Discovering lesser known molecular players and mechanistic patterns in Alzheimer's disease using an integrative disease modelling approach

Convergence of exponentially advancing technologies is driving medical research with life changing discoveries. On the contrary, repeated failures of high-profile drugs to battle Alzheimer's disease (AD) has made it one of the least successful therapeutic area. This failure pattern has provoked researchers to grapple with their beliefs about Alzheimer's aetiology. Thus, growing realisation that Amyloid-β and tau are not 'the' but rather 'one of the' factors necessitates the reassessment of pre-existing data to add new perspectives. To enable a holistic view of the disease, integrative modelling approaches are emerging as a powerful technique. Combining data at different scales and modes could considerably increase the predictive power of the integrative model by filling biological knowledge gaps. However, the reliability of the derived hypotheses largely depends on the completeness, quality, consistency, and context-specificity of the data. Thus, there is a need for agile methods and approaches that efficiently interrogate and utilise existing public data. This thesis presents the development of novel approaches and methods that address intrinsic issues of data integration and analysis in AD research. It aims to prioritise lesser-known AD candidates using highly curated and precise knowledge derived from integrated data. Here much of the emphasis is put on quality, reliability, and context-specificity. This thesis work showcases the benefit of integrating well-curated and disease-specific heterogeneous data in a semantic web-based framework for mining actionable knowledge. Furthermore, it introduces to the challenges encountered while harvesting information from literature and transcriptomic resources. State-of-the-art text-mining methodology is developed to extract miRNAs and its regulatory role in diseases and genes from the biomedical literature. To enable meta-analysis of biologically related transcriptomic data, a highly-curated metadata database has been developed, which explicates annotations specific to human and animal models. Finally, to corroborate common mechanistic patterns — embedded with novel candidates — across large-scale AD transcriptomic data, a new approach to generate gene regulatory networks has been developed. The work presented here has demonstrated its capability in identifying testable mechanistic hypotheses containing previously unknown or emerging knowledge from public data in two major publicly funded projects for Alzheimer's, Parkinson's and Epilepsy diseases

Knowledge and Reasoning for Image Understanding

abstract: Image Understanding is a long-established discipline in computer vision, which encompasses a body of advanced image processing techniques, that are used to locate (“where”), characterize and recognize (“what”) objects, regions, and their attributes in the image. However, the notion of “understanding” (and the goal of artificial intelligent machines) goes beyond factual recall of the recognized components and includes reasoning and thinking beyond what can be seen (or perceived). Understanding is often evaluated by asking questions of increasing difficulty. Thus, the expected functionalities of an intelligent Image Understanding system can be expressed in terms of the functionalities that are required to answer questions about an image. Answering questions about images require primarily three components: Image Understanding, question (natural language) understanding, and reasoning based on knowledge. Any question, asking beyond what can be directly seen, requires modeling of commonsense (or background/ontological/factual) knowledge and reasoning. Knowledge and reasoning have seen scarce use in image understanding applications. In this thesis, we demonstrate the utilities of incorporating background knowledge and using explicit reasoning in image understanding applications. We first present a comprehensive survey of the previous work that utilized background knowledge and reasoning in understanding images. This survey outlines the limited use of commonsense knowledge in high-level applications. We then present a set of vision and reasoning-based methods to solve several applications and show that these approaches benefit in terms of accuracy and interpretability from the explicit use of knowledge and reasoning. We propose novel knowledge representations of image, knowledge acquisition methods, and a new implementation of an efficient probabilistic logical reasoning engine that can utilize publicly available commonsense knowledge to solve applications such as visual question answering, image puzzles. Additionally, we identify the need for new datasets that explicitly require external commonsense knowledge to solve. We propose the new task of Image Riddles, which requires a combination of vision, and reasoning based on ontological knowledge; and we collect a sufficiently large dataset to serve as an ideal testbed for vision and reasoning research. Lastly, we propose end-to-end deep architectures that can combine vision, knowledge and reasoning modules together and achieve large performance boosts over state-of-the-art methods.Dissertation/ThesisDoctoral Dissertation Computer Science 201

Computational Proteomics Using Network-Based Strategies

This thesis examines the productive application of networks towards proteomics, with a specific biological focus on liver cancer. Contempory proteomics (shot- gun) is plagued by coverage and consistency issues. These can be resolved via network-based approaches. The application of 3 classes of network-based approaches are examined: A traditional cluster based approach termed Proteomics Expansion Pipeline), a generalization of PEP termed Maxlink and a feature-based approach termed Proteomics Signature Profiling. PEP is an improvement on prevailing cluster-based approaches. It uses a state- of-the-art cluster identification algorithm as well as network-cleaning approaches to identify the critical network regions indicated by the liver cancer data set. The top PARP1 associated-cluster was identified and independently validated. Maxlink allows identification of undetected proteins based on the number of links to identified differential proteins. It is more sensitive than PEP due to more relaxed requirements. Here, the novel roles of ARRB1/2 and ACTB are identified and discussed in the context of liver cancer. Both PEP and Maxlink are unable to deal with consistency issues, PSP is the first method able to deal with both, and is termed feature-based since the network- based clusters it uses are predicted independently of the data. It is also capable of using real complexes or predicted pathway subnets. By combining pathways and complexes, a novel basis of liver cancer progression implicating nucleotide pool imbalance aggravated by mutations of key DNA repair complexes was identified. Finally, comparative evaluations suggested that pure network-based methods are vastly outperformed by feature-based network methods utilizing real complexes. This is indicative that the quality of current networks are insufficient to provide strong biological rigor for data analysis, and should be carefully evaluated before further validations.Open Acces

Interactive Machine Learning with Applications in Health Informatics

Recent years have witnessed unprecedented growth of health data, including millions of biomedical research publications, electronic health records, patient discussions on health forums and social media, fitness tracker trajectories, and genome sequences. Information retrieval and machine learning techniques are powerful tools to unlock invaluable knowledge in these data, yet they need to be guided by human experts. Unlike training machine learning models in other domains, labeling and analyzing health data requires highly specialized expertise, and the time of medical experts is extremely limited. How can we mine big health data with little expert effort? In this dissertation, I develop state-of-the-art interactive machine learning algorithms that bring together human intelligence and machine intelligence in health data mining tasks. By making efficient use of human expert's domain knowledge, we can achieve high-quality solutions with minimal manual effort. I first introduce a high-recall information retrieval framework that helps human users efficiently harvest not just one but as many relevant documents as possible from a searchable corpus. This is a common need in professional search scenarios such as medical search and literature review. Then I develop two interactive machine learning algorithms that leverage human expert's domain knowledge to combat the curse of "cold start" in active learning, with applications in clinical natural language processing. A consistent empirical observation is that the overall learning process can be reliably accelerated by a knowledge-driven "warm start", followed by machine-initiated active learning. As a theoretical contribution, I propose a general framework for interactive machine learning. Under this framework, a unified optimization objective explains many existing algorithms used in practice, and inspires the design of new algorithms.PHDComputer Science & EngineeringUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttps://deepblue.lib.umich.edu/bitstream/2027.42/147518/1/raywang_1.pd

From Social Data Mining to Forecasting Socio-Economic Crisis

Socio-economic data mining has a great potential in terms of gaining a better understanding of problems that our economy and society are facing, such as financial instability, shortages of resources, or conflicts. Without large-scale data mining, progress in these areas seems hard or impossible. Therefore, a suitable, distributed data mining infrastructure and research centers should be built in Europe. It also appears appropriate to build a network of Crisis Observatories. They can be imagined as laboratories devoted to the gathering and processing of enormous volumes of data on both natural systems such as the Earth and its ecosystem, as well as on human techno-socio-economic systems, so as to gain early warnings of impending events. Reality mining provides the chance to adapt more quickly and more accurately to changing situations. Further opportunities arise by individually customized services, which however should be provided in a privacy-respecting way. This requires the development of novel ICT (such as a self- organizing Web), but most likely new legal regulations and suitable institutions as well. As long as such regulations are lacking on a world-wide scale, it is in the public interest that scientists explore what can be done with the huge data available. Big data do have the potential to change or even threaten democratic societies. The same applies to sudden and large-scale failures of ICT systems. Therefore, dealing with data must be done with a large degree of responsibility and care. Self-interests of individuals, companies or institutions have limits, where the public interest is affected, and public interest is not a sufficient justification to violate human rights of individuals. Privacy is a high good, as confidentiality is, and damaging it would have serious side effects for society.Comment: 65 pages, 1 figure, Visioneer White Paper, see http://www.visioneer.ethz.c