Unconventional machine learning of genome-wide human cancer data

Recent advances in high-throughput genomic technologies coupled with exponential increases in computer processing and memory have allowed us to interrogate the complex aberrant molecular underpinnings of human disease from a genome-wide perspective. While the deluge of genomic information is expected to increase, a bottleneck in conventional high-performance computing is rapidly approaching. Inspired in part by recent advances in physical quantum processors, we evaluated several unconventional machine learning (ML) strategies on actual human tumor data. Here we show for the first time the efficacy of multiple annealing-based ML algorithms for classification of high-dimensional, multi-omics human cancer data from the Cancer Genome Atlas. To assess algorithm performance, we compared these classifiers to a variety of standard ML methods. Our results indicate the feasibility of using annealing-based ML to provide competitive classification of human cancer types and associated molecular subtypes and superior performance with smaller training datasets, thus providing compelling empirical evidence for the potential future application of unconventional computing architectures in the biomedical sciences

Supporting Regularized Logistic Regression Privately and Efficiently

As one of the most popular statistical and machine learning models, logistic regression with regularization has found wide adoption in biomedicine, social sciences, information technology, and so on. These domains often involve data of human subjects that are contingent upon strict privacy regulations. Increasing concerns over data privacy make it more and more difficult to coordinate and conduct large-scale collaborative studies, which typically rely on cross-institution data sharing and joint analysis. Our work here focuses on safeguarding regularized logistic regression, a widely-used machine learning model in various disciplines while at the same time has not been investigated from a data security and privacy perspective. We consider a common use scenario of multi-institution collaborative studies, such as in the form of research consortia or networks as widely seen in genetics, epidemiology, social sciences, etc. To make our privacy-enhancing solution practical, we demonstrate a non-conventional and computationally efficient method leveraging distributing computing and strong cryptography to provide comprehensive protection over individual-level and summary data. Extensive empirical evaluation on several studies validated the privacy guarantees, efficiency and scalability of our proposal. We also discuss the practical implications of our solution for large-scale studies and applications from various disciplines, including genetic and biomedical studies, smart grid, network analysis, etc

Radiogenomics Framework for Associating Medical Image Features with Tumour Genetic Characteristics

Significant progress has been made in the understanding of human cancers at the molecular genetics level and it is providing new insights into their underlying pathophysiology. This progress has enabled the subclassification of the disease and the development of targeted therapies that address specific biological pathways. However, obtaining genetic information remains invasive and costly. Medical imaging is a non-invasive technique that captures important visual characteristics (i.e. image features) of abnormalities and plays an important role in routine clinical practice. Advancements in computerised medical image analysis have enabled quantitative approaches to extract image features that can reflect tumour genetic characteristics, leading to the emergence of ‘radiogenomics’. Radiogenomics investigates the relationships between medical imaging features and tumour molecular characteristics, and enables the derivation of imaging surrogates (radiogenomics features) to genetic biomarkers that can provide alternative approaches to non-invasive and accurate cancer diagnosis. This thesis presents a new framework that combines several novel methods for radiogenomics analysis that associates medical image features with tumour genetic characteristics, with the main objectives being: i) a comprehensive characterisation of tumour image features that reflect underlying genetic information; ii) a method that identifies radiogenomics features encoding common pathophysiological information across different diseases, overcoming the dependence on large annotated datasets; and iii) a method that quantifies radiogenomics features from multi-modal imaging data and accounts for unique information encoded in tumour heterogeneity sub-regions. The present radiogenomics methods advance radiogenomics analysis and contribute to improving research in computerised medical image analysis

EapGAFS: Microarray Dataset for Ensemble Classification for Diseases Prediction

Microarray data stores the measured expression levels of thousands of genes simultaneously which helps the researchers to get insight into the biological and prognostic information. Cancer is a deadly disease that develops over time and involves the uncontrolled division of body cells. In cancer, many genes are responsible for cell growth and division. But different kinds of cancer are caused by a different set of genes. So to be able to better understand, diagnose and treat cancer, it is essential to know which of the genes in the cancer cells are working abnormally. The advances in data mining, machine learning, soft computing, and pattern recognition have addressed the challenges posed by the researchers to develop computationally effective models to identify the new class of disease and develop diagnostic or therapeutic targets. This paper proposed an Ensemble Aprior Gentic Algorithm Feature Selection (EapGAFS) for microarray dataset classification. The proposed algorithm comprises of the genetic algorithm implemented with aprior learning for the microarray attributes classification. The proposed EapGAFS uses the rule set mining in the genetic algorithm for the microarray dataset processing. Through framed rule set the proposed model extract the attribute features in the dataset. Finally, with the ensemble classifier model the microarray dataset were classified for the processing. The performance of the proposed EapGAFS is conventional classifiers for the collected microarray dataset of the breast cancer, Hepatities, diabeties, and bupa. The comparative analysis of the proposed EapGAFS with the conventional classifier expressed that the proposed EapGAFS exhibits improved performance in the microarray dataset classification. The performance of the proposed EapGAFS is improved ~4 – 6% than the conventional classifiers such as Adaboost and ensemble