Unsupervised Learning with Normalised Data and Non-Euclidean Norms

Peer reviewe

Division of Giardia isolates from humans into two genetically distinct assemblages by electrophoretic analysis of enzyme encoded at 27 loci in comparison with Giardia muris

Giardia that infect humans are known to be heterogeneous but they are assigned currently to a single species, Giardia intestinalis (syn. G. lamblia). The genetic differences that exist within G. intestinalis have not yet been assessed quantitatively and neither have they been compared in magnitude with those that exist between G. intestinalis and species that are morphologically similar (G. duodenalis) or morphologically distinct (e.g. G. muris). In this study, 60 Australian isolates of G. intestinalis were analysed electrophoretically at 27 enzyme loci and compared with G. muris and a feline isolate of G. duodenalis. Isolates of G. intestinalis were distinct genetically from both G. muris (approximately 80% fixed allelic differences) and the feline G. duodenalis isolate (approximately 75% fixed allelic differences). The G. intestinalis isolates were extremely heterogeneous but they fell into 2 major genetic assemblages, separated by fixed allelic differences at approximately 60% of loci examined. The magnitude of the genetic differences between the G. intestinalis assemblages approached the level that distinguished the G. duodenalis isolate from the morphologically distinct G. muris. This raises important questions about the evolutionary relationships of the assemblages with Homo sapiens, the possibility of ancient or contemporary transmission from animal hosts to humans and the biogeographical origins of the two clusters.G. Mayrhofer, R. H. Andrews, P. L. Ey and N. B. Chilto

Impact of Unexpected Events, Shocking News and Rumours on Foreign Exchange Market Dynamics

We analyze the dynamical response of the world's financial community to various types of unexpected events, including the 9/11 terrorist attacks as they unfolded on a minute-by-minute basis. We find that there are various 'species' of news, characterized by how quickly the news get absorbed, how much meaning and importance is assigned to it by the community, and what subsequent actions are then taken. For example, the response to the unfolding events of 9/11 shows a gradual collective understanding of what was happening, rather than an immediate realization. For news items which are not simple economic statements, and hence whose implications are not immediately obvious, we uncover periods of collective discovery during which collective opinions seem to oscillate in a remarkably synchronized way. In the case of a rumour, our findings also provide a concrete example of contagion in inter-connected communities. Practical applications of this work include the possibility of producing selective newsfeeds for specific communities, based on their likely impact

On morphological hierarchical representations for image processing and spatial data clustering

Hierarchical data representations in the context of classi cation and data clustering were put forward during the fties. Recently, hierarchical image representations have gained renewed interest for segmentation purposes. In this paper, we briefly survey fundamental results on hierarchical clustering and then detail recent paradigms developed for the hierarchical representation of images in the framework of mathematical morphology: constrained connectivity and ultrametric watersheds. Constrained connectivity can be viewed as a way to constrain an initial hierarchy in such a way that a set of desired constraints are satis ed. The framework of ultrametric watersheds provides a generic scheme for computing any hierarchical connected clustering, in particular when such a hierarchy is constrained. The suitability of this framework for solving practical problems is illustrated with applications in remote sensing

How Fitch-Margoliash Algorithm can Benefit from Multi Dimensional Scaling

Whatever the phylogenetic method, genetic sequences are often described as strings of characters, thus molecular sequences can be viewed as elements of a multi-dimensional space. As a consequence, studying motion in this space (ie, the evolutionary process) must deal with the amazing features of high-dimensional spaces like concentration of measured phenomenon

On the origin of spaces: morphometric foundations of urban form evolution

The modern discipline of urban morphology gives us a ground for the comparative analysis of cities, which increasingly includes specific quantitative elements. In this paper, we make a further step forward towards the definition of a general method for the classification of urban form. We draw from morphometrics and taxonomy in life sciences to propose such method, which we name ‘urban morphometrics’. We then test it on a unit of the urban landscape named ‘Sanctuary Area’ (SA), explored in 45 cities whose origins span four historic time periods: Historic (medieval), Industrial (19th century), New Towns (post-WWII, high-rise) and Sprawl (post-WWII, low-rise). We describe each SA through 207 physical dimensions and then use these to discover features that discriminate them among the four temporal groups. Nine dimensions emerge as sufficient to correctly classify 90% of the urban settings by their historic origins. These nine attributes largely identify an area's ‘visible identity’ as reflected by three characteristics: (1) block perimeterness, or the way buildings define the street-edge; (2) building coverage, or the way buildings cover the land and (3) regular plot coverage, or the extent to which blocks are made of plots that have main access from a street. Hierarchical cluster analysis utilising only the nine key variables nearly perfectly clusters each SA according to its historic origin; moreover, the resulting dendrogram shows, just after WWII, the first ‘bifurcation’ of urban history, with the emergence of the modern city as a new ‘species’ of urban form. With ‘urban morphometrics’ we hope to extend urban morphological research and contribute to understanding the way cities evolve

Genetic variation of wild and hatchery populations of the catla Indian major carp (Catla catla Hamilton 1822: Cypriniformes, Cyprinidae) revealed by RAPD markers

Genetic variation is a key component for improving a stock through selective breeding programs. Randomly amplified polymorphic DNA (RAPD) markers were used to assess genetic variation in three wild population of the catla carp (Catla catla Hamilton 1822) in the Halda, Jamuna and Padma rivers and one hatchery population in Bangladesh. Five decamer random primers were used to amplify RAPD markers from 30 fish from each population. Thirty of the 55 scorable bands were polymorphic, indicating some degree of genetic variation in all the populations. The proportion of polymorphic loci and gene diversity values reflected a relatively higher level of genetic variation in the Halda population. Sixteen of the 30 polymorphic loci showed a significant (p < 0.05, p < 0.01, p < 0.001) departure from homogeneity and the FST values in the different populations indicated some degree of genetic differentiation in the population pairs. Estimated genetic distances between populations were directly correlated with geographical distances. The unweighted pair group method with averages (UPGMA) dendrogram showed two clusters, the Halda population forming one cluster and the other populations the second cluster. Genetic variation of C. catla is a useful trait for developing a good management strategy for maintaining genetic quality of the species

Methods for evaluating clustering algorithms for gene expression data using a reference set of functional classes

BACKGROUND: A cluster analysis is the most commonly performed procedure (often regarded as a first step) on a set of gene expression profiles. In most cases, a post hoc analysis is done to see if the genes in the same clusters can be functionally correlated. While past successes of such analyses have often been reported in a number of microarray studies (most of which used the standard hierarchical clustering, UPGMA, with one minus the Pearson's correlation coefficient as a measure of dissimilarity), often times such groupings could be misleading. More importantly, a systematic evaluation of the entire set of clusters produced by such unsupervised procedures is necessary since they also contain genes that are seemingly unrelated or may have more than one common function. Here we quantify the performance of a given unsupervised clustering algorithm applied to a given microarray study in terms of its ability to produce biologically meaningful clusters using a reference set of functional classes. Such a reference set may come from prior biological knowledge specific to a microarray study or may be formed using the growing databases of gene ontologies (GO) for the annotated genes of the relevant species. RESULTS: In this paper, we introduce two performance measures for evaluating the results of a clustering algorithm in its ability to produce biologically meaningful clusters. The first measure is a biological homogeneity index (BHI). As the name suggests, it is a measure of how biologically homogeneous the clusters are. This can be used to quantify the performance of a given clustering algorithm such as UPGMA in grouping genes for a particular data set and also for comparing the performance of a number of competing clustering algorithms applied to the same data set. The second performance measure is called a biological stability index (BSI). For a given clustering algorithm and an expression data set, it measures the consistency of the clustering algorithm's ability to produce biologically meaningful clusters when applied repeatedly to similar data sets. A good clustering algorithm should have high BHI and moderate to high BSI. We evaluated the performance of ten well known clustering algorithms on two gene expression data sets and identified the optimal algorithm in each case. The first data set deals with SAGE profiles of differentially expressed tags between normal and ductal carcinoma in situ samples of breast cancer patients. The second data set contains the expression profiles over time of positively expressed genes (ORF's) during sporulation of budding yeast. Two separate choices of the functional classes were used for this data set and the results were compared for consistency. CONCLUSION: Functional information of annotated genes available from various GO databases mined using ontology tools can be used to systematically judge the results of an unsupervised clustering algorithm as applied to a gene expression data set in clustering genes. This information could be used to select the right algorithm from a class of clustering algorithms for the given data set

Phylomemetics—Evolutionary Analysis beyond the Gene

Genes are propagated by error-prone copying, and the resulting variation provides the basis for phylogenetic reconstruction of evolutionary relationships. Horizontal gene transfer may be superimposed on a tree-like evolutionary pattern, with some relationships better depicted as networks. The copying of manuscripts by scribes is very similar to the replication of genes, and phylogenetic inference programs can be used directly for reconstructing the copying history of different versions of a manuscript text. Phylogenetic methods have also been used for some time to analyse the evolution of languages and the development of physical cultural artefacts. These studies can help to answer a range of anthropological questions. We propose the adoption of the term “phylomemetics” for phylogenetic analysis of reproducing non-genetic elements