Extracting protein-protein interactions from MEDLINE using the hidden vector state model

A major challenge in text mining for biomedicine is automatically extracting protein-protein interactions from the vast amount of biomedical literature. We have constructed an information extraction system based on the Hidden Vector State (HVS) model for protein-protein interactions. The HVS model can be trained using only lightly annotated data whilst simultaneously retaining sufficient ability to capture the hierarchical structure. When applied in extracting protein-protein interactions, we found that it performed better than other established statistical methods and achieved 61.5% in F-score with balanced recall and precision values. Moreover, the statistical nature of the pure data-driven HVS model makes it intrinsically robust and it can be easily adapted to other domains.

Is there life in the old dogs yet? Making break-tests work on financial contagion

Many tests of financial contagion require a definition of the dates separating calm from crisis periods. We propose to use a battery of break search procedures for individual time series to objectively identify potential break dates in relationships between countries. Applied to the biggest European stock markets and combined with two well established tests for financial contagion, this approach results in break dates which correctly identify the timing of changes in cross-country transmission mechanisms. Application of break search procedures breathes new life into the established contagion tests, allowing for an objective, data-driven timing of crisis periods.

One date, one break?

This paper demonstrates that the conventional approach of using official liberalisation dates as the only existing breakdates could lead to inaccurate conclusions as to the effect of the underlying liberalisation policies. It also proposes an alternative paradigm for obtaining more robust estimates of volatility changes around official liberalisation dates and/or other important market events. By focusing on five East Asian emerging markets, all of which liberalised their financial markets in the late, and by using recent advances in the econometrics of structural change, it shows that (i) the detected breakdates in the volatility of stock market returns can be dramatically different to official liberalisation dates and (ii) the use of official liberalisation dates as breakdates can readily entail inaccurate inference. In contrast, the use of data-driven techniques for the detection of multiple structural changes leads to a richer and inevitably more accurate pattern of volatility evolution emerges in comparison with focussing on official liberalisation dates.

Breaking down the non-normality of daily stock returns

This paper investigates whether the non-normality typically observed in daily stock-market returns could arise because of the joint existence of breaks and GARCH effects. It proposes a data-driven procedure to credibly identify the number and timing of breaks and applies it on the benchmark stock-market indices of 27 OECD countries. The findings suggest that a substantial element of the observed deviations from normality might indeed be due to the co-existence of breaks and GARCH effects. However, the presence of structural changes is found to be the primary reason for the non-normality and not the GARCH effects. Also, there is still some remaining excess kurtosis that is unlikely to be linked to the specification of the conditional volatility or the presence of breaks. Finally, an interesting sideline result implies that GARCH models have limited capacity in forecasting stock-market volatility.

Monetary variability and monetary variables in the Franc zone

Failure to detect or account for structural changes in economic modelling can lead to misleading policy inferences, which can be perilous, especially for the more fragile economies of developing countries. Using three potential monetary policy instruments (Money Base, M0, and Reserve Money) for 13 member-states of the CFA Franc zone over the period 1989:11-2002:09, we investigate the magnitude of information extracted by employing data-driven techniques when analyzing breaks in time-series, rather than the simplifying practice of imposing policy implementation dates as break dates. The paper also tests Granger's (1980) aggregation theory and highlights some policy implications of the results.

Stock market efficiency before and after a financial liberalisation reform:do breaks in volatility dynamics matter?

This article focuses on the deviations from normality of stock returns before and after a financial liberalisation reform, and shows the extent to which inference based on statistical measures of stock market efficiency can be affected by not controlling for breaks. Drawing from recent advances in the econometrics of structural change, it compares the distribution of the returns of five East Asian emerging markets when breaks in the mean and variance are either (i) imposed using certain official liberalisation dates or (ii) detected non-parametrically using a data-driven procedure. The results suggest that measuring deviations from normality of stock returns with no provision for potentially existing breaks incorporates substantial bias. This is likely to severely affect any inference based on the corresponding descriptive or test statistics.

Intonation contour realisation for Standard Yorùbá text-to-speech synthesis:a fuzzy computational approach

This paper presents a novel intonation modelling approach and demonstrates its applicability using the Standard Yorùbá language. Our approach is motivated by the theory that abstract and realised forms of intonation and other dimensions of prosody should be modelled within a modular and unified framework. In our model, this framework is implemented using the Relational Tree (R-Tree) technique. The R-Tree is a sophisticated data structure for representing a multi-dimensional waveform in the form of a tree. Our R-Tree for an utterance is generated in two steps. First, the abstract structure of the waveform, called the Skeletal Tree (S-Tree), is generated using tone phonological rules for the target language. Second, the numerical values of the perceptually significant peaks and valleys on the S-Tree are computed using a fuzzy logic based model. The resulting points are then joined by applying interpolation techniques. The actual intonation contour is synthesised by Pitch Synchronous Overlap Technique (PSOLA) using the Praat software. We performed both quantitative and qualitative evaluations of our model. The preliminary results suggest that, although the model does not predict the numerical speech data as accurately as contemporary data-driven approaches, it produces synthetic speech with comparable intelligibility and naturalness. Furthermore, our model is easy to implement, interpret and adapt to other tone languages.

Dynamic Ontologies in Information Security Systems

Different types of ontologies and knowledge or metaknowledge connected to them are considered and analyzed aiming at realization in contemporary information security systems (ISS) and especially the case of intrusion detection systems (IDS) or intrusion prevention systems (IPS). Human-centered methods INCONSISTENCY, FUNNEL, CALEIDOSCOPE and CROSSWORD are algorithmic or data-driven methods based on ontologies. All of them interact on a competitive principle ‘survival of the fittest’. They are controlled by a Synthetic MetaMethod SMM. It is shown that the data analysis frequently needs an act of creation especially if it is applied to knowledge-poor environments. It is shown that human-centered methods are very suitable for resolutions in case, and often they are based on the usage of dynamic ontologies

Towards in silico prediction of immunogenic epitopes

As torrents of new data now emerge from microbial genomics, bioinformatic prediction of immunogenic epitopes remains challenging but vital. In silico methods often produce paradoxically inconsistent results: good prediction rates on certain test sets but not others. The inherent complexity of immune presentation and recognition processes complicates epitope prediction. Two encouraging developments – data driven artificial intelligence sequence-based methods for epitope prediction and molecular modeling methods based on three-dimensional protein structures – offer hope for the future.

Parameterized Link Functions in Generalized Linear Random Effect Models: a Case Study on Breast Cancer Treatment

In non-linear random effects some attention has been very recently devoted to the analysis ofsuitable transformation of the response variables separately (Taylor 1996) or not (Oberg and Davidian 2000) from the transformations of the covariates and, as far as we know, no investigation has been carried out on the choice of link function in such models. In our study we consider the use of a random effect model when a parameterized family of links (Aranda-Ordaz 1981, Prentice 1996, Pregibon 1980, Stukel 1988 and Czado 1997) is introduced. We point out the advantages and the drawbacks associated with the choice of this data-driven kind of modeling. Difficulties in the interpretation of regression parameters, and therefore in understanding the influence of covariates, as well as problems related to loss of efficiency of estimates and overfitting, are discussed. A case study on radiotherapy usage in breast cancer treatment is discussed.

Class Size Reduction: A Significant Option for Improving Student Learning

Raising academic standards is a driving force behind public school initiatives. True educational reform requires a data-driven approach to choosing valid options for student improvement. We discuss current and continuing research that provides evidence that class size reduction, with related variables, is a significant option for improving student learning.

Statistical dialog management for health interventions

Research endeavors on spoken dialogue systems in the 1990s and 2000s have led to the deployment of commercial spoken dialogue systems (SDS) in microdomains such as customer service automation, reservation/booking and question answering systems. Recent research in SDS has been focused on the development of applications in different domains (e.g. virtual counseling, personal coaches, social companions) which requires more sophistication than the previous generation of commercial SDS. The focus of this research project is the delivery of behavior change interventions based on the brief intervention counseling style via spoken dialogue systems. ^ Brief interventions (BI) are evidence-based, short, well structured, one-on-one counseling sessions. Many challenges are involved in delivering BIs to people in need, such as finding the time to administer them in busy doctors' offices, obtaining the extra training that helps staff become comfortable providing these interventions, and managing the cost of delivering the interventions. Fortunately, recent developments in spoken dialogue systems make the development of systems that can deliver brief interventions possible. ^ The overall objective of this research is to develop a data-driven, adaptable dialogue system for brief interventions for problematic drinking behavior, based on reinforcement learning methods. The implications of this research project includes, but are not limited to, assessing the feasibility of delivering structured brief health interventions with a data-driven spoken dialogue system. Furthermore, while the experimental system focuses on harmful alcohol drinking as a target behavior in this project, the produced knowledge and experience may also lead to implementation of similarly structured health interventions and assessments other than the alcohol domain (e.g. obesity, drug use, lack of exercise), using statistical machine learning approaches. ^ In addition to designing a dialog system, the semantic and emotional meanings of user utterances have high impact on interaction. To perform domain specific reasoning and recognize concepts in user utterances, a named-entity recognizer and an ontology are designed and evaluated. To understand affective information conveyed through text, lexicons and sentiment analysis module are developed and tested.^

Temporal Mining for Distributed Systems

Many systems and applications are continuously producing events. These events are used to record the status of the system and trace the behaviors of the systems. By examining these events, system administrators can check the potential problems of these systems. If the temporal dynamics of the systems are further investigated, the underlying patterns can be discovered. The uncovered knowledge can be leveraged to predict the future system behaviors or to mitigate the potential risks of the systems. Moreover, the system administrators can utilize the temporal patterns to set up event management rules to make the system more intelligent. With the popularity of data mining techniques in recent years, these events grad- ually become more and more useful. Despite the recent advances of the data mining techniques, the application to system event mining is still in a rudimentary stage. Most of works are still focusing on episodes mining or frequent pattern discovering. These methods are unable to provide a brief yet comprehensible summary to reveal the valuable information from the high level perspective. Moreover, these methods provide little actionable knowledge to help the system administrators to better man- age the systems. To better make use of the recorded events, more practical techniques are required. From the perspective of data mining, three correlated directions are considered to be helpful for system management: (1) Provide concise yet comprehensive summaries about the running status of the systems; (2) Make the systems more intelligence and autonomous; (3) Effectively detect the abnormal behaviors of the systems. Due to the richness of the event logs, all these directions can be solved in the data-driven manner. And in this way, the robustness of the systems can be enhanced and the goal of autonomous management can be approached. This dissertation mainly focuses on the foregoing directions that leverage tem- poral mining techniques to facilitate system management. More specifically, three concrete topics will be discussed, including event, resource demand prediction, and streaming anomaly detection. Besides the theoretic contributions, the experimental evaluation will also be presented to demonstrate the effectiveness and efficacy of the corresponding solutions.

Improved Criteria for Estimating Calibration Factors for Highway Safety Manual (HSM) Applications

The Highway Safety Manual (HSM) estimates roadway safety performance based on predictive models that were calibrated using national data. Calibration factors are then used to adjust these predictive models to local conditions for local applications. The HSM recommends that local calibration factors be estimated using 30 to 50 randomly selected sites that experienced at least a total of 100 crashes per year. It also recommends that the factors be updated every two to three years, preferably on an annual basis. However, these recommendations are primarily based on expert opinions rather than data-driven research findings. Furthermore, most agencies do not have data for many of the input variables recommended in the HSM. This dissertation is aimed at determining the best way to meet three major data needs affecting the estimation of calibration factors: (1) the required minimum sample sizes for different roadway facilities, (2) the required frequency for calibration factor updates, and (3) the influential variables affecting calibration factors. In this dissertation, statewide segment and intersection data were first collected for most of the HSM recommended calibration variables using a Google Maps application. In addition, eight years (2005-2012) of traffic and crash data were retrieved from existing databases from the Florida Department of Transportation. With these data, the effect of sample size criterion on calibration factor estimates was first studied using a sensitivity analysis. The results showed that the minimum sample sizes not only vary across different roadway facilities, but they are also significantly higher than those recommended in the HSM. In addition, results from paired sample t-tests showed that calibration factors in Florida need to be updated annually. To identify influential variables affecting the calibration factors for roadway segments, the variables were prioritized by combining the results from three different methods: negative binomial regression, random forests, and boosted regression trees. Only a few variables were found to explain most of the variation in the crash data. Traffic volume was consistently found to be the most influential. In addition, roadside object density, major and minor commercial driveway densities, and minor residential driveway density were also identified as influential variables.

Internet of Things collaborativo: progettazione ed analisi di una piattaforma di aggregazione di dati sensoristici

Internet ha rivoluzionato il modo di comunicare degli individui. Siamo testimoni della nascita e dello sviluppo di un'era caratterizzata dalla disponibilità di informazione libera e accessibile a tutti. Negli ultimi anni grazie alla diffusione di smartphone, tablet e altre tipologie di dispositivi connessi, è cambiato il fulcro dell'innovazione spostandosi dalle persone agli oggetti. E' così che nasce il concetto di Internet of Things, termine usato per descrivere la rete di comunicazione creata tra i diversi dispositivi connessi ad Internet e capaci di interagire in autonomia. Gli ambiti applicativi dell'Internet of Things spaziano dalla domotica alla sanità, dall'environmental monitoring al concetto di smart cities e così via. L'obiettivo principale di tale disciplina è quello di migliorare la vita delle persone grazie a sistemi che siano in grado di interagire senza aver bisogno dell'intervento dell'essere umano. Proprio per la natura eterogenea della disciplina e in relazione ai diversi ambiti applicativi, nell'Internet of Things si può incorrere in problemi derivanti dalla presenza di tecnologie differenti o di modalità eterogenee di memorizzazione dei dati. A questo proposito viene introdotto il concetto di Internet of Things collaborativo, termine che indica l'obiettivo di realizzare applicazioni che possano garantire interoperabilità tra i diversi ecosistemi e tra le diverse fonti da cui l'Internet of Things attinge, sfruttando la presenza di piattaforme di pubblicazione di Open Data. L'obiettivo di questa tesi è stato quello di creare un sistema per l'aggregazione di dati da due piattaforme, ThingSpeak e Sparkfun, con lo scopo di unificarli in un unico database ed estrarre informazioni significative dai dati tramite due tecniche di Data Mining: il Dictionary Learning e l'Affinity Propagation. Vengono illustrate le due metodologie che rientrano rispettivamente tra le tecniche di classificazione e di clustering.