Machine Learning and Integrative Analysis of Biomedical Big Data.

Recent developments in high-throughput technologies have accelerated the accumulation of massive amounts of omics data from multiple sources: genome, epigenome, transcriptome, proteome, metabolome, etc. Traditionally, data from each source (e.g., genome) is analyzed in isolation using statistical and machine learning (ML) methods. Integrative analysis of multi-omics and clinical data is key to new biomedical discoveries and advancements in precision medicine. However, data integration poses new computational challenges as well as exacerbates the ones associated with single-omics studies. Specialized computational approaches are required to effectively and efficiently perform integrative analysis of biomedical data acquired from diverse modalities. In this review, we discuss state-of-the-art ML-based approaches for tackling five specific computational challenges associated with integrative analysis: curse of dimensionality, data heterogeneity, missing data, class imbalance and scalability issues

DEVELOPMENTS IN NONPARAMETRIC REGRESSION METHODS WITH APPLICATION TO RAMAN SPECTROSCOPY ANALYSIS

Raman spectroscopy has been successfully employed in the classification of breast pathologies involving basis spectra for chemical constituents of breast tissue and resulted in high sensitivity (94%) and specificity (96%) (Haka et al, 2005). Motivated by recent developments in nonparametric regression, in this work, we adapt stacking, boosting, and dynamic ensemble learning into a nonparametric regression framework with application to Raman spectroscopy analysis for breast cancer diagnosis. In Chapter 2, we apply compound estimation (Charnigo and Srinivasan, 2011) in Raman spectra analysis to classify normal, benign, and malignant breast tissue. We explore both the spectra profiles and their derivatives to differentiate different types of breast tissue. In Chapters 3-5 of this dissertation, we develop a novel paradigm for incorporating ensemble learning classification methodology into a nonparametric regression framework. Specifically, in Chapter 3 we set up modified stacking framework and combine different classifiers together to make better predictions in nonparametric regression settings. In Chapter 4 we develop a method by incorporating a modified AdaBoost algorithm in nonparametric regression settings to improve classification accuracy. In Chapter 5 we propose a dynamic ensemble integration based on multiple meta-learning strategies for nonparametric regression based classification. In Chapter 6, we revisit the Raman spectroscopy data in Chapter 2, and make improvements based on the developments of the methods from Chapter 3 to Chapter 4. Finally we summarize the major findings and contributions of this work as well as identify opportunities for future research and their public health implications

Predicting breast cancer risk, recurrence and survivability

This thesis focuses on predicting breast cancer at early stages by using machine learning algorithms based on biological datasets. The accuracy of those algorithms has been improved to enable the physicians to enhance the success of treatment, thus saving lives and avoiding several further medical tests

Histopathological image analysis : a review

Over the past decade, dramatic increases in computational power and improvement in image analysis algorithms have allowed the development of powerful computer-assisted analytical approaches to radiological data. With the recent advent of whole slide digital scanners, tissue histopathology slides can now be digitized and stored in digital image form. Consequently, digitized tissue histopathology has now become amenable to the application of computerized image analysis and machine learning techniques. Analogous to the role of computer-assisted diagnosis (CAD) algorithms in medical imaging to complement the opinion of a radiologist, CAD algorithms have begun to be developed for disease detection, diagnosis, and prognosis prediction to complement the opinion of the pathologist. In this paper, we review the recent state of the art CAD technology for digitized histopathology. This paper also briefly describes the development and application of novel image analysis technology for a few specific histopathology related problems being pursued in the United States and Europe