Supervised projection pursuit - A dimensionality reduction technique optimized for probabilistic classification

An important step in multivariate analysis is the dimensionality reduction, which allows for a better classification and easier visualization of the class structures in the data. Techniques like PCA, PLS-DA and LDA are most often used to explore the patterns in the data and to reduce the dimensions. Yet the data does not always reveal properly the structures when these techniques are applied. To this end, a supervised projection pursuit (SuPP) is proposed in this article, based on Jensen-Shannon divergence. The combination of this metric with powerful Monte Carlo based optimization algorithm, yielded a versatile dimensionality reduction technique capable of working with highly dimensional data and missing observations. Combined with Naïve Bayes (NB) classifier, SuPP proved to be a powerful preprocessing tool for classification. Namely, on the Iris data set, the prediction accuracy of SuPP-NB is significantly higher than the prediction accuracy of PCA-NB, (p-value ≤ 4.02E-05 in a 2D latent space, p-value ≤ 3.00E-03 in a 3D latent space) and significantly higher than the prediction accuracy of PLS-DA (p-value ≤ 1.17E-05 in a 2D latent space and p-value ≤ 3.08E-03 in a 3D latent space). The significantly higher accuracy for this particular data set is a strong evidence of a better class separation in the latent spaces obtained with SuPP

Development of Artificial Intelligence systems as a prediction tool in ovarian cancer

PhD ThesisOvarian cancer is the 5th most common cancer in females and the UK has one of the highest incident rates in Europe. In the UK only 36% of patients will live for at least 5 years after diagnosis. The number of prognostic markers, treatments and the sequences of treatments in ovarian cancer are rising. Therefore, it is getting more difficult for the human brain to perform clinical decision making. There is a need for an expert computer system (e.g. Artificial Intelligence (AI)), which is capable of investigating the possible outcomes for each marker, treatment and sequence of treatment. Such expert systems may provide a tool which could help clinicians to analyse and predict outcome using different treatment pathways. Whilst prediction of overall survival of a patient is difficult there may be some benefits, as this not only is useful information for the patient but may also determine treatment modality. In this project a dataset was constructed of 352 patients who had been treated at a single centre. Clinical data were extracted from the health records. Expert systems were then investigated to determine the optimum model to predict overall survival of a patient. The five year survival period (a standard survival outcome measure in cancer research) was investigated; in addition, the system was developed with the flexibility to predict patient survival rates for many other categories. Comparisons with currently used prognostic models in ovarian cancer demonstrated a significant improvement in performance for the AI model (Area under the Curve (AUC) of 0.72 for AI and AUC of 0.62 for the statistical model). Using various methods, the most important variables in this prediction were identified as: FIGO stage, outcome of the surgery and CA125. This research investigated the effects of increasing the number of cases in prediction models. Results indicated that by increasing the number of cases, the prediction performance improved. Categorization of continuous data did not improve the prediction performance. The project next investigated the possibility of predicting surgical outcomes in ovarian cancer using AI, based on the variables that are available for clinicians prior to the surgery. Such a tool could have direct clinical relevance. Diverse models that can predict the outcome of the surgery were investigated and developed. The developed AI models were also compared against the standard statistical prediction model, which demonstrated that the AI model outperformed the statistical prediction model: the prediction of all outcomes (complete or optimal or suboptimal) (AUC of AI: 0.71 and AUC of statistical model: 0.51), the prediction of complete or optimal cytoreduction versus suboptimal cytoreduction (AUC of AI: 0.73 and AUC of statistical model: 0.50) and finally the prediction of complete cytoreduction versus optimal or suboptimal cytoreduction (AUC of AI: 0.79 and AUC of statistical model: 0.47). The most important variables for this prediction were identified as: FIGO stage, tumour grade and histology. The application of transcriptomic analysis to cancer research raises the question of which genes are significantly involved in a particular cancer and which genes can accurately predict survival outcomes in a given cancer. Therefore, AI techniques were employed to identify the most important genes for the prediction of Homologous Recombination (HR), an important DNA repair pathway in ovarian cancer, identifying LIG1 and POLD3 as novel prognostic biomarkers. Finally, AI models were used to predict the HR status for any given patient (AUC: 0.87). This project has demonstrated that AI may have an important role in ovarian cancer. AI systems may provide tools to help clinicians and research in ovarian cancer and may allow more informed decisions resulting in better management of this cancer

Structured Sparse Methods for Imaging Genetics

abstract: Imaging genetics is an emerging and promising technique that investigates how genetic variations affect brain development, structure, and function. By exploiting disorder-related neuroimaging phenotypes, this class of studies provides a novel direction to reveal and understand the complex genetic mechanisms. Oftentimes, imaging genetics studies are challenging due to the relatively small number of subjects but extremely high-dimensionality of both imaging data and genomic data. In this dissertation, I carry on my research on imaging genetics with particular focuses on two tasks---building predictive models between neuroimaging data and genomic data, and identifying disorder-related genetic risk factors through image-based biomarkers. To this end, I consider a suite of structured sparse methods---that can produce interpretable models and are robust to overfitting---for imaging genetics. With carefully-designed sparse-inducing regularizers, different biological priors are incorporated into learning models. More specifically, in the Allen brain image--gene expression study, I adopt an advanced sparse coding approach for image feature extraction and employ a multi-task learning approach for multi-class annotation. Moreover, I propose a label structured-based two-stage learning framework, which utilizes the hierarchical structure among labels, for multi-label annotation. In the Alzheimer's disease neuroimaging initiative (ADNI) imaging genetics study, I employ Lasso together with EDPP (enhanced dual polytope projections) screening rules to fast identify Alzheimer's disease risk SNPs. I also adopt the tree-structured group Lasso with MLFre (multi-layer feature reduction) screening rules to incorporate linkage disequilibrium information into modeling. Moreover, I propose a novel absolute fused Lasso model for ADNI imaging genetics. This method utilizes SNP spatial structure and is robust to the choice of reference alleles of genotype coding. In addition, I propose a two-level structured sparse model that incorporates gene-level networks through a graph penalty into SNP-level model construction. Lastly, I explore a convolutional neural network approach for accurate predicting Alzheimer's disease related imaging phenotypes. Experimental results on real-world imaging genetics applications demonstrate the efficiency and effectiveness of the proposed structured sparse methods.Dissertation/ThesisDoctoral Dissertation Computer Science 201

A Novel Hybrid Dimensionality Reduction Method using Support Vector Machines and Independent Component Analysis

Due to the increasing demand for high dimensional data analysis from various applications such as electrocardiogram signal analysis and gene expression analysis for cancer detection, dimensionality reduction becomes a viable process to extracts essential information from data such that the high-dimensional data can be represented in a more condensed form with much lower dimensionality to both improve classification accuracy and reduce computational complexity. Conventional dimensionality reduction methods can be categorized into stand-alone and hybrid approaches. The stand-alone method utilizes a single criterion from either supervised or unsupervised perspective. On the other hand, the hybrid method integrates both criteria. Compared with a variety of stand-alone dimensionality reduction methods, the hybrid approach is promising as it takes advantage of both the supervised criterion for better classification accuracy and the unsupervised criterion for better data representation, simultaneously. However, several issues always exist that challenge the efficiency of the hybrid approach, including (1) the difficulty in finding a subspace that seamlessly integrates both criteria in a single hybrid framework, (2) the robustness of the performance regarding noisy data, and (3) nonlinear data representation capability. This dissertation presents a new hybrid dimensionality reduction method to seek projection through optimization of both structural risk (supervised criterion) from Support Vector Machine (SVM) and data independence (unsupervised criterion) from Independent Component Analysis (ICA). The projection from SVM directly contributes to classification performance improvement in a supervised perspective whereas maximum independence among features by ICA construct projection indirectly achieving classification accuracy improvement due to better intrinsic data representation in an unsupervised perspective. For linear dimensionality reduction model, I introduce orthogonality to interrelate both projections from SVM and ICA while redundancy removal process eliminates a part of the projection vectors from SVM, leading to more effective dimensionality reduction. The orthogonality-based linear hybrid dimensionality reduction method is extended to uncorrelatedness-based algorithm with nonlinear data representation capability. In the proposed approach, SVM and ICA are integrated into a single framework by the uncorrelated subspace based on kernel implementation. Experimental results show that the proposed approaches give higher classification performance with better robustness in relatively lower dimensions than conventional methods for high-dimensional datasets

Cell Type Classification Via Deep Learning On Single-Cell Gene Expression Data

Single-cell sequencing is a recently advanced revolutionary technology which enables researchers to obtain genomic, transcriptomic, or multi-omics information through gene expression analysis. It gives the advantage of analyzing highly heterogenous cell type information compared to traditional sequencing methods, which is gaining popularity in the biomedical area. Moreover, this analysis can help for early diagnosis and drug development of tumor cells, and cancer cell types. In the workflow of gene expression data profiling, identification of the cell types is an important task, but it faces many challenges like the curse of dimensionality, sparsity, batch effect, and overfitting. However, these challenges can be overcome by performing a feature selection technique which selects more relevant features by reducing feature dimensions. In this research work, recurrent neural network-based feature selection model is proposed to extract relevant features from high dimensional, and low sample size data. Moreover, a deep learning-based gene embedding model is also proposed to reduce data sparsity of single-cell data for cell type identification. The proposed frameworks have been implemented with different architectures of recurrent neural networks, and demonstrated via real-world micro-array datasets and single-cell RNA-seq data and observed that the proposed models perform better than other feature selection models. A semi-supervised model is also implemented using the same workflow of gene embedding concept since labeling data is very cumbersome, time consuming, and requires manual effort and expertise in the field. Therefore, different ratios of labeled data are used in the experiment to validate the concept. Experimental results show that the proposed semi-supervised approach represents very encouraging performance even though a limited number of labeled data is used via the gene embedding concept. In addition, graph attention based autoencoder model has also been studied to learn the latent features by incorporating prior knowledge with gene expression data for cell type classification. Index Terms — Single-Cell Gene Expression Data, Gene Embedding, Semi-Supervised model, Incorporate Prior Knowledge, Gene-gene Interaction Network, Deep Learning, Graph Auto Encode

New approaches for unsupervised transcriptomic data analysis based on Dictionary learning

The era of high-throughput data generation enables new access to biomolecular profiles and exploitation thereof. However, the analysis of such biomolecular data, for example, transcriptomic data, suffers from the so-called "curse of dimensionality". This occurs in the analysis of datasets with a significantly larger number of variables than data points. As a consequence, overfitting and unintentional learning of process-independent patterns can appear. This can lead to insignificant results in the application. A common way of counteracting this problem is the application of dimension reduction methods and subsequent analysis of the resulting low-dimensional representation that has a smaller number of variables. In this thesis, two new methods for the analysis of transcriptomic datasets are introduced and evaluated. Our methods are based on the concepts of Dictionary learning, which is an unsupervised dimension reduction approach. Unlike many dimension reduction approaches that are widely applied for transcriptomic data analysis, Dictionary learning does not impose constraints on the components that are to be derived. This allows for great flexibility when adjusting the representation to the data. Further, Dictionary learning belongs to the class of sparse methods. The result of sparse methods is a model with few non-zero coefficients, which is often preferred for its simplicity and ease of interpretation. Sparse methods exploit the fact that the analysed datasets are highly structured. Indeed, a characteristic of transcriptomic data is particularly their structuredness, which appears due to the connection of genes and pathways, for example. Nonetheless, the application of Dictionary learning in medical data analysis is mainly restricted to image analysis. Another advantage of Dictionary learning is that it is an interpretable approach. Interpretability is a necessity in biomolecular data analysis to gain a holistic understanding of the investigated processes. Our two new transcriptomic data analysis methods are each designed for one main task: (1) identification of subgroups for samples from mixed populations, and (2) temporal ordering of samples from dynamic datasets, also referred to as "pseudotime estimation". Both methods are evaluated on simulated and real-world data and compared to other methods that are widely applied in transcriptomic data analysis. Our methods convince through high performance and overall outperform the comparison methods