Overlap Matrix Completion for Predicting Drug-Associated Indications

Identification of potential drug-associated indications is critical for either approved or novel drugs in drug repositioning. Current computational methods based on drug similarity and disease similarity have been developed to predict drug-disease associations. When more reliable drug- or disease-related information becomes available and is integrated, the prediction precision can be continuously improved. However, it is a challenging problem to effectively incorporate multiple types of prior information, representing different characteristics of drugs and diseases, to identify promising drug-disease associations. In this study, we propose an overlap matrix completion (OMC) for bilayer networks (OMC2) and tri-layer networks (OMC3) to predict potential drug-associated indications, respectively. OMC is able to efficiently exploit the underlying low-rank structures of the drug-disease association matrices. In OMC2, first of all, we construct one bilayer network from drug-side aspect and one from disease-side aspect, and then obtain their corresponding block adjacency matrices. We then propose the OMC2 algorithm to fill out the values of the missing entries in these two adjacency matrices, and predict the scores of unknown drug-disease pairs. Moreover, we further extend OMC2 to OMC3 to handle tri-layer networks. Computational experiments on various datasets indicate that our OMC methods can effectively predict the potential drug-disease associations. Compared with the other state-of-the-art approaches, our methods yield higher prediction accuracy in 10-fold cross-validation and de novo experiments. In addition, case studies also confirm the effectiveness of our methods in identifying promising indications for existing drugs in practical applications

Intelligent Fusion of Structural and Citation-Based Evidence for Text Classification

This paper investigates how citation-based information and structural content (e.g., title, abstract) can be combined to improve classification of text documents into predefined categories. We evaluate different measures of similarity, five derived from the citation structure of the collection, and three measures derived from the structural content, and determine how they can be fused to improve classification effectiveness. To discover the best fusion framework, we apply Genetic Programming (GP) techniques. Our empirical experiments using documents from the ACM digital library and the ACM classification scheme show that we can discover similarity functions that work better than any evidence in isolation and whose combined performance through a simple majority voting is comparable to that of Support Vector Machine classifiers

Data Scarcity in Event Analysis and Abusive Language Detection

Lack of data is almost always the cause of the suboptimal performance of neural networks. Even though data scarce scenarios can be simulated for any task by assuming limited access to training data, we study two problem areas where data scarcity is a practical challenge: event analysis and abusive content detection} Journalists, social scientists and political scientists need to retrieve and analyze event mentions in unstructured text to compute useful statistical information to understand society. We claim that it is hard to specify information need about events using keyword-based representation and propose a Query by Example (QBE) setting for event retrieval. In the QBE setting, we assume that there are a few example sentences mentioning the event class a user is interested in and we aim to retrieve relevant events using only the examples as a query. Traditional event detection approaches are not applicable in this setting as event detection datasets are constructed based on pre-defined schemas which limits them to a small set of event and event-argument types. Moreover, the amount of annotated data in event detection datasets is limited that only allows us to build a retrieval corpus for evaluation. Thus we assume that there are no relevance judgments to train an event retrieval model -- except for the few examples of a specific event type. We create three QBE evaluation settings from three event detection datasets: PoliceKilling, ACE, and IndiaPoliceEvents. For the PoliceKilling dataset, where a relevant sentence describes a police killing event, we show that a query model constructed from the NLP features extracted from the few given examples is effective compared to event detection baselines. For the ACE dataset, where there are thirty-three types of events, we construct a QBE setting for each type and show that a sentence embedding approach effectively transfers for event matching. Finally, we conducted a unified evaluation of all three datasets using the sentence-embedding-based model and showed that it outperforms strong baselines. We further examine the effect of data scarcity in abusive language detection. We first study a specific type of abusive language -- hate speech. Neural hate speech detection models trained from one dataset poorly generalize to another dataset from a different domain. This is because characteristics of hate speech vary based on racial and cultural aspects. Our data scarcity scenario assumes that we have a hate speech dataset from a domain and it needs to generalize to a test set from another domain using the unlabeled data from the test domain only. Thus we assume zero target domain data in this scenario. To tackle the data scarcity, we propose an unsupervised domain adaptation approach to augment labeled data for hate speech detection. We evaluate the approach with three different models (character CNNs, BiLSTMs, and BERT) on three different collections. We show our approach improves Area under the Precision/Recall curve by as much as 42% and recall by as much as 278%, with no loss (and in some cases a significant gain) in precision. Finally, we examine the cross-lingual abusive language detection problem. Abusive language is a superclass of hate speech that includes profanity, aggression, offensiveness, cyberbullying, toxicity, and hate speech itself. There is a large collection of abusive language detection datasets in English such as Jigsaw. For other languages there exist datasets for abusive language detection but with very limited data. We propose a cross-lingual transfer learning approach to learn an effective neural abusive language classifier for such low-resource languages with help from a dataset from a resource-rich language. The framework is based on a nearest-neighbor architecture and is thus interpretable by design. It is a modern instantiation of the classic k-nearest neighbor model, as we use transformer representations in all its components. Unlike prior work on neighborhood-based approaches, we encode the neighborhood information based on query-neighbor interactions. We propose two encoding schemes and show their effectiveness using both qualitative and quantitative analyses. Our evaluation results on eight languages from two different datasets for abusive language detection show sizable improvements in F1 over strong baselines

A New Web Search Engine with Learning Hierarchy

Most of the existing web search engines (such as Google and Bing) are in the form of keyword-based search. Typically, after the user issues a query with the keywords, the search engine will return a flat list of results. When the query issued by the user is related to a topic, only the keyword matching may not accurately retrieve the whole set of webpages in that topic. On the other hand, there exists another type of search system, particularly in e-Commerce web- sites, where the user can search in the categories of different faceted hierarchies (e.g., product types and price ranges). Is it possible to integrate the two types of search systems and build a web search engine with a topic hierarchy? The main diffculty is how to classify the vast number of webpages on the Internet into the topic hierarchy. In this thesis, we will leverage machine learning techniques to automatically classify webpages into the categories in our hierarchy, and then utilize the classification results to build the new search engine SEE. The experimental results demonstrate that SEE can achieve better search results than the traditional keyword-based search engine in most of the queries, particularly when the query is related to a topic. We also conduct a small-scale usability study which further verifies that SEE is a promising search engine. To further improve SEE, we also propose a new active learning framework with several novel strategies for hierarchical classification

Introducing linked open data in graph-based recommender systems

Thanks to the recent spread of the Linked Open Data (LOD) initiative, a huge amount of machine-readable knowledge encoded as RDF statements is today available in the so-called LOD cloud. Accordingly, a big effort is now spent to investigate to what extent such information can be exploited to develop new knowledge-based services or to improve the effectiveness of knowledge-intensive platforms as Recommender Systems (RS). To this end, in this article we study the impact of the exogenous knowledge coming from the LOD cloud on the overall performance of a graph-based recommendation framework. Specifically, we propose a methodology to automatically feed a graph-based RS with features gathered from the LOD cloud and we analyze the impact of several widespread feature selection techniques in such recommendation settings. The experimental evaluation, performed on three state-of-the-art datasets, provided several outcomes: first, information extracted from the LOD cloud can significantly improve the performance of a graph-based RS. Next, experiments showed a clear correlation between the choice of the feature selection technique and the ability of the algorithm to maximize specific evaluation metrics, as accuracy or diversity of the recommendations. Moreover, our graph-based algorithm fed with LOD-based features was able to overcome several baselines, as collaborative filtering and matrix factorization

Graph Embedding and Nonlinear Dimensionality Reduction

Traditionally, spectral methods such as principal component analysis (PCA) have been applied to many graph embedding and dimensionality reduction tasks. These methods aim to find low-dimensional representations of data that preserve its inherent structure. However, these methods often perform poorly when applied to data which does not lie exactly near a linear manifold. In this thesis, I present a set of novel graph embedding algorithms which extend spectral methods, allowing graph representations of high-dimensional data or networks to be accurately embedded in a low-dimensional space. I first propose minimum volume embedding (MVE) which, like other leading dimensionality reduction algorithms, first encodes the high-dimensional data as a nearest-neighbor graph, where the edge weights between neighbors correspond to kernel values between points, and then embeds this graph in a low-dimensional space. Next I present structure preserving embedding (SPE), an algorithm for embedding unweighted graphs where similarity between nodes is not known. SPE finds low-dimensional embeddings which explicitly preserve graph topology, meaning a connectivity algorithm, such as k-nearest neighbors, will recover the edges of the input graph from only the coordinates of the nodes after embedding. I further explore preserving graph structure during embedding, and find the concept applicable to dimensionality reduction, large-scale network visualization, and metric learning for link prediction. This thesis posits that simply preserving pairwise distances, as with many spectral methods, is insufficient for capturing the structure of many datasets and that preserving both local distances and graph topology is crucial for producing accurate low-dimensional representations of networks and high-dimensional data

Healthcare data mining from multi-source data

The "big data" challenge is changing the way we acquire, store, analyse, and draw conclusions from data. How we effectively and efficiently "mine" the data from possibly multiple sources and extract useful information is a critical question. Increasing research attention has been drawn to healthcare data mining, with an ultimate goal to improve the quality of care. The human body is complex and so too the data collected in treating it. Data noise that is often introduced via the collection process makes building Data Mining models a challenging task. This thesis focuses on the classification tasks of mining healthcare data, with the goal of improving the effectiveness of health risk prediction. In particular, we developed algorithms to address issues identified from real healthcare data, such as feature extraction, heterogeneity, label uncertainty, and large unlabeled data. The three main contributions of this research are as follows. First, we developed a new health index called Personal Health Index (PHI) that scores a person's health status based on the examination records of a given population. Second, we identified the key characteristics of the real datasets and issues that were associated with the data. Third, we developed classification algorithms to cope with those issues, particularly, the label uncertainty and large unlabeled data issues. This research takes one step forward towards scoring personal health based on mining increasingly large health records. Particularly, it pioneers exploring the mining of GHE data and tackles the associated challenges. It is our anticipation that in the near future, more robust data-mining-based health scoring systems will be available for healthcare professionals to understand people's health status and thus improve the quality of care