Finite size corrections to random Boolean networks

Since their introduction, Boolean networks have been traditionally studied in view of their rich dynamical behavior under different update protocols and for their qualitative analogy with cell regulatory networks. More recently, tools borrowed from statistical physics of disordered systems and from computer science have provided a more complete characterization of their equilibrium behavior. However, the largest part of the results have been obtained in the thermodynamic limit, which is often far from being reached when dealing with realistic instances of the problem. The numerical analysis presented here aims at comparing - for a specific family of models - the outcomes given by the heuristic belief propagation algorithm with those given by exhaustive enumeration. In the second part of the paper some analytical considerations on the validity of the annealed approximation are discussed.Comment: Minor correction

Mining association rules for label ranking

Lecture Notes in Computer Science Volume 6635, 2011.Recently, a number of learning algorithms have been adapted for label ranking, including instance-based and tree-based methods. In this paper, we continue this line of work by proposing an adaptation of association rules for label ranking based on the APRIORI algorithm. Given that the original APRIORI algorithm does not aim to obtain predictive models, two changes were needed for this achievement. The adaptation essentially consists of using variations of the support and confidence measures based on ranking similarity functions that are suitable for label ranking. Additionally we propose a simple greedy method to select the parameters of the algorithm. We also adapt the method to make a prediction from the possibly con icting consequents of the rules that apply to an example. Despite having made our adaptation from a very simple variant of association rules for classification, partial results clearly show that the method is making valid predictions. Additionally, they show that it competes well with state-of-the-art label ranking algorithms.This work was partially supported by project Rank! (PTDC/EIA/81178/2006) from FCT and Palco AdI project Palco3.0 financed by QREN and Fundo Europeu de Desenvolvimento Regional (FEDER). We thank the anonymous referees for useful comments

Medios de comunicación y derecho a la información en Jalisco, 2015

El octavo informe Q ITESO: Análisis Crítico de Medios revisa el funcionamiento del sistema de comunicación política durante el proceso electoral de 2015, así como diversos aspectos relevantes de unas elecciones que culminaron con un cambio radical en el panorama político en Jalisco. En el universo de los medios de comunicación, se analizan los cambios experimentados por estos en el marco de la coyuntura electoral local, la equidad y profundidad en la cobertura por parte de los periódicos y la difusión que hicieron de las encuestas, así como el discurso e impacto de la propaganda difundida a través de la televisión y la Internet, a lo que se suma los debates registrados en redes sociales como Twitter, y la percepción sobre las campañas por parte de la audiencia tapatía. El examen político se enfoca en la campaña realizada por los candidatos independientes, el planteamiento socioeconómico de los contendientes por la capital del estado y el impacto electoral de un personaje incómodo como el papá del gobernador, para culminar este informe con una reflexión general y un balance de quiénes perdieron y quiénes ganaron al término de las elecciones de 2015.ITESO, A.C

SNAI1 expression and the mesenchymal phenotype: an immunohistochemical study performed on 46 cases of oral squamous cell carcinoma

Abstract Background SNAI1 can initiate epithelial-mesenchymal transition (EMT), leading to loss of epithelial characteristics and, in cancer, to invasion and metastasis. We hypothesized that SNAI1 reactivation occurs in oral squamous cell carcinoma (OSCC) where it might also be associated with focal adhesion kinase (FAK) expression and p63 loss. Methods Immunohistochemistry was performed on 46 tumors and 26 corresponding lymph node metastases. Full tissue sections were examined to account for rare and focal expression. Clinical outcome data were collected and analyzed. Results SNAI1-positivity (nuclear, ≥ 5% tumor cells) was observed in 10 tumors and 5 metastases (n = 12 patients). Individual SNAI1(+) tumor cells were seen in primary tumors of 30 patients. High level SNAI1 expression (>10% tumor cells) was rare, but significantly associated with poor outcome. Two cases displayed a sarcomatoid component as part of the primary tumor with SNAI1(+)/FAK(+)/E-cadherin(-)/p63(-) phenotype, but disparate phenotypes in corresponding metastases. All cases had variable SNAI1(+) stroma. A mesenchymal-like immunoprofile in primary tumors characterized by E-cadherin loss (n = 29, 63%) or high cytoplasmic FAK expression (n = 10, 22%) was associated with N(+) status and tumor recurrence/new primary, respectively. Conclusions SNAI1 is expressed, although at low levels, in a substantial proportion of OSCC. High levels of SNAI1 may herald a poor prognosis and circumscribed SNAI1 expression can indicate the presence of a sarcomatoid component. Absence of p63 in this context does not exclude squamous tumor origin. Additional EMT inducers may contribute to a mesenchymal-like phenotype and OSCC progression

High-Order SNP Combinations Associated with Complex Diseases: Efficient Discovery, Statistical Power and Functional Interactions

There has been increased interest in discovering combinations of single-nucleotide polymorphisms (SNPs) that are strongly associated with a phenotype even if each SNP has little individual effect. Efficient approaches have been proposed for searching two-locus combinations from genome-wide datasets. However, for high-order combinations, existing methods either adopt a brute-force search which only handles a small number of SNPs (up to few hundreds), or use heuristic search that may miss informative combinations. In addition, existing approaches lack statistical power because of the use of statistics with high degrees-of-freedom and the huge number of hypotheses tested during combinatorial search. Due to these challenges, functional interactions in high-order combinations have not been systematically explored. We leverage discriminative-pattern-mining algorithms from the data-mining community to search for high-order combinations in case-control datasets. The substantially improved efficiency and scalability demonstrated on synthetic and real datasets with several thousands of SNPs allows the study of several important mathematical and statistical properties of SNP combinations with order as high as eleven. We further explore functional interactions in high-order combinations and reveal a general connection between the increase in discriminative power of a combination over its subsets and the functional coherence among the genes comprising the combination, supported by multiple datasets. Finally, we study several significant high-order combinations discovered from a lung-cancer dataset and a kidney-transplant-rejection dataset in detail to provide novel insights on the complex diseases. Interestingly, many of these associations involve combinations of common variations that occur in small fractions of population. Thus, our approach is an alternative methodology for exploring the genetics of rare diseases for which the current focus is on individually rare variations

De-identifying a public use microdata file from the Canadian national discharge abstract database

<p>Abstract</p> <p>Background</p> <p>The Canadian Institute for Health Information (CIHI) collects hospital discharge abstract data (DAD) from Canadian provinces and territories. There are many demands for the disclosure of this data for research and analysis to inform policy making. To expedite the disclosure of data for some of these purposes, the construction of a DAD public use microdata file (PUMF) was considered. Such purposes include: confirming some published results, providing broader feedback to CIHI to improve data quality, training students and fellows, providing an easily accessible data set for researchers to prepare for analyses on the full DAD data set, and serve as a large health data set for computer scientists and statisticians to evaluate analysis and data mining techniques. The objective of this study was to measure the probability of re-identification for records in a PUMF, and to de-identify a national DAD PUMF consisting of 10% of records.</p> <p>Methods</p> <p>Plausible attacks on a PUMF were evaluated. Based on these attacks, the 2008-2009 national DAD was de-identified. A new algorithm was developed to minimize the amount of suppression while maximizing the precision of the data. The acceptable threshold for the probability of correct re-identification of a record was set at between 0.04 and 0.05. Information loss was measured in terms of the extent of suppression and entropy.</p> <p>Results</p> <p>Two different PUMF files were produced, one with geographic information, and one with no geographic information but more clinical information. At a threshold of 0.05, the maximum proportion of records with the diagnosis code suppressed was 20%, but these suppressions represented only 8-9% of all values in the DAD. Our suppression algorithm has less information loss than a more traditional approach to suppression. Smaller regions, patients with longer stays, and age groups that are infrequently admitted to hospitals tend to be the ones with the highest rates of suppression.</p> <p>Conclusions</p> <p>The strategies we used to maximize data utility and minimize information loss can result in a PUMF that would be useful for the specific purposes noted earlier. However, to create a more detailed file with less information loss suitable for more complex health services research, the risk would need to be mitigated by requiring the data recipient to commit to a data sharing agreement.</p

Planning and Optimizing Semantic Information Requests Using Domain Modeling and Resource Characteristics

The focus of information integration systems providing single access to data from distributed, diverse, and autonomous sources (including web-based sources) has changed from syntax and structure to semantics, allowing more meaningful integratia of data. InfoQuilt goes one step further to support knowledge discovery by providing users with tools to analyze the data, understand the domains and relationships between them, and explore new potential relationships. It provides a framework to model the semantics of domains, complex semantic relationships between them, characteristics of available sources, and provides an interface to specify information requests that the system can &quot;understand&quot;. This thesis focuses on the use of knowledge about domains, their relationships, and sources to efficiently create practical execution plans for such semantic information requests