Probabilistic Independence Networks for Hidden Markov Probability Models

Graphical techniques for modeling the dependencies of randomvariables have been explored in a variety of different areas includingstatistics, statistical physics, artificial intelligence, speech recognition, image processing, and genetics.Formalisms for manipulating these models have been developedrelatively independently in these research communities. In this paper weexplore hidden Markov models (HMMs) and related structures within the general framework of probabilistic independencenetworks (PINs). The paper contains a self-contained review of the basic principles of PINs.It is shown that the well-known forward-backward (F-B) and Viterbialgorithms for HMMs are special cases of more general inference algorithms forarbitrary PINs. Furthermore, the existence of inference and estimationalgorithms for more general graphical models provides a set of analysistools for HMM practitioners who wish to explore a richer class of HMMstructures.Examples of relatively complex models to handle sensorfusion and coarticulationin speech recognitionare introduced and treated within the graphical model framework toillustrate the advantages of the general approach.

Factorial Hidden Markov Models

We present a framework for learning in hidden Markov models with distributed state representations. Within this framework, we derive a learning algorithm based on the Expectation--Maximization (EM) procedure for maximum likelihood estimation. Analogous to the standard Baum-Welch update rules, the M-step of our algorithm is exact and can be solved analytically. However, due to the combinatorial nature of the hidden state representation, the exact E-step is intractable. A simple and tractable mean field approximation is derived. Empirical results on a set of problems suggest that both the mean field approximation and Gibbs sampling are viable alternatives to the computationally expensive exact algorithm.

Estimating Dependency Structure as a Hidden Variable

This paper introduces a probability model, the mixture of trees that can account for sparse, dynamically changing dependence relationships. We present a family of efficient algorithms that use EMand the Minimum Spanning Tree algorithm to find the ML and MAP mixtureof trees for a variety of priors, including the Dirichlet and the MDL priors.

Estimating Dependency Structure as a Hidden Variable

This paper introduces a probability model, the mixture of trees that can account for sparse, dynamically changing dependence relationships. We present a family of efficient algorithms that use EM and the Minimum Spanning Tree algorithm to find the ML and MAP mixture of trees for a variety of priors, including the Dirichlet and the MDL priors. We also show that the single tree classifier acts like an implicit feature selector, thus making the classification performance insensitive to irrelevant attributes. Experimental results demonstrate the excellent performance of the new model both in density estimation and in classification.

Selecting Relevant Genes with a Spectral Approach

Array technologies have made it possible to record simultaneously the expression pattern of thousands of genes. A fundamental problem in the analysis of gene expression data is the identification of highly relevant genes that either discriminate between phenotypic labels or are important with respect to the cellular process studied in the experiment: for example cell cycle or heat shock in yeast experiments, chemical or genetic perturbations of mammalian cell lines, and genes involved in class discovery for human tumors. In this paper we focus on the task of unsupervised gene selection. The problem of selecting a small subset of genes is particularly challenging as the datasets involved are typically characterized by a very small sample size ?? the order of few tens of tissue samples ??d by a very large feature space as the number of genes tend to be in the high thousands. We propose a model independent approach which scores candidate gene selections using spectral properties of the candidate affinity matrix. The algorithm is very straightforward to implement yet contains a number of remarkable properties which guarantee consistent sparse selections. To illustrate the value of our approach we applied our algorithm on five different datasets. The first consists of time course data from four well studied Hematopoietic cell lines (HL-60, Jurkat, NB4, and U937). The other four datasets include three well studied treatment outcomes (large cell lymphoma, childhood medulloblastomas, breast tumors) and one unpublished dataset (lymph status). We compared our approach both with other unsupervised methods (SOM,PCA,GS) and with supervised methods (SNR,RMB,RFE). The results clearly show that our approach considerably outperforms all the other unsupervised approaches in our study, is competitive with supervised methods and in some case even outperforms supervised approaches.

Variability in the Stability and Productivity of Transfected Genes in Chinese Hamster Ovary (CHO) cells

In the field of biologics production, productivity and stability of the transfected gene of interest are two very important attributes that dictate if a production process is viable. To further understand and improve these two traits, we would need to further our understanding of the factors affecting them. These would include integration site of the gene, gene copy number, cell phenotypic variation and cell environment. As these factors play different parts in the development process, they lead to variable productivity and stability of the transfected gene between clones, the well-known phenomenon of “clonal variation”. A study of this phenomenon and how the various factors contribute to it will thus shed light on strategies to improve productivity and stability in the production cell line. Of the four factors, the site of gene integration appears to be one of the most important. Hence, it is proposed that work is done on studying how different integration sites affect the productivity and stability of transfected genes in the development process. For the study to be more industrially relevant, it is proposed that the Chinese Hamster Ovary dhfr-deficient cell line, CHO-DG44, is used as the model system.

En busca de los or??genes del hombre : un recorrido did??ctico por la antropolog??a f??sica

Se trata de un CD multimedia que desarrolla de una forma did??ctica, pero sin perder el rigor cient??fico y filos??fico, una serie de materiales de antropolog??a evolutiva pensados para el Bachillerato. Se pretende que el alumnado comprenda cu??les han sido los or??genes del hombre. Para ello se desarrollan: un tema introductorio sobre c??mo entender la evoluci??n org??nica y c??mo ha ido evolucionando la idea de evoluci??n desde Arist??teles a la Teor??a Sint??tica y otro, que es propiamente el contenido del CD, sobre la evoluci??n humana desde los primates hasta el hombre moderno.

Polimorfismos de los genes P53, CYP1A1, CYP1B1, GSTM1, GSTT1 y GSTP1 en cáncer de seno familiar en una población colombiana

Se considera que la susceptibilidad al cáncer de seno es poligenética1; es necesario estudiar otros factores genéticos. Se realizó un estudio analítico de casos y controles (1:2) 120:240. Se tomó una muestra de sangre periférica, y posteriormente se realizó la extracción de ADN (PROBE) y se determinó la presencia de los polimorfismos. Se encontraron asociaciones estadísticamente significativas con el cáncer de seno para: del grupo p53 exón 4, la Arginina con un OR 1,923 IC 95% (1,117 – 3.309); del grupo p53 intrón 3 el I3wm con OR 30,887 IC 95% (3,709 – 257,209); del grupo p53 intrón 6 el I6wm con OR 2.061 IC 95% (1.059 - 4,013); del grupo CYP1B1 Val432leu, la Valina con un OR 2.273 con un IC del 95% (1.084 – 4.855); del grupo CYP1B1 Asn453se la Asparagina/Serina con un OR 1,987 con IC 95% (1.076 – 3.670). El polimorfismo gstm 1r reporta un OR 0,366 con IC 95% (0,219 – 0,613) el cual se considera como protector para cáncer de seno.

Testing for Hidden Information and Action in Automobile Insurance Market

I test the presence of hidden information and action in the automobile insurance market using a data set from several Colombian insurers. To identify the presence of hidden information I find a common knowledge variable providing information on policyholder s risk type which is related to both experienced risk and insurance demand and that was excluded from the pricing mechanism. Such unused variable is the record of policyholder s traffic offenses. I find evidence of adverse selection in six of the nine insurance companies for which the test is performed. From the point of view of hidden action I develop a dynamic model of effort in accident prevention given an insurance contract with bonus experience rating scheme and I show that individual accident probability decreases with previous accidents. This result brings a testable implication for the empirical identification of hidden action and based on that result I estimate an econometric model of the time spans between the purchase of the insurance and the first claim, between the first claim and the second one, and so on. I find strong evidence on the existence of unobserved heterogeneity that deceives the testable implication. Once the unobserved heterogeneity is controlled, I find conclusive statistical grounds supporting the presence of moral hazard in the Colombian insurance market.

Estudio molecular de los genes cdkn1b y cited2 en mujeres con falla ovárica prematura idiopática

En los últimos años el aumento sustancial de la incidencia de la infertilidad humana ha convertido esta patología en un problema real de salud pública: alrededor del 15% de las parejas consultan por esta causa. Entre las causas femeninas de infertilidad, la falla ovárica prematura (FOP) es extremadamente frecuente puesto que afecta entre el 1 y el 3% de las mujeres de la población general. Múltiples etiologías de FOP se han descrito (e.g. autoinmunes, infecciosas, iatrogénicas) pero desafortunadamente en más de 80% de los casos no se conocen las causas, lo que sugiere mecanismos genéticos subyacentes. Algunas causas genéticas se han descrito, especialmente relacionadas con formas sindrómicas de la enfermedad (e.g. síndromes de Turner y BPES). En estos casos se evidencian principalmente alteraciones cromosómicas y mutaciones específicas en genes participantes en la foliculogénesis. En otros casos, la presentación de la enfermedad es aislada y se relaciona con mutaciones en genes específicos localizados en los autosomas y en el cromosoma X. Sin embargo, la complejidad genética, la baja heredabilidad y el carácter cuantitativo de los fenotipos asociados a la reproducción en los mamíferos implica que en casos fisiológicos y patológicos (hipofertilidad e infertilidad) cientos de genes participen en una red de sutil regulación. En este contexto, recientemente se han propuesto una cantidad significativa de genes candidato para la FOP. Por consiguiente el estudio de genes potencialmente candidatos en la etiología de la FOP por aproximaciones gen candidato, entre ellos CDKN1B y CITED2, es de especial interés en la comprensión de los mecanismos subyacentes de esta en enfermedad. Además, es una etapa necesaria en la búsqueda de nuevos marcadores de esta patología que permitan en un futuro mejorar el asesoramiento genético y proponer alternativas terapéuticas. Durante este trabajo de tesis nos hemos focalizado en la búsqueda de variantes en la secuencia codificante de CDKN1B y CITED2 en mujeres FOP. Nuestros resultados sugieren que estos genes son dos nuevos factores etiológicos de la enfermedad.

??Qu?? ha supuesto Edutec? : apuntes para no perder los or??genes

Resumen tomado del autor

Polimorfismos de los genes ABCB1 y CYP2C8 en pacientes sometidos a TASPE. Análisis farmacogenético del Paclitaxel en la toxicidad y movilización de progenitores hemopoyéticos

Fundamentos. La eficacia de paclitaxel junto con rhG-CSF en la movilización de progenitores hematopoyéticos, se ha probada en pacientes hematológicos. Farmacogenéticamente el paclitaxel presenta una alta variabilidad inter-individual. Los genes CYP2C8 y ABCB1 involucrados en su metabolismo y transporte podrían afectar dicha variabilidad inter-individual. Objetivo. Evaluar en una cohorte retrospectiva de pacientes sometidos a TASPE, el efecto de algunos polimorfismos de nucleótido simple (del gen CYP2C8 y del gen ABCB1) sobre la eficacia en la movilización y toxicidad hematológica inducida por del paclitaxel. Materiales y Métodos. Un grupo de 107 pacientes recibieron paclitaxel y rhG-CSF como esquema movilizador. Los polimorfismos genotipados fueron para los genes ABCB1 rs1045642 A>G, ABCB1 rs2032582 C>A, ABCB1 rs2032582 C>T, CYP2C8 rs10509681 C>T, y CYP2C8 rs11572080 A>G. Resultados. El uso de paclitaxel logró éxito movilizador en más del 80% de los pacientes con linfomas o mieloma (p=0,0021), pero no lo fue en la leucemia aguda. En pacientes con mieloma la variable G>rs1045642 del gen ABCB1 se asoció con mala movilización (p= 0,018) y mayor toxicidad hematológica (p= 0,034). El alelo C>rs10509681 del gen CYP2C8 se relacionó con mayor toxicidad en pacientes con linfoma (p= 0,045) y mieloma múltiple (p=0,042), y portadores del alelo TT en homocigosis presentaron una mayor toxicidad hematológica comparada con los portadores CC o CT (p= 0,027). Conclusión. Este estudio sugiere que los SNPs de las variables alélicas analizadas en los genes CYP2C8 y ABCB1 en algunos grupos de pacientes inciden en la capacidad movilizadora y afectan el grado de toxicidad hematológica.

Els col??legis privats de segona ensenyan??a a Mallorca : els or??gens. 'Los colegios privados de segunda ense??anza en Mallorca : los or??genes'

Resumen tomado parcialmente de la publicaci??n