CART - a chemical annotation retrieval toolkit

MOTIVATION: Data on bioactivities of drug-like chemicals is rapidly accumulating in public repositories, creating new opportunities for research in computational systems pharmacology. However, integrative analysis of these data sets is difficult due to prevailing ambiguity between chemical names and identifiers and a lack of cross-references between databases. RESULTS: To address this challenge, we have developed CART, a Chemical Annotation Retrieval Toolkit. As a key functionality, it matches an input list of chemical names into a comprehensive reference space to assign unambiguous chemical identifiers. In this unified space, bioactivity annotations can be easily retrieved from databases covering a wide variety of chemical effects on biological systems. Subsequently, CART can determine annotations enriched in the input set of chemicals and display these in tabular format and interactive network visualizations, thereby facilitating integrative analysis of chemical bioactivity data

Molecular epidemiology of dengue viruses from complete genome sequences

The availability of the complete genetic blueprint of the dengue virus is essential in molecular epidemiological studies to uncover the role of the virus in dengue pathogenesis. During the course of this project, over two hundred complete genomes of the dengue virus were generated from clinical samples collected in three dengue-endemic Southeast Asian countries. In addition, a bioinformatics platform integrating a sequence database, sequence retrieval tools, sequence annotation data and a variety of analysis tools was developed for easy management, manipulation and analysis of dengue virus sequence data. Whereas previous studies have mostly focused on epidemiological events in the Americas and Thailand, sequence data recovered from dengue epidemics in Indonesia, Malaysia and Singapore in this study have uncovered some of the dengue virus diversity circulating in the region. The three countries appear to share similar pool of dengue viruses, with some viral lineages in sustained circulation since at least the 1970s. Sequencing of historical virus isolates prior to the 2004/2005 epidemics in Indonesia and Singapore revealed that adaptive viral evolution played little or no role in triggering those epidemics. Lastly, a method that utilised all available sequence data from Malaysia was devised to reconstruct the history of dengue virus in that country since the 1960s

Design and implementation of a cyberinfrastructure for RNA motif search, prediction and analysis

RNA secondary and tertiary structure motifs play important roles in cells. However, very few web servers are available for RNA motif search and prediction. In this dissertation, a cyberinfrastructure, named RNAcyber, capable of performing RNA motif search and prediction, is proposed, designed and implemented. The first component of RNAcyber is a web-based search engine, named RmotifDB. This web-based tool integrates an RNA secondary structure comparison algorithm with the secondary structure motifs stored in the Rfam database. With a user-friendly interface, RmotifDB provides the ability to search for ncRNA structure motifs in both structural and sequential ways. The second component of RNAcyber is an enhanced version of RmotifDB. This enhanced version combines data from multiple sources, incorporates a variety of well-established structure-based search methods, and is integrated with the Gene Ontology. To display RmotifDB’s search results, a software tool, called RSview, is developed. RSview is able to display the search results in a graphical manner. Finally, RNAcyber contains a web-based tool called Junction-Explorer, which employs a data mining method for predicting tertiary motifs in RNA junctions. Specifically, the tool is trained on solved RNA tertiary structures obtained from the Protein Data Bank, and is able to predict the configuration of coaxial helical stacks and families (topologies) in RNA junctions at the secondary structure level. Junction-Explorer employs several algorithms for motif prediction, including a random forest classification algorithm, a pseudoknot removal algorithm, and a feature ranking algorithm based on the gini impurity measure. A series of experiments including 10-fold cross- validation has been conducted to evaluate the performance of the Junction-Explorer tool. Experimental results demonstrate the effectiveness of the proposed algorithms and the superiority of the tool over existing methods. The RNAcyber infrastructure is fully operational, with all of its components accessible on the Internet

Doctor of Philosophy

dissertationOver 40 years ago, the first computer simulation of a protein was reported: the atomic motions of a 58 amino acid protein were simulated for few picoseconds. With today's supercomputers, simulations of large biomolecular systems with hundreds of thousands of atoms can reach biologically significant timescales. Through dynamics information biomolecular simulations can provide new insights into molecular structure and function to support the development of new drugs or therapies. While the recent advances in high-performance computing hardware and computational methods have enabled scientists to run longer simulations, they also created new challenges for data management. Investigators need to use local and national resources to run these simulations and store their output, which can reach terabytes of data on disk. Because of the wide variety of computational methods and software packages available to the community, no standard data representation has been established to describe the computational protocol and the output of these simulations, preventing data sharing and collaboration. Data exchange is also limited due to the lack of repositories and tools to summarize, index, and search biomolecular simulation datasets. In this dissertation a common data model for biomolecular simulations is proposed to guide the design of future databases and APIs. The data model was then extended to a controlled vocabulary that can be used in the context of the semantic web. Two different approaches to data management are also proposed. The iBIOMES repository offers a distributed environment where input and output files are indexed via common data elements. The repository includes a dynamic web interface to summarize, visualize, search, and download published data. A simpler tool, iBIOMES Lite, was developed to generate summaries of datasets hosted at remote sites where user privileges and/or IT resources might be limited. These two informatics-based approaches to data management offer new means for the community to keep track of distributed and heterogeneous biomolecular simulation data and create collaborative networks

When Silver Is As Good As Gold: Using Weak Supervision to Train Machine Learning Models on Social Media Data

Over the last decade, advances in machine learning have led to an exponential growth in artificial intelligence i.e., machine learning models capable of learning from vast amounts of data to perform several tasks such as text classification, regression, machine translation, speech recognition, and many others. While massive volumes of data are available, due to the manual curation process involved in the generation of training datasets, only a percentage of the data is used to train machine learning models. The process of labeling data with a ground-truth value is extremely tedious, expensive, and is the major bottleneck of supervised learning. To curtail this, the theory of noisy learning can be employed where data labeled through heuristics, knowledge bases and weak classifiers can be utilized for training, instead of data obtained through manual annotation. The assumption here is that a large volume of training data, which contains noise and acquired through an automated process, can compensate for the lack of manual labels. In this study, we utilize heuristic based approaches to create noisy silver standard datasets. We extensively tested the theory of noisy learning on four different applications by training several machine learning models using the silver standard dataset with several sample sizes and class imbalances and tested the performance using a gold standard dataset. Our evaluations on the four applications indicate the success of silver standard datasets in identifying a gold standard dataset. We conclude the study with evidence that noisy social media data can be utilized for weak supervisio

Learning and Leveraging Structured Knowledge from User-Generated Social Media Data

Knowledge has long been a crucial element in Artificial Intelligence (AI), which can be traced back to knowledge-based systems, or expert systems, in the 1960s. Knowledge provides contexts to facilitate machine understanding and improves the explainability and performance of many semantic-based applications. The acquisition of knowledge is, however, a complex step, normally requiring much effort and time from domain experts. In machine learning as one key domain of AI, the learning and leveraging of structured knowledge, such as ontologies and knowledge graphs, have become popular in recent years with the advent of massive user-generated social media data. The main hypothesis in this thesis is therefore that a substantial amount of useful knowledge can be derived from user-generated social media data. A popular, common type of social media data is social tagging data, accumulated from users' tagging in social media platforms. Social tagging data exhibit unstructured characteristics, including noisiness, flatness, sparsity, incompleteness, which prevent their efficient knowledge discovery and usage. The aim of this thesis is thus to learn useful structured knowledge from social media data regarding these unstructured characteristics. Several research questions have then been formulated related to the hypothesis and the research challenges. A knowledge-centred view has been considered throughout this thesis: knowledge bridges the gap between massive user-generated data to semantic-based applications. The study first reviews concepts related to structured knowledge, then focuses on two main parts, learning structured knowledge and leveraging structured knowledge from social tagging data. To learn structured knowledge, a machine learning system is proposed to predict subsumption relations from social tags. The main idea is to learn to predict accurate relations with features, generated with probabilistic topic modelling and founded on a formal set of assumptions on deriving subsumption relations. Tag concept hierarchies can then be organised to enrich existing Knowledge Bases (KBs), such as DBpedia and ACM Computing Classification Systems. The study presents relation-level evaluation, ontology-level evaluation, and the novel, Knowledge Base Enrichment based evaluation, and shows that the proposed approach can generate high quality and meaningful hierarchies to enrich existing KBs. To leverage structured knowledge of tags, the research focuses on the task of automated social annotation and propose a knowledge-enhanced deep learning model. Semantic-based loss regularisation has been proposed to enhance the deep learning model with the similarity and subsumption relations between tags. Besides, a novel, guided attention mechanism, has been proposed to mimic the users' behaviour of reading the title before digesting the content for annotation. The integrated model, Joint Multi-label Attention Network (JMAN), significantly outperformed the state-of-the-art, popular baseline methods, with consistent performance gain of the semantic-based loss regularisers on several deep learning models, on four real-world datasets. With the careful treatment of the unstructured characteristics and with the novel probabilistic and neural network based approaches, useful knowledge can be learned from user-generated social media data and leveraged to support semantic-based applications. This validates the hypothesis of the research and addresses the research questions. Future studies are considered to explore methods to efficiently learn and leverage other various types of structured knowledge and to extend current approaches to other user-generated data

Using machine learning to support better and intelligent visualisation for genomic data

Massive amounts of genomic data are created for the advent of Next Generation Sequencing technologies. Great technological advances in methods of characterising the human diseases, including genetic and environmental factors, make it a great opportunity to understand the diseases and to find new diagnoses and treatments. Translating medical data becomes more and more rich and challenging. Visualisation can greatly aid the processing and integration of complex data. Genomic data visual analytics is rapidly evolving alongside with advances in high-throughput technologies such as Artificial Intelligence (AI), and Virtual Reality (VR). Personalised medicine requires new genomic visualisation tools, which can efficiently extract knowledge from the genomic data effectively and speed up expert decisions about the best treatment of an individual patient’s needs. However, meaningful visual analysis of such large genomic data remains a serious challenge. Visualising these complex genomic data requires not only simply plotting of data but should also lead to better decisions. Machine learning has the ability to make prediction and aid in decision-making. Machine learning and visualisation are both effective ways to deal with big data, but they focus on different purposes. Machine learning applies statistical learning techniques to automatically identify patterns in data to make highly accurate prediction, while visualisation can leverage the human perceptual system to interpret and uncover hidden patterns in big data. Clinicians, experts and researchers intend to use both visualisation and machine learning to analyse their complex genomic data, but it is a serious challenge for them to understand and trust machine learning models in the serious medical industry. The main goal of this thesis is to study the feasibility of intelligent and interactive visualisation which combined with machine learning algorithms for medical data analysis. A prototype has also been developed to illustrate the concept that visualising genomics data from childhood cancers in meaningful and dynamic ways could lead to better decisions. Machine learning algorithms are used and illustrated during visualising the cancer genomic data in order to provide highly accurate predictions. This research could open a new and exciting path to discovery for disease diagnostics and therapies

Health systems data interoperability and implementation

Objective The objective of this study was to use machine learning and health standards to address the problem of clinical data interoperability across healthcare institutions. Addressing this problem has the potential to make clinical data comparable, searchable and exchangeable between healthcare providers. Data sources Structured and unstructured data has been used to conduct the experiments in this study. The data was collected from two disparate data sources namely MIMIC-III and NHanes. The MIMIC-III database stored data from two electronic health record systems which are CareVue and MetaVision. The data stored in these systems was not recorded with the same standards; therefore, it was not comparable because some values were conflicting, while one system would store an abbreviation of a clinical concept, the other would store the full concept name and some of the attributes contained missing information. These few issues that have been identified make this form of data a good candidate for this study. From the identified data sources, laboratory, physical examination, vital signs, and behavioural data were used for this study. Methods This research employed a CRISP-DM framework as a guideline for all the stages of data mining. Two sets of classification experiments were conducted, one for the classification of structured data, and the other for unstructured data. For the first experiment, Edit distance, TFIDF and JaroWinkler were used to calculate the similarity weights between two datasets, one coded with the LOINC terminology standard and another not coded. Similar sets of data were classified as matches while dissimilar sets were classified as non-matching. Then soundex indexing method was used to reduce the number of potential comparisons. Thereafter, three classification algorithms were trained and tested, and the performance of each was evaluated through the ROC curve. Alternatively the second experiment was aimed at extracting patient’s smoking status information from a clinical corpus. A sequence-oriented classification algorithm called CRF was used for learning related concepts from the given clinical corpus. Hence, word embedding, random indexing, and word shape features were used for understanding the meaning in the corpus. Results Having optimized all the model’s parameters through the v-fold cross validation on a sampled training set of structured data ( ), out of 24 features, only ( 8) were selected for a classification task. RapidMiner was used to train and test all the classification algorithms. On the final run of classification process, the last contenders were SVM and the decision tree classifier. SVM yielded an accuracy of 92.5% when the and parameters were set to and . These results were obtained after more relevant features were identified, having observed that the classifiers were biased on the initial data. On the other side, unstructured data was annotated via the UIMA Ruta scripting language, then trained through the CRFSuite which comes with the CLAMP toolkit. The CRF classifier obtained an F-measure of 94.8% for “nonsmoker” class, 83.0% for “currentsmoker”, and 65.7% for “pastsmoker”. It was observed that as more relevant data was added, the performance of the classifier improved. The results show that there is a need for the use of FHIR resources for exchanging clinical data between healthcare institutions. FHIR is free, it uses: profiles to extend coding standards; RESTFul API to exchange messages; and JSON, XML and turtle for representing messages. Data could be stored as JSON format on a NoSQL database such as CouchDB, which makes it available for further post extraction exploration. Conclusion This study has provided a method for learning a clinical coding standard by a computer algorithm, then applying that learned standard to unstandardized data so that unstandardized data could be easily exchangeable, comparable and searchable and ultimately achieve data interoperability. Even though this study was applied on a limited scale, in future, the study would explore the standardization of patient’s long-lived data from multiple sources using the SHARPn open-sourced tools and data scaling platformsInformation ScienceM. Sc. (Computing

Integration and analysis of large scale data in chemical biology

much lower molecular weight than macromolecules like proteins or DNA. Small molecules are grouped into different families according to their physico-chemical or functional properties, and they can be either natural (like lipids) or synthetic (like drugs). Only a staggeringly low fraction of the small molecule universe has been characterize, and very little is known about it. For instance, we know that lipids can play the role of scaffolding and energy storage compounds, and that they differently compose biological membranes. However, we don’t know if it influences some biological functions, including protein recruitment to membranes and cellular transport. Chemical biology aims at utilizing chemicals in order to explore biological systems. Advances in synthesizing big chemical libraries as well as in high-throughput screenings have led to technologies capable of studying protein-lipid interactions at large scale and in physiological conditions. Therefore, answering such questions has become possible, but it presents many new computational challenges. For instance, establishing methods capable of automatically classifying interactions as binding or non-binding requiring a minimal interaction with human experts. Making use of unsupervised clustering methods to identify clusters of lipids and proteins exhibiting similar patterns and linking them to similar biological functions. To tackle these challenges, I have developed a computational pipeline performing a technical and functional analysis on the readouts produced by the high-throughput technology LiMA. Applied to a screen focusing on 94 proteins and 122 lipid combinations yielding more than 10,000 interactions, I have demonstrated that cooperativity was a key mechanism for membrane recruitment and that it could be applied to most PH domains. Furthermore, I have identified a conserved motif conferring PH domains the ability to be recruited to organellar membranes and which is linked to cellular transport functions. Two amino acids of this motif are found mutated in some human cancer, and we predicted and confirmed that these mutations could induce discrete changes in binding affinities in vitro and protein mis-localization in vivo. These results represent milestones in the field of protein-lipid interactions. While we are progressing toward a global understanding of protein-lipid interactions, data on the bioactivities of small molecules is accumulating at a tremendous speed. In vitro data on interactions with targets are complemented by other molecular and phenotypic readouts, such as gene expression profiles or toxicity readouts. The diversity of screening technologies accompanied by big efforts to collect the resulting data in public databases have created unprecedented opportunities for chemo-informatics work to integrate these data and make new inferences. For instance, is the protein target profile of a drug correlated with a given phenotype? Can we predict the side effects of a drug based on its toxicology readouts? In this context, I have developed CART: a computational platform with which we address major chemo-informatics challenges to answer such questions. CART integrates many resources covering molecular and phenotypical readouts, and annotates sets of chemical names with these integrated resources. CART includes state-of-the-art full-text search engine technologies in order to match chemical names at a very high speed and accuracy. Importantly, CART is a scalable resource that can cope with the increasing number of new chemical annotation resources, and therefore, constitutes a major contribution to chemical biology