Computing resources sensitive parallelization of neural neworks for large scale diabetes data modelling, diagnosis and prediction

This thesis was submitted for the degree of Doctor of Philosophy and awarded by Brunel University.Diabetes has become one of the most severe deceases due to an increasing number of diabetes patients globally. A large amount of digital data on diabetes has been collected through various channels. How to utilize these data sets to help doctors to make a decision on diagnosis, treatment and prediction of diabetic patients poses many challenges to the research community. The thesis investigates mathematical models with a focus on neural networks for large scale diabetes data modelling and analysis by utilizing modern computing technologies such as grid computing and cloud computing. These computing technologies provide users with an inexpensive way to have access to extensive computing resources over the Internet for solving data and computationally intensive problems. This thesis evaluates the performance of seven representative machine learning techniques in classification of diabetes data and the results show that neural network produces the best accuracy in classification but incurs high overhead in data training. As a result, the thesis develops MRNN, a parallel neural network model based on the MapReduce programming model which has become an enabling technology in support of data intensive applications in the clouds. By partitioning the diabetic data set into a number of equally sized data blocks, the workload in training is distributed among a number of computing nodes for speedup in data training. MRNN is first evaluated in small scale experimental environments using 12 mappers and subsequently is evaluated in large scale simulated environments using up to 1000 mappers. Both the experimental and simulations results have shown the effectiveness of MRNN in classification, and its high scalability in data training. MapReduce does not have a sophisticated job scheduling scheme for heterogonous computing environments in which the computing nodes may have varied computing capabilities. For this purpose, this thesis develops a load balancing scheme based on genetic algorithms with an aim to balance the training workload among heterogeneous computing nodes. The nodes with more computing capacities will receive more MapReduce jobs for execution. Divisible load theory is employed to guide the evolutionary process of the genetic algorithm with an aim to achieve fast convergence. The proposed load balancing scheme is evaluated in large scale simulated MapReduce environments with varied levels of heterogeneity using different sizes of data sets. All the results show that the genetic algorithm based load balancing scheme significantly reduce the makespan in job execution in comparison with the time consumed without load balancing.This work is funded by the EPSRC and China Market Association

Discovery of drug-omics associations in type 2 diabetes with generative deep-learning models.

The application of multiple omics technologies in biomedical cohorts has the potential to reveal patient-level disease characteristics and individualized response to treatment. However, the scale and heterogeneous nature of multi-modal data makes integration and inference a non-trivial task. We developed a deep-learning-based framework, multi-omics variational autoencoders (MOVE), to integrate such data and applied it to a cohort of 789 people with newly diagnosed type 2 diabetes with deep multi-omics phenotyping from the DIRECT consortium. Using in silico perturbations, we identified drug-omics associations across the multi-modal datasets for the 20 most prevalent drugs given to people with type 2 diabetes with substantially higher sensitivity than univariate statistical tests. From these, we among others, identified novel associations between metformin and the gut microbiota as well as opposite molecular responses for the two statins, simvastatin and atorvastatin. We used the associations to quantify drug-drug similarities, assess the degree of polypharmacy and conclude that drug effects are distributed across the multi-omics modalities. [Abstract copyright: © 2023. The Author(s).

Ontology-based personalized performance evaluation and dietary recommendation for weightlifting.

Studies in weightlifting have been characterized by unclear results and information paucity, mainly due to the lack of information sharing between athletes, coaches, biomechanists, physiologists and nutritionists. Becoming successful in weightlifting performance requires a unique physiological and biomechanics profile based on a distinctive combination of muscular strength, muscular power, flexibility, and lifting technique. An effective training which is carefully designed and monitored, is needed for accomplishment of consistent high performance. While it takes years of dedicated training, diet is also critical as optimal nutrition is essential for peak performance. Nutritional misinformation can do as much harm to ambitious athletes as good nutrition can help. In spite of several studies on nutrition guidelines for weightlifting training and competition as well as on design and implementation of weightlifting training programs, to the best of authors' knowledge, there is no attempt to semantically model the whole "training-diet-competition" cycle by integrating training, biomechanics, and nutrition domains.This study aims to conceive and design an ontology-enriched knowledge model to guide and support the implementation of "Recommender system of workout and nutrition forweightlifters". In doing so, it will propose: (i) understanding the weightlifting training system, from both qualitative and quantitative perspectives, following a modular ontology modeling, (ii) understanding the weightlifting diet following a modular ontology modeling, (iii) semantically integrating weightlifting and nutrition ontologies to mainly promote nutrition and weightlifting snatch exercises interoperability, (iv) extending modular ontology scope by mining rules while analyzing open data from the literature, and (v) devising reasoning capability toward an automated weightlifting "training-diet-competition" cycle supported by previously mined rulesTo support the above claims, two main artefacts were generated such as: (i) a weightliftingnutritional knowledge questionnaire to assess Thai weightlifting coaches' and athletes'knowledge regarding the weightlifting "training-diet-competition" cycle and (ii) a dual ontologyoriented weightlifting-nutrition knowledge model extended with mined rules and designed following a standard ontology development methodology.Studies in weightlifting have been characterized by unclear results and information paucity, mainly due to the lack of information sharing between athletes, coaches, biomechanists, physiologists and nutritionists. Becoming successful in weightlifting performance requires a unique physiological and biomechanics profile based on a distinctive combination of muscular strength, muscular power, flexibility, and lifting technique. An effective training which is carefully designed and monitored, is needed for accomplishment of consistent high performance. While it takes years of dedicated training, diet is also critical as optimal nutrition is essential for peak performance. Nutritional misinformation can do as much harm to ambitious athletes as good nutrition can help. In spite of several studies on nutrition guidelines for weightlifting training and competition as well as on design and implementation of weightlifting training programs, to the best of authors' knowledge, there is no attempt to semantically model the whole "training-diet-competition" cycle by integrating training, biomechanics, and nutrition domains.This study aims to conceive and design an ontology-enriched knowledge model to guide and support the implementation of "Recommender system of workout and nutrition forweightlifters". In doing so, it will propose: (i) understanding the weightlifting training system, from both qualitative and quantitative perspectives, following a modular ontology modeling, (ii) understanding the weightlifting diet following a modular ontology modeling, (iii) semantically integrating weightlifting and nutrition ontologies to mainly promote nutrition and weightlifting snatch exercises interoperability, (iv) extending modular ontology scope by mining rules while analyzing open data from the literature, and (v) devising reasoning capability toward an automated weightlifting "training-diet-competition" cycle supported by previously mined rulesTo support the above claims, two main artefacts were generated such as: (i) a weightliftingnutritional knowledge questionnaire to assess Thai weightlifting coaches' and athletes'knowledge regarding the weightlifting "training-diet-competition" cycle and (ii) a dual ontologyoriented weightlifting-nutrition knowledge model extended with mined rules and designed following a standard ontology development methodology

Distribution and Population Structure of the Invasive \u3ci\u3eNitellopsis obtusa\u3c/i\u3e (Desv. In Loisel.) J. Groves and Native Species of Characeae in the Northeast U.S.A.

Freshwater ecosystems are some of the most biologically diverse environments on Earth. Billions of humans rely on functioning freshwater ecosystems for drinking water and many other services. These ecosystems are increasingly threatened by human impacts including nutrient pollution, invasive species, and climate change. Here I contribute four research chapters that investigate freshwater diversity and ecosystem threats using the Characeae, a family of freshwater green macroalgae, as a study system. Characeae are a diverse and ancient group with more than 500 extant species and a fossil history spanning at least 250 million years. These algae are macrophytes in freshwater ecosystems, and serve important roles in stabilizing sediment, sequestering nutrients, and providing forage for fish and waterfowl. Although most Characeae are considered beneficial in freshwater ecosystems, one species, Nitellopsis obtusa (Desv. in Loisel.) J. Groves, has been identified as an invasive species in North America. In the first research chapter (Chapter 2) a systematic survey of 390 sites across New York state was conducted to discover new populations of Nitellopsis obtusa and confirm the known distribution of this invasive species. In the third chapter the survey was extended to include New England, for a total of 740 sites, from which species distribution models were constructed. These models demonstrated that water chemistry variables can predict Characeae habitat, and that species can be classified as specialist species, occurring in a narrow chemical niche, while others can be classified as generalist species that occur broadly across the region. Scenarios simulating increased nutrient pollution and future climate change were explored with some species predicted to increase in range and other species predicted to be extirpated from the region. Nitellopsis obtusa was found to be a hard water specialist, occurring at sites with elevated levels of calcium. Models found the niche of Nitellopsis obtusa similar to Chara contraria, a native species whose distribution can be used to identify sites that may be susceptible to Nitellopsis obtusa invasion. In the fourth chapter the fully sequenced and annotated organellar genomes of Nitellopsis obtusa are presented with an analysis of the genetic patterns of invasion. The chloroplast genome was more variable than the mitochondrial genome, and both genomes showed that samples in the invasive range were nearly identical, evidence of a single introduction event. Invasive samples clustered most closely but were not identical to samples from Western Europe, specifically France. Intra-individual polymorphism of the mitochondrial genome was detected and PacBio sequencing indicated that polymorphism likely arises from transfer of mitochondrial regions to the nuclear genome. In the fifth chapter a draft nuclear genome of Nitellopsis obtusa is presented and used to determine whether rapid adaptation in the invasive range gave rise to a more successful invasive genotype. The genome of Nitellopsis obtusa was estimated by kmer counting to be 2.5-5 Gb. A highly fragmented assembly of 2.3Gb was achieved. Double digest restriction site associated DNA sequencing (ddRAD) of individuals across the native and invasive range was unable to detect a signal of differentiation in putative adaptive genes, possibly due to cross-contamination during the pooling step of library construction. The results of these studies provide insights relevant for freshwater conservation and invasive species outreach and management

Computing resources sensitive parallelization of neural neworks for large scale diabetes data modelling, diagnosis and prediction

Diabetes has become one of the most severe deceases due to an increasing number of diabetes patients globally. A large amount of digital data on diabetes has been collected through various channels. How to utilize these data sets to help doctors to make a decision on diagnosis, treatment and prediction of diabetic patients poses many challenges to the research community. The thesis investigates mathematical models with a focus on neural networks for large scale diabetes data modelling and analysis by utilizing modern computing technologies such as grid computing and cloud computing. These computing technologies provide users with an inexpensive way to have access to extensive computing resources over the Internet for solving data and computationally intensive problems. This thesis evaluates the performance of seven representative machine learning techniques in classification of diabetes data and the results show that neural network produces the best accuracy in classification but incurs high overhead in data training. As a result, the thesis develops MRNN, a parallel neural network model based on the MapReduce programming model which has become an enabling technology in support of data intensive applications in the clouds. By partitioning the diabetic data set into a number of equally sized data blocks, the workload in training is distributed among a number of computing nodes for speedup in data training. MRNN is first evaluated in small scale experimental environments using 12 mappers and subsequently is evaluated in large scale simulated environments using up to 1000 mappers. Both the experimental and simulations results have shown the effectiveness of MRNN in classification, and its high scalability in data training. MapReduce does not have a sophisticated job scheduling scheme for heterogonous computing environments in which the computing nodes may have varied computing capabilities. For this purpose, this thesis develops a load balancing scheme based on genetic algorithms with an aim to balance the training workload among heterogeneous computing nodes. The nodes with more computing capacities will receive more MapReduce jobs for execution. Divisible load theory is employed to guide the evolutionary process of the genetic algorithm with an aim to achieve fast convergence. The proposed load balancing scheme is evaluated in large scale simulated MapReduce environments with varied levels of heterogeneity using different sizes of data sets. All the results show that the genetic algorithm based load balancing scheme significantly reduce the makespan in job execution in comparison with the time consumed without load balancing.EThOS - Electronic Theses Online ServiceEPSRCChina Market AssociationGBUnited Kingdo

Ontomet: Ontology Metadata Framework

Proper description of data, or metadata, is important to facilitate data sharing among Geospatial Information Communities. To avoid the production of arbitrary metadata annotations, communities agree that creating or adopting a metadata specification is needed. The specification is a document, such as the Geographic Metadata Standard (ISO 19115-2003), which provides a set of rules for the proper use of metadata elements. When a community is adopting a metadata specification it has two main concerns: 1) how can an existing specification be adopted, so that elements can be restricted and domain vocabularies be used? and 2) how can a metadata specification be mapped withanother one to achieve interoperability? The two aforementioned concerns are raised due to the fact that: 1) specifications lack domain-specific elements, 2) specifications have limited extensibility, 3) specifications do not always solve semantic heterogeneities and 4) methodologies to create crosswalks among specification have not been formalized. The main goal of this thesis is to present a feasible solution for these problems by providing a flexible environment to allow interoperations of formalized metadata specifications, extensions, crosswalks and domain vocabularies. The main contributions of this thesis are: 1) creation of an abstract model to represent metadata specifications, 2) development of a methodology to extend metadata specifications, called Dynamic Community Profile, and 3) formalization of semantic mappings to perform complex and contextual metadata crosswalks. These three main contributions are encapsulated in a framework called Ontology- Metadata Framework or ONTOMET. ONTOMET has seven components: metadata specification, a domain vocabulary, top-domain ontology, metadata crosswalk, Dynamic Community Profile and vocabulary mapper. A Dynamic Community Profile is a metadata specification, which extends other metadata specifications and infer terms from controlled vocabularies. Vocabulary mappers solve semantic heterogeneities that appear in domain vocabularies and a metadata crosswalk expresses the semantic mappings of two specifications. Also strategies to conceptualize metadata specifications and vocabularies, are presented. Stand alone JAVA Tools and Web programs were created that implemented the methodologies presented, to allow creation of metadata instances and mappings, as well as views of hydrologic vocabularies to facilitate discovery of knowledge and resources in the Web.Ph.D., Civil Engineering -- Drexel University, 200