errors made by StarryNite. We target one of the most frequent error types (movements annotated as divisions) and train a support vector machine (SVM) classifier to decide whether a division call made by StarryNite is correct or not. We show, via cross-validation experiments on several benchmark data sets, that the SVM successfully identifies this type of error significantly. A new version of StarryNite that includes the trained SVM classifier is available at <url>http://starrynite.sourceforge.net</url>.</p> <p>Conclusions</p> <p>We demonstrate the utility of a machine learning approach to error annotation for StarryNite. In the process, we also provide some general methodologies for developing and validating a classifier with respect to a given pattern recognition task.</p

Segmentation of Multi-Isotope Imaging Mass Spectrometry Data for Semi-Automatic Detection of Regions of Interest

Multi-isotope imaging mass spectrometry (MIMS) associates secondary ion mass spectrometry (SIMS) with detection of several atomic masses, the use of stable isotopes as labels, and affiliated quantitative image-analysis software. By associating image and measure, MIMS allows one to obtain quantitative information about biological processes in sub-cellular domains. MIMS can be applied to a wide range of biomedical problems, in particular metabolism and cell fate [1], [2], [3]. In order to obtain morphologically pertinent data from MIMS images, we have to define regions of interest (ROIs). ROIs are drawn by hand, a tedious and time-consuming process. We have developed and successfully applied a support vector machine (SVM) for segmentation of MIMS images that allows fast, semi-automatic boundary detection of regions of interests. Using the SVM, high-quality ROIs (as compared to an expert's manual delineation) were obtained for 2 types of images derived from unrelated data sets. This automation simplifies, accelerates and improves the post-processing analysis of MIMS images. This approach has been integrated into “Open MIMS,” an ImageJ-plugin for comprehensive analysis of MIMS images that is available online at http://www.nrims.hms.harvard.edu/NRIMS_ImageJ.php

Classification of microarray data using gene networks

BACKGROUND: Microarrays have become extremely useful for analysing genetic phenomena, but establishing a relation between microarray analysis results (typically a list of genes) and their biological significance is often difficult. Currently, the standard approach is to map a posteriori the results onto gene networks in order to elucidate the functions perturbed at the level of pathways. However, integrating a priori knowledge of the gene networks could help in the statistical analysis of gene expression data and in their biological interpretation. RESULTS: We propose a method to integrate a priori the knowledge of a gene network in the analysis of gene expression data. The approach is based on the spectral decomposition of gene expression profiles with respect to the eigenfunctions of the graph, resulting in an attenuation of the high-frequency components of the expression profiles with respect to the topology of the graph. We show how to derive unsupervised and supervised classification algorithms of expression profiles, resulting in classifiers with biological relevance. We illustrate the method with the analysis of a set of expression profiles from irradiated and non-irradiated yeast strains. CONCLUSION: Including a priori knowledge of a gene network for the analysis of gene expression data leads to good classification performance and improved interpretability of the results

The Use of Phonetic Motor Invariants Can Improve Automatic Phoneme Discrimination

affiliation: Castellini, C (Reprint Author), Univ Genoa, LIRA Lab, Genoa, Italy. Castellini, Claudio; Metta, Giorgio; Tavella, Michele, Univ Genoa, LIRA Lab, Genoa, Italy. Badino, Leonardo; Metta, Giorgio; Sandini, Giulio; Fadiga, Luciano, Italian Inst Technol, Genoa, Italy. Grimaldi, Mirko, Salento Univ, CRIL, Lecce, Italy. Fadiga, Luciano, Univ Ferrara, DSBTA, I-44100 Ferrara, Italy. article-number: e24055 keywords-plus: SPEECH-PERCEPTION; RECOGNITION research-areas: Science & Technology - Other Topics web-of-science-categories: Multidisciplinary Sciences author-email: [email protected] funding-acknowledgement: European Commission [NEST-5010, FP7-IST-250026] funding-text: The authors acknowledge the support of the European Commission project CONTACT (grant agreement NEST-5010) and SIEMPRE (grant agreement number FP7-IST-250026). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript. number-of-cited-references: 31 times-cited: 0 journal-iso: PLoS One doc-delivery-number: 817OO unique-id: ISI:000294683900024We investigate the use of phonetic motor invariants (MIs), that is, recurring kinematic patterns of the human phonetic articulators, to improve automatic phoneme discrimination. Using a multi-subject database of synchronized speech and lips/tongue trajectories, we first identify MIs commonly associated with bilabial and dental consonants, and use them to simultaneously segment speech and motor signals. We then build a simple neural network-based regression schema (called Audio-Motor Map, AMM) mapping audio features of these segments to the corresponding MIs. Extensive experimental results show that (a) a small set of features extracted from the MIs, as originally gathered from articulatory sensors, are dramatically more effective than a large, state-of-the-art set of audio features, in automatically discriminating bilabials from dentals; (b) the same features, extracted from AMM-reconstructed MIs, are as effective as or better than the audio features, when testing across speakers and coarticulating phonemes; and dramatically better as noise is added to the speech signal. These results seem to support some of the claims of the motor theory of speech perception and add experimental evidence of the actual usefulness of MIs in the more general framework of automated speech recognition

Predicting Human Nucleosome Occupancy from Primary Sequence

Nucleosomes are the fundamental repeating unit of chromatin and comprise the structural building blocks of the living eukaryotic genome. Micrococcal nuclease (MNase) has long been used to delineate nucleosomal organization. Microarray-based nucleosome mapping experiments in yeast chromatin have revealed regularly-spaced translational phasing of nucleosomes. These data have been used to train computational models of sequence-directed nuclesosome positioning, which have identified ubiquitous strong intrinsic nucleosome positioning signals. Here, we successfully apply this approach to nucleosome positioning experiments from human chromatin. The predictions made by the human-trained and yeast-trained models are strongly correlated, suggesting a shared mechanism for sequence-based determination of nucleosome occupancy. In addition, we observed striking complementarity between classifiers trained on experimental data from weakly versus heavily digested MNase samples. In the former case, the resulting model accurately identifies nucleosome-forming sequences; in the latter, the classifier excels at identifying nucleosome-free regions. Using this model we are able to identify several characteristics of nucleosome-forming and nucleosome-disfavoring sequences. First, by combining results from each classifier applied de novo across the human ENCODE regions, the classifier reveals distinct sequence composition and periodicity features of nucleosome-forming and nucleosome-disfavoring sequences. Short runs of dinucleotide repeat appear as a hallmark of nucleosome-disfavoring sequences, while nucleosome-forming sequences contain short periodic runs of GC base pairs. Second, we show that nucleosome phasing is most frequently predicted flanking nucleosome-free regions. The results suggest that the major mechanism of nucleosome positioning in vivo is boundary-event-driven and affirm the classical statistical positioning theory of nucleosome organization