Platelet Diagnostics:A novel liquid biomarker

The aim of this thesis is to find a novel liquid biomarker for the detection of cancer and to optimize treatment. The first chapter gives an introduction to the oncology biomarker field and focuses on platelets and their role in cancer. In part 1, we evaluate extracellular vesicles (EVs). EVs are small vesicles released by all types of cells, including tumor cells, into the circulation. They carry protein kinases and can be isolated from plasma. We demonstrate that AKT and ERK kinase protein levels in EVs reflect the cellular expression levels and treatment with kinase inhibitors alters their concentration, depending on the clinical response to the drug. Therefore, EVs may provide a promising biomarker biosource for monitoring of treatment responses. Part 2 starts with reviews describing the function and role of platelets in greater depth. Chapter 3 focusses on thrombocytogenesis and several biological processes in which platelets play a role. Furthermore, the RNA processing machineries harboured by platelets are discussed. Both chapter 3 and 4 evaluate the change platelets undergo after being exposed to tumor and its environment. The exchange of biomolecules with tumor cells results in educated platelets, so-called tumor educated platelets (TEPs). TEPs play a role in several hallmarks of cancer and have the ability to respond to systemic alterations making them an interesting biomarker. In chapter 5 the diagnostic potential of platelets is first discussed. We determine their potential by sequencing the RNA of 283 platelet samples, of which 228 are patients with cancer, and 55 are healthy controls. We reach an accuracy of 96%. Furthermore, we are able to pinpoint the location of the primary tumor with an accuracy of 71%. In part 3, our developed thromboSeq platform is taken to the next level. Several potential confounding factors are taken into account such as age and comorbidity. We show that particle-swarm optimization (PSO)-enhanced algorithms enable efficient selection of RNA biomarker panels. In a validation cohort we apply these algorithms to non-small-cell lung cancer and reach an accuracy of 88% in late stage (n=518) and early-stage 81% accuracy. Finally, in chapter 7 we describe our wet- and dry-lab protocols in detail. This includes platelet RNA isolation, mRNA amplification, and preparation for next-generation sequencing. The dry-lab protocol describes the automated FASTQ file pre-processing to quantified gene counts, quality controls, data normalization and correction, and swarm intelligence-enhanced support vector machine (SVM) algorithm development. Part 4 focuses on central nervous system (CNS) malignancies especially on glioblastoma. Chapter 8 gives an overview of the different liquid biomarkers for diffuse glioma, the most common primary CNS malignancy. In chapter 9 we assess the specificity of the platelet education due to glioblastoma by comparing the RNA profile of TEPs from glioblastoma patients with a neuroinflammatory disease and brain metastasis patients. This results in a detection accuracy of 80%. Secondly, analysis of patients with glioblastoma versus healthy controls in an independent validation series provide a detection accuracy of 95%. Furthermore, we describe the potential value of platelets as a monitoring biomarker for patients with glioma, distinguishing pseudoprogression from real tumor progression. In part 5 thromboSeq is applied to breast cancer diagnostics both as a screening tool in the general population and in a high risk population, BRCA mutated women. In chapter 11 we first apply our technique to an inflammatory condition, multiple sclerosis (MS). Platelet RNA is used as input for the development of a diagnostic MS classifier capable of detecting MS with 80% accuracy in the independent validation series. In the final part we conclude this thesis with a general discussion of the main findings and suggestions for future research

Ensemble deep learning: A review

Ensemble learning combines several individual models to obtain better generalization performance. Currently, deep learning models with multilayer processing architecture is showing better performance as compared to the shallow or traditional classification models. Deep ensemble learning models combine the advantages of both the deep learning models as well as the ensemble learning such that the final model has better generalization performance. This paper reviews the state-of-art deep ensemble models and hence serves as an extensive summary for the researchers. The ensemble models are broadly categorised into ensemble models like bagging, boosting and stacking, negative correlation based deep ensemble models, explicit/implicit ensembles, homogeneous /heterogeneous ensemble, decision fusion strategies, unsupervised, semi-supervised, reinforcement learning and online/incremental, multilabel based deep ensemble models. Application of deep ensemble models in different domains is also briefly discussed. Finally, we conclude this paper with some future recommendations and research directions

A Comprehensive Survey on Particle Swarm Optimization Algorithm and Its Applications

Particle swarm optimization (PSO) is a heuristic global optimization method, proposed originally by Kennedy and Eberhart in 1995. It is now one of the most commonly used optimization techniques. This survey presented a comprehensive investigation of PSO. On one hand, we provided advances with PSO, including its modifications (including quantum-behaved PSO, bare-bones PSO, chaotic PSO, and fuzzy PSO), population topology (as fully connected, von Neumann, ring, star, random, etc.), hybridization (with genetic algorithm, simulated annealing, Tabu search, artificial immune system, ant colony algorithm, artificial bee colony, differential evolution, harmonic search, and biogeography-based optimization), extensions (to multiobjective, constrained, discrete, and binary optimization), theoretical analysis (parameter selection and tuning, and convergence analysis), and parallel implementation (in multicore, multiprocessor, GPU, and cloud computing forms). On the other hand, we offered a survey on applications of PSO to the following eight fields: electrical and electronic engineering, automation control systems, communication theory, operations research, mechanical engineering, fuel and energy, medicine, chemistry, and biology. It is hoped that this survey would be beneficial for the researchers studying PSO algorithms

Genetic algorithm-neural network: feature extraction for bioinformatics data.

With the advance of gene expression data in the bioinformatics field, the questions which frequently arise, for both computer and medical scientists, are which genes are significantly involved in discriminating cancer classes and which genes are significant with respect to a specific cancer pathology. Numerous computational analysis models have been developed to identify informative genes from the microarray data, however, the integrity of the reported genes is still uncertain. This is mainly due to the misconception of the objectives of microarray study. Furthermore, the application of various preprocessing techniques in the microarray data has jeopardised the quality of the microarray data. As a result, the integrity of the findings has been compromised by the improper use of techniques and the ill-conceived objectives of the study. This research proposes an innovative hybridised model based on genetic algorithms (GAs) and artificial neural networks (ANNs), to extract the highly differentially expressed genes for a specific cancer pathology. The proposed method can efficiently extract the informative genes from the original data set and this has reduced the gene variability errors incurred by the preprocessing techniques. The novelty of the research comes from two perspectives. Firstly, the research emphasises on extracting informative features from a high dimensional and highly complex data set, rather than to improve classification results. Secondly, the use of ANN to compute the fitness function of GA which is rare in the context of feature extraction. Two benchmark microarray data have been taken to research the prominent genes expressed in the tumour development and the results show that the genes respond to different stages of tumourigenesis (i.e. different fitness precision levels) which may be useful for early malignancy detection. The extraction ability of the proposed model is validated based on the expected results in the synthetic data sets. In addition, two bioassay data have been used to examine the efficiency of the proposed model to extract significant features from the large, imbalanced and multiple data representation bioassay data

A Machine Learning Classification Framework for Early Prediction of Alzheimer’s Disease

People today, in addition to their concerns about getting old and having to go through watching themselves grow weak and wrinkly, are facing an increasing fear of dementia. There are around 47 million people affected by dementia worldwide and the cost associated with providing them health and social care support is estimated to reach 2 trillion by 2030 which is almost equivalent to the 18th largest economy in the world. The most common form of dementia with the highest costs in health and social care is Alzheimer’s disease, which gradually kills neurons and causes patients to lose loving memories, the ability to recognise family members, childhood memories, and even the ability to follow simple instructions. Alzheimer’s disease is irreversible, unstoppable and has no known cure. Besides being a calamity to affected patients, it is a great financial burden on health providers. Health care providers also face a challenge in diagnosing the disease as current methods used to diagnose Alzheimer’s disease rely on manual evaluations of a patient’s medical history and mental examinations such as the Mini-Mental State Examination. These diagnostic methods often give a false diagnosis and were designed to identify Alzheimer’s after stage two when the part of all symptoms are evident. The problem is that clinicians are unable to stop or control the progress of Alzheimer’s disease, because of a lack of knowledge on the patterns that triggered the development of the disease. In this thesis, we explored and investigated Alzheimer’s disease from a computational perspective to uncover different risk factors and present a strategic framework called Early Prediction of Alzheimer’s Disease Framework (EPADf) that would give a future prediction of early-onset Alzheimer’s disease. Following extensive background research that resulted in the formalisation of the framework concept, prediction approaches, and the concept of ranking the risk factors based on clinical instinct, knowledge and experience using mathematical reasoning, we carried out experiments to get further insight and investigate the disease further using machine learning models. In this study, we used machine learning models and conducted two classification experiments for early prediction of Alzheimer’s disease, and one ranking experiment to rank its risk factors by importance. Besides these experiments, we also presented two logical approaches to search for patterns in an Alzheimer’s dataset, and a ranking algorithm to rank Alzheimer’s disease risk factors based on clinical evaluation. For the classification experiments we used five different Machine Learning models; Random Forest (RF), Random Oracle Model (ROM), a hybrid model combined of Levenberg-Marquardt neural network and Random Forest, combined using Fischer discriminate analysis (H2), Linear Neural Networks (LNN), and Multi-layer Perceptron Model (MLP). These models were deployed on a de-identified multivariable patient’s data, provided by the ADNI (Alzheimer’s disease Neuroimaging Initiative), to illustrate the effective use of data analysis to investigate Alzheimer’s disease biological and behavioural risk factors. We found that the continues enhancement of patient’s data and the use of combined machine learning models can provide an early cost-effective prediction of Alzheimer’s disease, and help in extracting insightful information on the risk factors of the disease. Based on this work and findings we have developed the strategic framework (EPADf) which is discussed in more depth in this thesis

Deep learning of brain asymmetry digital biomarkers to support early diagnosis of cognitive decline and dementia

Early identification of degenerative processes in the human brain is essential for proper care and treatment. This may involve different instrumental diagnostic methods, including the most popular computer tomography (CT), magnetic resonance imaging (MRI) and positron emission tomography (PET) scans. These technologies provide detailed information about the shape, size, and function of the human brain. Structural and functional cerebral changes can be detected by computational algorithms and used to diagnose dementia and its stages (amnestic early mild cognitive impairment - EMCI, Alzheimer’s Disease - AD). They can help monitor the progress of the disease. Transformation shifts in the degree of asymmetry between the left and right hemispheres illustrate the initialization or development of a pathological process in the brain. In this vein, this study proposes a new digital biomarker for the diagnosis of early dementia based on the detection of image asymmetries and crosssectional comparison of NC (normal cognitively), EMCI and AD subjects. Features of brain asymmetries extracted from MRI of the ADNI and OASIS databases are used to analyze structural brain changes and machine learning classification of the pathology. The experimental part of the study includes results of supervised machine learning algorithms and transfer learning architectures of convolutional neural networks for distinguishing between cognitively normal subjects and patients with early or progressive dementia. The proposed pipeline offers a low-cost imaging biomarker for the classification of dementia. It can be potentially helpful to other brain degenerative disorders accompanied by changes in brain asymmetries

Evolutionary Computation

This book presents several recent advances on Evolutionary Computation, specially evolution-based optimization methods and hybrid algorithms for several applications, from optimization and learning to pattern recognition and bioinformatics. This book also presents new algorithms based on several analogies and metafores, where one of them is based on philosophy, specifically on the philosophy of praxis and dialectics. In this book it is also presented interesting applications on bioinformatics, specially the use of particle swarms to discover gene expression patterns in DNA microarrays. Therefore, this book features representative work on the field of evolutionary computation and applied sciences. The intended audience is graduate, undergraduate, researchers, and anyone who wishes to become familiar with the latest research work on this field

Imaging biomarkers extraction and classification for Prion disease

Prion diseases are a group of rare neurodegenerative conditions characterised by a high rate of progression and highly heterogeneous phenotypes. Whilst the most common form of prion disease occurs sporadically (sporadic Creutzfeldt-Jakob disease, sCJD), other forms are caused by inheritance of prion protein gene mutations or exposure to prions. To date, there are no accurate imaging biomarkers that can be used to predict the future diagnosis of a subject or to quantify the progression of symptoms over time. Besides, CJD is commonly mistaken for other forms of dementia. Due to the large heterogeneity of phenotypes of prion disease and the lack of a consistent spatial pattern of disease progression, the approaches used to study other types of neurodegenerative diseases are not satisfactory to capture the progression of the human form of prion disease. Using a tailored framework, I extracted quantitative imaging biomarkers for characterisation of patients with Prion diseases. Following the extraction of patient-specific imaging biomarkers from multiple images, I implemented a Gaussian Process approach to correlated symptoms with disease types and stages. The model was used on three different tasks: diagnosis, differential diagnosis and stratification, addressing an unmet need to automatically identify patients with or at risk of developing Prion disease. The work presented in this thesis has been extensively validated in a unique Prion disease cohort, comprising both the inherited and sporadic forms of the disease. The model has shown to be effective in the prediction of this illness. Furthermore, this approach may have used in other disorders with heterogeneous imaging features, being an added value for the understanding of neurodegenerative diseases. Lastly, given the rarity of this disease, I also addressed the issue of missing data and the limitations raised by it. Overall, this work presents progress towards modelling of Prion diseases and which computational methodologies are potentially suitable for its characterisation

Genetic algorithm-neural network : feature extraction for bioinformatics data

With the advance of gene expression data in the bioinformatics field, the questions which frequently arise, for both computer and medical scientists, are which genes are significantly involved in discriminating cancer classes and which genes are significant with respect to a specific cancer pathology. Numerous computational analysis models have been developed to identify informative genes from the microarray data, however, the integrity of the reported genes is still uncertain. This is mainly due to the misconception of the objectives of microarray study. Furthermore, the application of various preprocessing techniques in the microarray data has jeopardised the quality of the microarray data. As a result, the integrity of the findings has been compromised by the improper use of techniques and the ill-conceived objectives of the study. This research proposes an innovative hybridised model based on genetic algorithms (GAs) and artificial neural networks (ANNs), to extract the highly differentially expressed genes for a specific cancer pathology. The proposed method can efficiently extract the informative genes from the original data set and this has reduced the gene variability errors incurred by the preprocessing techniques. The novelty of the research comes from two perspectives. Firstly, the research emphasises on extracting informative features from a high dimensional and highly complex data set, rather than to improve classification results. Secondly, the use of ANN to compute the fitness function of GA which is rare in the context of feature extraction. Two benchmark microarray data have been taken to research the prominent genes expressed in the tumour development and the results show that the genes respond to different stages of tumourigenesis (i.e. different fitness precision levels) which may be useful for early malignancy detection. The extraction ability of the proposed model is validated based on the expected results in the synthetic data sets. In addition, two bioassay data have been used to examine the efficiency of the proposed model to extract significant features from the large, imbalanced and multiple data representation bioassay data.EThOS - Electronic Theses Online ServiceGBUnited Kingdo