Computational Methods for the Study of Peroxisomes in Health and Disease

Peroxisomal dysfunction has been linked to severe human metabolic disorders but is also linked to human diseases, including obesity, neurodegeneration, age-related diseases, and cancer. As such, peroxisome research has significantly increased in recent years. In parallel, advances in computational methods and data processing analysis may now be used to approach unanswered questions on peroxisome regulation, mechanism, function, and biogenesis in the context of healthy and pathological phenotypes. Here, we intend to provide an overview of advanced computational methods for the analysis of imaging data, protein structure modeling, proteomics, and genomics. We provide a concise background on these approaches, with specific and relevant examples. This chapter may serve as a broad resource for the current status of technological advances, and an introduction to computational methods for peroxisome research

When the machine does not know measuring uncertainty in deep learning models of medical images

This thesis was submitted for the award of Doctor of Philosophy and was awarded by Brunel University LondonRecently, Deep learning (DL), which involves powerful black box predictors, has outperformed human experts in several medical diagnostic problems. However, these methods focus exclusively on improving the accuracy of point predictions without assessing their outputs’ quality and ignore the asymmetric cost involved in different types of misclassification errors. Neural networks also do not deliver confidence in predictions and suffer from over and under confidence, i.e. are not well calibrated. Knowing how much confidence there is in a prediction is essential for gaining clinicians’ trust in the technology. Calibrated uncertainty quantification is a challenging problem as no ground truth is available. To address this, we make two observations: (i) cost-sensitive deep neural networks with Dropweights models better quantify calibrated predictive uncertainty, and (ii) estimated uncertainty with point predictions in Deep Ensembles Bayesian Neural Networks with DropWeights can lead to a more informed decision and improve prediction quality. This dissertation focuses on quantifying uncertainty using concepts from cost-sensitive neural networks, calibration of confidence, and Dropweights ensemble method. First, we show how to improve predictive uncertainty by deep ensembles of neural networks with Dropweights learning an approximate distribution over its weights in medical image segmentation and its application in active learning. Second, we use the Jackknife resampling technique to correct bias in quantified uncertainty in image classification and propose metrics to measure uncertainty performance. The third part of the thesis is motivated by the discrepancy between the model predictive error and the objective in quantified uncertainty when costs for misclassification errors or unbalanced datasets are asymmetric. We develop cost-sensitive modifications of the neural networks in disease detection and propose metrics to measure the quality of quantified uncertainty. Finally, we leverage an adaptive binning strategy to measure uncertainty calibration error that directly corresponds to estimated uncertainty performance and address problematic evaluation methods. We evaluate the effectiveness of the tools on nuclei images segmentation, multi-class Brain MRI image classification, multi-level cell type-specific protein expression prediction in ImmunoHistoChemistry (IHC) images and cost-sensitive classification for Covid-19 detection from X-Rays and CT image dataset. Our approach is thoroughly validated by measuring the quality of uncertainty. It produces an equally good or better result and paves the way for the future that addresses the practical problems at the intersection of deep learning and Bayesian decision theory. In conclusion, our study highlights the opportunities and challenges of the application of estimated uncertainty in deep learning models of medical images, representing the confidence of the model’s prediction, and the uncertainty quality metrics show a significant improvement when using Deep Ensembles Bayesian Neural Networks with DropWeights

Context-specific subcellular localization prediction: Leveraging protein interaction networks and scientific texts

Zhu L. Context-specific subcellular localization prediction: Leveraging protein interaction networks and scientific texts. Bielefeld: Universität Bielefeld; 2018.One essential task in proteomics analysis is to explore the functions of proteins in conducting and regulating the activities at the subcellular level. Compartmentalization of cells allows proteins to perform their activities efficiently. A protein functions correctly only if it occurs at the right place, at the right time, and interacts with the right molecules. Therefore, the knowledge of protein subcellular localization (SCL) can provide valuable insights for understanding protein functions and related cellular mechanisms. Thus, the systematic study of the subcellular distribution of human proteins is an essential task for fully characterizing the human proteome. The context-specific analysis is an important and challenging task in systems biology research. Proteins may perform different functions at different subcellular compartments (SCCs). Hence, the dynamic and context-specific alterations of the subcellular spatial distribution of proteins are essential in identifying cellular function. While this important feature is well-known in molecular and cell biology, most large-scale protein annotation studies to-date have ignored it. Tissue is one particularly crucial biological context for human biology. Proteins show their tissue specificity at the subcellular level by localizing to different SCCs in different tissues. For example, glutamine synthetase localizes in mitochondria in liver cells while in the cytoplasm in brain cells. The knowledge of the tissue-specific SCLs can enrich the human protein annotation, and thus will increase our understanding of human biology. Conventional wet-lab experiments are used to determine the SCL of proteins. Due to the expense and low-throughput of wet-lab experimental approaches, various algorithms and tools have been developed for predicting protein SCLs by integrating biological background knowledge into machine learning methods. Most of the existing approaches are designed for handling general genome-wide large-scale analysis. Thus, they cannot be used for context-specific analysis of protein SCL. The focus of this work is to develop new methods to perform tissue-specific SCL prediction. (1) First, we developed Bayesian collective Markov Random Fields (BCMRFs) to address the general multi-SCL problem. BCMRFs integrate both protein-protein interaction network (PPIN) features and the protein sequence features, consider the spatial adjacency of SCCs, and employ transductive learning on imbalanced SCL data sets. Our experimental results show that BCMRFs achieve higher performance in comparison with the state-of-art PPI-based method in SCL prediction. (2) We then integrated BCMRFs into a novel end-to-end computational approach to perform tissue-specific SCL prediction on tissue-specific PPINs. In total, 1314 proteins which SCLs were previously proven cell lines dependent were successfully localized based on nine tissue-specific PPINs. Furthermore, 549 new tissue-specific localized candidate proteins were predicted and confirmed by scientific literature. Due to the high performance of BCMRFs on known tissue-specific proteins, these are excellent candidates for further wet-lab experimental validation. (3) In addition to the proteomics data, the existing scientific literature contains an abundance of tissue-specific SCL data. To collect these data, we developed a scoring-based text mining system and extracted tissue-specific SCL associations from the abstracts of a large number of biomedical papers. The obtained data are accessible from the web based database TS-SCL DB. (4) We concluded the study with an application case study of the tissue-specific subcellular distribution of human argonaute-2 (AGO2) protein. We demonstrated how to perform tissue-specific SCL prediction on AGO2-related PPINs. Most of the resulting tissue-specific SCLs are confirmed by literature results available in TS-SCL DB

Bioinformatics analysis of mitochondrial disease

PhD thesisSeveral bioinformatic methods have been developed to aid the identification of novel nuclear-mitochondrial genes involved in disease. Previous research has aimed to increase the sensitivity and specificity of these predictions through a combination of available techniques. This investigation shows the optimum sensitivity and specificity can be achieved by carefully selecting seven specific classifiers in combination. The results also show that increasing the number of classifiers even further can paradoxically decrease the sensitivity and specificity of a prediction. Additionally, text mining applications are playing a huge role in disease candidate gene identification providing resources for interpreting the vast quantities of biomedical literature currently available. A workflow resource was developed identifying a number of genes potentially associated with Lebers Hereditary Optic Neuropathy (LHON). This included specific orthologues in mouse displaying a potential association to LHON not annotated as such in humans. Mitochondrial DNA (mtDNA) fragments have been transferred to the human nuclear genome over evolutionary time. These insertions were compared to an existing database of 263 mtDNA deletions to highlight any associated mechanisms governing DNA loss from mitochondria. Flanking regions were also screened within the nuclear genome that surrounded these insertions for transposable elements, GC content and mitochondrial genes. No obvious association was found relating NUMTs to mtDNA deletions. NUMTs do not appear to be distributed throughout the genome via transposition and integrate predominantly in areas of low %GC with low gene content. These areas also lacked evidence of an elevated number of surrounding nuclear-mitochondrial genes but a further genome-wide study is required