Adapting Hartigan & Wong K-Means for the Efficient Clustering of Sets1

Abstract

This paper proposes an algorithm, named HWK-Sets, based on K-Means, suited for clustering data which are variable-sized sets of elementary items. An example of such data occurs in the analysis of medical diagnosis, where the goal is to detect human subjects who share common diseases so as to predict future illnesses from previous medical history possibly. Clustering sets is difficult because data objects do not have numerical attributes and therefore it is not possible to use the classical Euclidean distance upon which K-Means is normally based. An adaptation of the Jaccard distance between sets is used, which exploits application-sensitive information. More in particular, the Hartigan and Wong variation of K-Means is adopted, which can favor the fast attainment of a careful solution. The HWK-Sets algorithm can flexibly use various stochastic seeding techniques. Since the difficulty of calculating a mean among the sets of a cluster, the concept of a medoid is employed as a cluster representative (centroid), which always remains a data object of the application. The paper describes the HWK-Sets clustering algorithm and outlines its current implementation in Java based on parallel streams. After that, the efficiency and accuracy of the proposed algorithm are demonstrated by applying it to 15 benchmark datasets.

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Binomial Population of Biological Objects

Opinion

Let the system consist of n of the same type and independent biological individuals with the same indicator p survival at a given interval [0,T] time [1]. Let us assume that a population is subject to an epidemic that leads to the death of some of the individuals.

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On Structure-Function Asymmetry

Abstract

This is a short opinion article pointing out the importance of examining more precisely the intimate relationship between structure and function that arises for all complex systems. It is claimed that this relationship is highly asymmetrical, which has many unforeseen consequences. The presented claims are made based on intuition and inductive reasoning. Although some claims may be only partially valid, for instance, under yet to be specified conditions, they can inspire new research directions to investigate in future

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Copulas in Gene’s Relation

Opinion

Genes are considered as segments of DNA that provide instructions for the production of specific proteins or RNA molecules. Genes can be related to one another in a variety of ways. These relationships can play important roles in the regulation of cellular processes and functions, as well as in the development and evolution of organisms. Gene relation refers to the relationship between two or more genes in terms of their function, location, or sequence similarity. In studying the expression and relationship of genes, researchers can get a sense of how gene-information is used to know cell functions and how changes in gene expression or regulation can lead to disease. As an example, co-expression analysis can help to determine gene-groups that are functionally related and may be involved in specific biological processes or diseases. Also, network analysis can be used to identify regulatory relationships between genes as well as to build gene regulation networks that control specific cellular processes. See also Allocco et al. [1], Elise et al. [2], Ruan et al. [3] & Song et al. [4], By interacting with each other, genes form a complicated network. The relationship between groups of genes with different functions can be represented as gene networks. Since the accessibility of the large gene expression data, several methods have been developed to analyze the gene-to-gene relationship. Copula approach is being used to study the relationship between the expression profiles of pairs of genes, since a copula is a multivariate model that can be used for explaining gene-gene dependence.

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Again, about the Rhythms of Biological Processes

Opinion

Biological rhythms are one of the components of the endogenous time of an organism as a set of all its temporal processes. Circadian, ultra- and intracardiac rhythms have such temporal parameters as latency, speed, period, duration and frequency, i.e., are temporary processes. They are superimposed on the directed time of ontogenesis and, together with monophasic processes, trends and cycles, determine the biochemical and physiological specifics of its certain periods. Numerous studies of the past three decades have contributed to the understanding of the types of molecular oscillators that shape cellular rhythms. These include: an oscillator of impulse activity in neurons and some endocrinocytes; an oscillator that reflects the circadian rhythms of the cAMP and Ca2+ content; redox oscillator and finally PER - oscillator, working on the basis of interactions of clock proteins [1-5]. The specificity of the interaction of these oscillators in cells of different tissues is gradually becoming clear: in endocrinocytes, in blood cells, in hepatocytes and adipocytes. However, the essence of the functions of the rhythms themselves as time processes is not clear enough.

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Bayesian Shape Invariant Model for Latent Growth Curve with Time-Invariant Covariates

Abstract

In the attention-deficit hyperactivity disorder (ADHD) study, children are prescribed different stimulant medications. The height measurements are recorded longitudinally along with the medication time. Differences among the patients are captured by the parameters suggested the Superimposition by Translation and Rotation (SITAR) model using three subject-specific parameters to estimate their deviation from the mean growth curve. In this paper, we generalize the SITAR model in a Bayesian way with time-invariant covariates. The time-invariant model allows us to predict latent growth factors. Since patients suffer from a common disease, they usually exhibit a similar pattern, and it is natural to build a nonlinear model that is shaped invariant. The model is semi-parametric, where the population time curve is modeled with a natural cubic spline. The original shape invariant growth curve model, motivated by epidemiological research on the evolution of pubertal heights over time, fits the underlying shape function for height over age and estimates subject-specific deviations from this curve in terms of size, tempo, and velocity using maximum likelihood. The usefulness of the model is illustrated in the attention deficit hyperactivity disorder (ADHD) study. Further, we demonstrated the effect of stimulant medications on pubertal growth by gender.

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Resolving Issues at Host Level Using Variable Reduction Methods: Case of Cornudiscoides Spp. (Platyhelminthes: Monogenoidea)

 

Abstract

Taxonomy deals with delineating and classifying organisms, and traditionally relies on morphological characters only. We have used morphological characters, and molecular biology to distinguish monogenoids at generic and specific levels. Principal Component Analysis provided a magnifying glass to resolve the taxonomic issues as well as their host levels.

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