An integrative bioinformatic approach for identifying subtypes and subtype-specific drivers in cancer

Cancer is a complex disease and within a cancer, subtypes of patients with distinct behaviors often exist. The subtypes might have been caused by different hits, such as copy number aberrations (CNAs) and point mutations, on different pathways/cells-of-origin in a common tissue/organ. Identifying the subtypes with subtype-specific drivers, i.e., hits, is key to the understanding of cancer and development of novel treatments. Here, we report the development of an integrative method to identify the subtypes of cancer. Specifically, we consider CNAs and their impact on gene expressions. Based on these relations, we propose an iterative approach that alternates between kernel based gene expression clustering and gene signature selection. We applied the method to datasets of the pediatric cancer medulloblastoma (MB). The consensus number of clusters quickly converges to three; and for each of these three subtypes, the signature detection also converges to a consistent set of a few hundred highly functionally related genes. For each of the subtypes, we correlate its signature with the set of within-subtype recurrent CNA-affected genes for identifying drivers. The top-ranked driver candidates are found to be enriched with known pathways in certain subtypes of MB as well as containing novel genes that might reveal new understandings for other subtypes.published_or_final_versionThe 2012 IEEE Symposium on Computational Intelligence in Bioinformatics and Computational Biology (CIBCB’12), San Diego, CA., 9-12 May 2012. In IEEE CIBCB Proceedings, 2012, p. 169-17

Segment and track neurons in 3D by repulsive snake method

We present a snake (active contour) model based on repulsive force to segment neurons obtained from microscopy. Based on these segmentation results, we track the neurons in 3D image to look for its branch structure. These segmentation results allow user to study morphology of neurons to further investigate neuronal function and connectivity. This repulsive snake model can successfully segment two or multiple neurons that are close to each other by some alternating repulsive force generated from the neighboring objects. We apply our results on real data to demonstrate the performance of our method. © 2005 IEEE.published_or_final_versio

A gene signature based method for identifying subtypes and subtype-specific drivers in cancer with an application to medulloblastoma

published_or_final_versio

Computational analysis of image-based drug profiling predicts synergistic drug combinations: Applications in triple-negative breast cancer

© 2014 Federation of European Biochemical Societies. An imaged-based profiling and analysis system was developed to predict clinically effective synergistic drug combinations that could accelerate the identification of effective multi-drug therapies for the treatment of triple-negative breast cancer and other challenging malignancies. The identification of effective drug combinations for the treatment of triple-negative breast cancer (TNBC) was achieved by integrating high-content screening, computational analysis, and experimental biology. The approach was based on altered cellular phenotypes induced by 55 FDA-approved drugs and biologically active compounds, acquired using fluorescence microscopy and retained in multivariate compound profiles. Dissimilarities between compound profiles guided the identification of 5 combinations, which were assessed for qualitative interaction on TNBC cell growth. The combination of the microtubule-targeting drug vinblastine with KSP/Eg5 motor protein inhibitors monastrol or ispinesib showed potent synergism in 3 independent TNBC cell lines, which was not substantiated in normal fibroblasts. The synergistic interaction was mediated by an increase in mitotic arrest with cells demonstrating typical ispinesib-induced monopolar mitotic spindles, which translated into enhanced apoptosis induction. The antitumour activity of the combination vinblastine/ispinesib was confirmed in an orthotopic mouse model of TNBC. Compared to single drug treatment, combination treatment significantly reduced tumour growth without causing increased toxicity. Image-based profiling and analysis led to the rapid discovery of a drug combination effective against TNBC invitro and invivo, and has the potential to lead to the development of new therapeutic options in other hard-to-treat cancers

Creación, re significación y uso de recursos simbólicos y discursivos durante el gobierno de Evo Morales (2006-2019)

Trabajo de conclusión de curso presentado al Instituto Latinoamericano de Economía, Sociedad y Política de la Universidad Federal de Integración Latino Americana, como requisito parcial para acceder a la Licenciatura en Relaciones Internacionales e Integración Orientador: Prof. Dra. Paula Daniela FernandezDurante los trece años de gobierno de Evo Morales (2006-2019) junto a su partido el Movimiento al Socialismo (MAS), se observan grandes contradicciones entre su discurso político de orden indigenista y sus prácticas políticas, algunas que se remiten a formas políticas de gobiernos tradicionales y de corte occidental. En este sentido se pueden apreciar sus herramientas simbólicas y discursivas, que por un lado toman la imagen de Morales como el único camino posible para el “proceso de cambio”, y otros que resignifican elementos del mundo indígena para ser incorporados al ideario del MAS. De este modo el objetivo de este trabajo es describir y analizar los recursos simbólicos y discursivos utilizados por el gobierno de Evo Morales y el MAS en el periodo 2006- 2019 a fin de legitimarse y consolidarse en el poder.Durante os treze anos de governo de Evo Morales (2006-2019) com seu partido, o Movimento ao Socialismo (MAS), existem grandes contradições entre seu discurso político de uma ordem indigenista e suas práticas políticas, algumas que se referem a formas políticas de governos tradicionais e ocidentais. Nesse sentido, podem ser apreciadas suas ferramentas simbólicas e discursivas, que por um lado tomam a imagem de Morales como o único caminho possível para o “processo de mudança” e outras que ressignificam elementos do mundo indígena a serem incorporados à ideologia do MAS. Assim, o objetivo deste trabalho é descrever e analisar os recursos simbólicos e discursivos utilizados pelo governo de Evo Morales e pelo MAS no período 2006-2019 para legitimar e consolidar no poder

Illness causal beliefs in Turkish immigrants

<p>Abstract</p> <p>Background</p> <p>People hold a wide variety of beliefs concerning the causes of illness. Such beliefs vary across cultures and, among immigrants, may be influenced by many factors, including level of acculturation, gender, level of education, and experience of illness and treatment. This study examines illness causal beliefs in Turkish-immigrants in Australia.</p> <p>Methods</p> <p>Causal beliefs about somatic and mental illness were examined in a sample of 444 members of the Turkish population of Melbourne. The socio-demographic characteristics of the sample were broadly similar to those of the Melbourne Turkish community. Five issues were examined: the structure of causal beliefs; the relative frequency of natural, supernatural and metaphysical beliefs; ascription of somatic, mental, or both somatic and mental conditions to the various causes; the correlations of belief types with socio-demographic, modernizing and acculturation variables; and the relationship between causal beliefs and current illness.</p> <p>Results</p> <p>Principal components analysis revealed two broad factors, accounting for 58 percent of the variation in scores on illness belief scales, distinctly interpretable as natural and supernatural beliefs. Second, beliefs in natural causes were more frequent than beliefs in supernatural causes. Third, some causal beliefs were commonly linked to both somatic and mental conditions while others were regarded as more specific to either somatic or mental disorders. Last, there was a range of correlations between endorsement of belief types and factors defining heterogeneity within the community, including with demographic factors, indicators of modernizing and acculturative processes, and the current presence of illness.</p> <p>Conclusion</p> <p>Results supported the classification of causal beliefs proposed by Murdock, Wilson & Frederick, with a division into natural and supernatural causes. While belief in natural causes is more common, belief in supernatural causes persists despite modernizing and acculturative influences. Different types of causal beliefs are held in relation to somatic or mental illness, and a variety of apparently logically incompatible beliefs may be concurrently held. Illness causal beliefs are dynamic and are related to demographic, modernizing, and acculturative factors, and to the current presence of illness. Any assumption of uniformity of illness causal beliefs within a community, even one that is relatively culturally homogeneous, is likely to be misleading. A better understanding of the diversity, and determinants, of illness causal beliefs can be of value in improving our understanding of illness experience, the clinical process, and in developing more effective health services and population health strategies.</p

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Comparison of reversible-jump Markov-chain-Monte-Carlo learning approach with other methods for missing enzyme identification

Computational identification of missing enzymes plays a significant role in accurate and complete reconstruction of metabolic network for both newly sequenced and well-studied organisms. For a metabolic reaction, given a set of candidate enzymes identified according to certain biological evidences, a powerful mathematical model is required to predict the actual enzyme(s) catalyzing the reactions. In this study, several plausible predictive methods are considered for the classification problem in missing enzyme identification, and comparisons are performed with an aim to identify a method with better performance than the Bayesian model used in previous work. In particular, a regression model consisting of a linear term and a nonlinear term is proposed to apply to the problem, in which the reversible jump Markov-chain-Monte-Carlo (MCMC) learning technique (developed in [Andrieu C, Freitas Nando de, Doucet A. Robust full Bayesian learning for radial basis networks 2001;13:2359-407.]) is adopted to estimate the model order and the parameters. We evaluated the models using known reactions in Escherichia coli, Mycobacterium tuberculosis, Vibrio cholerae and Caulobacter cresentus bacteria, as well as one eukaryotic organism, Saccharomyces Cerevisiae. Although support vector regression also exhibits comparable performance in this application, it was demonstrated that the proposed model achieves favorable prediction performance, particularly sensitivity, compared with the Bayesian method. © 2007 Elsevier Inc. All rights reserved.link_to_subscribed_fulltex

Repulsive force based snake model to segment and track neuronal axons in 3D microscopy image stacks

The branching patterns of axons and dendrites are fundamental structural properties that affect the synaptic connectivity of axons. Although today three-dimensional images of fluorescently labeled processes can be obtained to study axonal branching, there are no robust methods of tracing individual axons. This paper describes a repulsive force based snake model to segment and track axonal profiles in 3D images. This new method segments all the axonal profiles in a 2D image and then uses the results obtained from that image as prior information to help segment the adjacent 2D image. In this way, the segmentation successfully connects axonal profiles over hundreds of images in a 3D image stack. Individual axons can then be extracted based on the segmentation results. The utility and performance of the method are demonstrated using 3D axonal images obtained from transgenic mice that express fluorescent protein. © 2006 Elsevier Inc. All rights reserved.link_to_subscribed_fulltex