Probabilistic estimation of microarray data reliability and underlying gene expression

Background: The availability of high throughput methods for measurement of mRNA concentrations makes the reliability of conclusions drawn from the data and global quality control of samples and hybridization important issues. We address these issues by an information theoretic approach, applied to discretized expression values in replicated gene expression data. Results: Our approach yields a quantitative measure of two important parameter classes: First, the probability $P(\sigma | S)$ that a gene is in the biological state $\sigma$ in a certain variety, given its observed expression $S$ in the samples of that variety. Second, sample specific error probabilities which serve as consistency indicators of the measured samples of each variety. The method and its limitations are tested on gene expression data for developing murine B-cells and a $t$-test is used as reference. On a set of known genes it performs better than the $t$-test despite the crude discretization into only two expression levels. The consistency indicators, i.e. the error probabilities, correlate well with variations in the biological material and thus prove efficient. Conclusions: The proposed method is effective in determining differential gene expression and sample reliability in replicated microarray data. Already at two discrete expression levels in each sample, it gives a good explanation of the data and is comparable to standard techniques.Comment: 11 pages, 4 figure

A Globally Consistent Framework for Reliability-based Trade Statistics Reconciliation in the Presence of an Entrepôt

This paper develops a mathematicla programming model to reconcile trade statistics subject to a set of global consistency conditions in the presence of an entrepot. Initial data reliability serves a key function for governing the magnitude of adjustment. Through a two-stage optimization procedure, the adjusted trade statistics are achived as solutions to a system of simultaneous equations that minimize a quadratic penalty function. As an empirical illustration, the model is applied to reconcile the 2004 trade statistics reported by China, Hong Kong, and their major trading partners, initialized with detailed estimates of bilateral trade flows, re-export markups, cif/fob ratios and data reliability indexes.trade statistics reconciliation, entrepot trade, data reliability, global consistency

Vector Approach to Context Data Reliability

Context-aware processing is a part of intensively developed ubiquitous computing andmobile systems. Surrounding awareness is used to introduce new functions and solutions. Somecategories of the context data are taken for security purposes in the context-aware security implementations.This kind of data has to meet some conditions since it is used for decision making aboutsecurity mechanisms adaptation and configuration. One of these conditions is reliability. The paperpresents vector approach to context data reliability assessment introducing mechanism which allowsto assess reliability parameters for further usage in context aware security processing. The followingaspects of the context data are taken into account: interface reliability, data quality, data source reliabilityand security level. Introducing reliability metric for context data may be beneficial to othermechanisms which utilize context data. The vector form of reliability may be even more flexible thanthe scalar value

Threshold-based Selective Cooperative-NOMA

In this letter, we propose threshold-based selective cooperative-NOMA (TBS-C-NOMA) to increase the data reliability of conventional cooperative-NOMA (C-NOMA) networks. In TBS-C-NOMA, the intra-cell user forwards the symbols of cell-edge user after successive interference canceler (SIC) only if the signal-to-interference plus noise ratio (SINR) is greater than the pre-determined threshold value. Hence, the data reliability of the cell-edge user is increased by eliminating the effect of the error propagation. We derive closed-form end-to-end exact bit error probability (BEP) of proposed system for various modulation constellations. Then, the optimum threshold value is analyzed in order to minimize BEP. The obtained expressions are validated via simulations and it is revealed that TBS-C-NOMA outperforms C-NOMA and full diversity order is achieved

Reliable online social network data collection

Large quantities of information are shared through online social networks, making them attractive sources of data for social network research. When studying the usage of online social networks, these data may not describe properly users’ behaviours. For instance, the data collected often include content shared by the users only, or content accessible to the researchers, hence obfuscating a large amount of data that would help understanding users’ behaviours and privacy concerns. Moreover, the data collection methods employed in experiments may also have an effect on data reliability when participants self-report inacurrate information or are observed while using a simulated application. Understanding the effects of these collection methods on data reliability is paramount for the study of social networks; for understanding user behaviour; for designing socially-aware applications and services; and for mining data collected from such social networks and applications. This chapter reviews previous research which has looked at social network data collection and user behaviour in these networks. We highlight shortcomings in the methods used in these studies, and introduce our own methodology and user study based on the Experience Sampling Method; we claim our methodology leads to the collection of more reliable data by capturing both those data which are shared and not shared. We conclude with suggestions for collecting and mining data from online social networks.Postprin

Improving Offshore Regulatory Data Reliability for Decision Making

PresentationAs the need for quality data increases for both internal and public use for BSEE's regulatory enforcement activities, a greater emphasis has been placed on improving data reliability. A review of offshore operational event data in 2014, such as loss of well control and injuries, showed significant disparities between data entry and data validation, upwards of 35%. Without this data, BSEE cannot determine improvement to offshore safety and environmental management systems, or create new initiatives such as the Risk Based Inspections Program. To correct this issue, BSEE is taking a number of approaches; submitting defects to internal change request boards, implementing a data stewardship program and contracting Argonne National Lab to create a validation procedure. With the help of these initiatives, BSEE can replicate quality data sets for decision making and use by external stakeholders

Manager perceptions of big data reliability in revenue management.

This paper investigates the perceptions that managers have of the value and reliability of using big data to make hotel revenue management and pricing decisions. Whilst big data-driven automated revenue systems are technically capable of making pricing and inventory decisions without user input, the findings here show that the reality is that managers still interact with every stage of the revenue and pricing process from data collection to the implementation of price changes. They believe their personal insights are as valid as big data in increasing the reliability of the decision-making process. This is driven primarily by a lack of trust on the behalf of managers in the ability of the big data systems to understand and interpret local market and customer dynamics. The less a manager believes in the ability of those systems to interpret this data, the more they perceive gut-instinct to increase the reliability of their decision-making and the less they conduct an analysis of the statistical data provided by the systems. This provides a clear message that there appears to be a need for automated revenue systems to be flexible enough for managers to import the local data, information, and knowledge that they believe leads to revenue growth

Exploiting Data Reliability and Fuzzy Clustering for Journal Ranking

This is the author accepted manuscript. The final version is available from IEEE Computational Intelligence Society via http://dx.doi.org/10.1109/TFUZZ.2016.2612265Journal impact indicators are widely accepted as possible measurements of academic journal quality. However, much debate has recently surrounded their use, and alternative journal impact evaluation techniques are desirable. Aggregation of multiple indicators offers a promising method to produce a more robust ranking result, avoiding the possible bias caused by the use of a single impact indicator. In this paper, fuzzy aggregation and fuzzy clustering, especially the Ordered Weighted Averaging (OWA) operators are exploited to aggregate the quality scores of academic journals that are obtained from different impact indicators. Also, a novel method for linguistic term-based fuzzy cluster grouping is proposed to rank academic journals. The work allows for the construction of distinctive fuzzy clusters of academic journals on the basis of their performance with respect to different journal impact indicators, which may be subsequently combined via the use of the OWA operators. Journals are ranked in relation to their memberships in the resulting combined fuzzy clusters. In particular, the nearest-neighbour guided aggregation operators are adopted to characterise the reliability of the indicators, and the fuzzy clustering mechanism is utilised to enhance the interpretability of the underlying ranking procedure. The ranking results of academic journals from six subjects are systematically compared with the outlet ranking used by the Excellence in Research for Australia (ERA), demonstrating the significant potential of the proposed approach.publishersversionPeer reviewe