Simcluster: clustering enumeration gene expression data on the simplex space

Abstract Background Transcript enumeration methods such as SAGE, MPSS, and sequencing-by-synthesis EST "digital northern", are important high-throughput techniques for digital gene expression measurement. As other counting or voting processes, these measurements constitute compositional data exhibiting properties particular to the simplex space where the summation of the components is constrained. These properties are not present on regular Euclidean spaces, on which hybridization-based microarray data is often modeled. Therefore, pattern recognition methods commonly used for microarray data analysis may be non-informative for the data generated by transcript enumeration techniques since they ignore certain fundamental properties of this space. Results Here we present a software tool, Simcluster, designed to perform clustering analysis for data on the simplex space. We present Simcluster as a stand-alone command-line C package and as a user-friendly on-line tool. Both versions are available at: http://xerad.systemsbiology.net/simcluster. Conclusion Simcluster is designed in accordance with a well-established mathematical framework for compositional data analysis, which provides principled procedures for dealing with the simplex space, and is thus applicable in a number of contexts, including enumeration-based gene expression data.

Supervised classification of basaltic aggregate particles based on texture properties

The strength and durability of materials produced from aggregates (e.g., concrete bricks, concrete, and ballast) are critically affected by the weathering of the particles, which is closely related to their mineral composition. It is possible to infer the degree of weathering from visual features derived from the surface of the aggregates. By using sound pattern recognition methods, this study shows that the characterization of the visual texture of particles, performed by using texture-related features of gray scale images, allows the effective differentiation between weathered and nonweathered aggregates. The selection of the most discriminative features is also performed by taking into account a feature ranking method. The evaluation of the methodology in the presence of noise suggests that it can be used in stone quarries for automatic detection of weathered materials.

Detection of protein fold similarity based on correlation of amino acid properties

An increasing number of proteins with weak sequence similarity have been found to assume similar three-dimensional fold and often have similar or related biochemical or biophysical functions. We propose a method for detecting the fold similarity between two proteins with low sequence similarity based on their amino acid properties alone. The method, the proximity correlation matrix (PCM) method, is built on the observation that the physical properties of neighboring amino acid residues in sequence at structurally equivalent positions of two proteins of similar fold are often correlated even when amino acid sequences are different. The hydrophobicity is shown to be the most strongly correlated property for all protein fold classes. The PCM method was tested on 420 proteins belonging to 64 different known folds, each having at least three proteins with little sequence similarity. The method was able to detect fold similarities for 40% of the 420 sequences. Compared with sequence comparison and several fold-recognition methods, the method demonstrates good performance in detecting fold similarities among the proteins with low sequence identity. Applied to the complete genome of Methanococcus jannaschii, the method recognized the folds for 22 hypothetical proteins.

The SBASE protein domain library, release 8.0: a collection of annotated protein sequence segments

SBASE 8.0 is the eighth release of the SBASE library of protein domain sequences that contains 294 898 annotated structural, functional, ligand-binding and topogenic segments of proteins, cross-referenced to most major sequence databases and sequence pattern collections. The entries are clustered into over 2005 statistically validated domain groups (SBASE-A) and 595 non-validated groups (SBASE-B), provided with several WWW-based search and browsing facilities for online use. A domain-search facility was developed, based on non-parametric pattern recognition methods, including artificial neural networks. SBASE 8.0 is freely available by anonymous ‘ftp’ file transfer from ftp.icgeb.trieste.it. Automated searching of SBASE can be carried out with the WWW servers http://www.icgeb.trieste.it/sbase/ and http://sbase.abc.hu/sbase/.

Prototype learning methods for online handwriting recognition

In this paper, we study different methods for prototype selection for recognizing handwritten characters of Tamil script. In the first method, cumulative pairwise- distances of the training samples of a given class are used to select prototypes. In the second method, cumulative distance to allographs of different orientation is used as a criterion to decide if the sample is representative of the group. The latter method is presumed to offset the possible orientation effect. This method still uses fixed number of prototypes for each of the classes. Finally, a prototype set growing algorithm is proposed, with a view to better model the differences in complexity of different character classes. The proposed algorithms are tested and compared for both writer independent and writer adaptation scenarios.

A detailed investigation of classification methods for vowel speech imagery recognition

Accurate and fast decoding of speech imagery from electroencephalographic (EEG) data could serve as a basis for a new generation of brain computer interfaces (BCIs), more portable and easier to use. However, decoding of speech imagery from EEG is a hard problem due to many factors. In this paper we focus on the analysis of the classification step of speech imagery decoding for a three-class vowel speech imagery recognition problem. We empirically show that different classification subtasks may require different classifiers for accurately decoding and obtain a classification accuracy that improves the best results previously published. We further investigate the relationship between the classifiers and different sets of features selected by the common spatial patterns method. Our results indicate that further improvement on BCIs based on speech imagery could be achieved by carefully selecting an appropriate combination of classifiers for the subtasks involved.

Indexing Methods for Efficient Multiclass Recognition

Many real world image analysis problems, such as face recognition and hand pose estimation, involve recognizing a large number of classes of objects or shapes. Large margin methods, such as AdaBoost and Support Vector Machines (SVMs), often provide competitive accuracy rates, but at the cost of evaluating a large number of binary classifiers, thus making it difficult to apply such methods when thousands or millions of classes need to be recognized. This thesis proposes a filter-and-refine framework, whereby, given a test pattern, a small number of candidate classes can be identified efficiently at the filter step, and computationally expensive large margin classifiers are used to evaluate these candidates at the refine step. Two different filtering methods are proposed, ClassMap and OVA-VS (One-vs.-All classification using Vector Search). ClassMap is an embedding-based method, works for both boosted classifiers and SVMs, and tends to map the patterns and their associated classes close to each other in a vector space. OVA-VS maps OVA classifiers and test patterns to vectors based on the weights and outputs of weak classifiers of the boosting scheme. At runtime, finding the strongest-responding OVA classifier becomes a classical vector search problem, where well-known methods can be used to gain efficiency. In our experiments, the proposed methods achieve significant speed-ups, in some cases up to two orders of magnitude, compared to exhaustive evaluation of all OVA classifiers. This was achieved in hand pose recognition and face recognition systems where the number of classes ranges from 535 to 48,600.

Evaluation of Speaker Normalization Methods for Vowel Recognition Using Fuzzy ARTMAP and K-NN

A procedure that uses fuzzy ARTMAP and K-Nearest Neighbor (K-NN) categorizers to evaluate intrinsic and extrinsic speaker normalization methods is described. Each classifier is trained on preprocessed, or normalized, vowel tokens from about 30% of the speakers of the Peterson-Barney database, then tested on data from the remaining speakers. Intrinsic normalization methods included one nonscaled, four psychophysical scales (bark, bark with end-correction, mel, ERB), and three log scales, each tested on four different combinations of the fundamental (Fo) and the formants (F1 , F2, F3). For each scale and frequency combination, four extrinsic speaker adaptation schemes were tested: centroid subtraction across all frequencies (CS), centroid subtraction for each frequency (CSi), linear scale (LS), and linear transformation (LT). A total of 32 intrinsic and 128 extrinsic methods were thus compared. Fuzzy ARTMAP and K-NN showed similar trends, with K-NN performing somewhat better and fuzzy ARTMAP requiring about 1/10 as much memory. The optimal intrinsic normalization method was bark scale, or bark with end-correction, using the differences between all frequencies (Diff All). The order of performance for the extrinsic methods was LT, CSi, LS, and CS, with fuzzy AHTMAP performing best using bark scale with Diff All; and K-NN choosing psychophysical measures for all except CSi.

Silica bonded stationary phases: Innovative functionalization methods and novel chromatographic separations based on molecular recognition

The research work included in this thesis examines the synthesis, characterization and chromatographic evaluation of novel bonded silica stationary phases. Innovative methods of preparation of silica hydride intermediates and octadecylsilica using a “green chemistry” approach eliminate the use of toxic organic solvents and exploit the solvating power and enhanced diffusivity of supercritical carbon dioxide to produce phases with a surface coverage of bonded ligands which is comparable to, or exceeds, that achieved using traditional organic solvent-based methods. A new stationary phase is also discussed which displays chromatographic selectivity based on molecular recognition. Chapter 1 introduces the chemistry of silica stationary phases, the retention mechanisms and theories on which reversed-phase liquid chromatography and hydrophilic interaction chromatograpy are based, the art and science of achieving a well packed liquid chromatography column, the properties of supercritical carbon dioxide and molecular recognition chemistry. Chapter 2 compares the properties of silica hydride materials prepared using supercritical carbon dioxide as the reaction medium with those synthesized in an organic solvent. A higher coverage of hydride groups on the silica surface is seen when a monofunctional silane is reacted in supercritical carbon dioxide while trifunctional silanes result in a phase which exhibits different properties depending on the reaction medium used. The differing chromatographic behaviour of these silica hydride materials prepared using supercritical carbon dioxide and using organic solvent are explored in chapter 3. Chapter 4 focusses on the preparation of octadecylsilica using mono-, di- and trifunctional alkoxysilanes in supercritical carbon dioxide and in anhydrous toluene. The surface coverage of octadecyl groups, as calculated using thermogravimetric analysis and elemental analysis, is highest when a trifunctional alkoxysilane is reacted with silica in supercritical carbon dioxide. A novel silica stationary phase is discussed in chapter 5 which displays selectivity for analytes based on their hydrogen bonding capabilities. The phase is also highly selective for barbituric acid and may have a future application in the solid phase extraction of barbiturates from biological samples.

Gender comparisons in non-acute cardiac symptom recognition and subsequent help-seeking decisions: a mixed methods study protocol

Introduction: Coronary heart disease (CHD) is one of the leading causes of death in both men and women worldwide. Despite the common misconception that CHD is a ‘man's disease’, it is now well accepted that women endure worse clinical outcomes than men following CHD-related events. A number of studies have explored whether or not gender differences exist in patients presenting with CHD, and specifically whether women delay seeking help for cardiac conditions. UK and overseas studies on help-seeking for emergency cardiac events are contradictory, yet suggest that women often delay help-seeking. In addition, no studies have looked at presumed cardiac symptoms outside an emergency situation. Given the lack of understanding in this area, an explorative qualitative study on the gender differences in help-seeking for a non-emergency cardiac events is needed. Methods and analysis: A purposive sample of 20–30 participants of different ethnic backgrounds and ages attending a rapid access chest pain clinic will be recruited to achieve saturation. Semistructured interviews focusing on help-seeking decision-making for apparent cardiac symptoms will be undertaken. Interview data will be analysed thematically using qualitative software (NVivo) to understand any similarities and differences between the way men and women construct help-seeking. Findings will also be used to inform the preliminary development of a cardiac help-seeking intentions questionnaire. Ethics and dissemination: Ethical approvals were sought and granted. Namely, the University of Westminster (sponsor) and St Georges NHS Trust REC, and the Trust Research and Development Office granted approval to host the study on the Queen Mary's Roehampton site. The study is low risk, with interviews being conducted on hospital premises during working hours. Investigators will disseminate findings via presentations and publications. Participants will receive a written summary of the key findings.