Gene set based ensemble methods for cancer classification

Diagnosis of cancer very often depends on conclusions drawn after both clinical and microscopic examinations of tissues to study the manifestation of the disease in order to place tumors in known categories. One factor which determines the categorization of cancer is the tissue from which the tumor originates. Information gathered from clinical exams may be partial or not completely predictive of a specific category of cancer. Further complicating the problem of categorizing various tumors is that the histological classification of the cancer tissue and description of its course of development may be atypical. Gene expression data gleaned from micro-array analysis provides tremendous promise for more accurate cancer diagnosis. One hurdle in the classification of tumors based on gene expression data is that the data space is ultra-dimensional with relatively few points; that is, there are a small number of examples with a large number of genes. A second hurdle is expression bias caused by the correlation of genes. Analysis of subsets of genes, known as gene set analysis, provides a mechanism by which groups of differentially expressed genes can be identified. We propose an ensemble of classifiers whose base classifiers are ℓ1-regularized logistic regression models with restriction of the feature space to biologically relevant genes. Some researchers have already explored the use of ensemble classifiers to classify cancer but the effect of the underlying base classifiers in conjunction with biologically-derived gene sets on cancer classification has not been explored

Stable Feature Selection for Biomarker Discovery

Feature selection techniques have been used as the workhorse in biomarker discovery applications for a long time. Surprisingly, the stability of feature selection with respect to sampling variations has long been under-considered. It is only until recently that this issue has received more and more attention. In this article, we review existing stable feature selection methods for biomarker discovery using a generic hierarchal framework. We have two objectives: (1) providing an overview on this new yet fast growing topic for a convenient reference; (2) categorizing existing methods under an expandable framework for future research and development

ada: An R Package for Stochastic Boosting

Boosting is an iterative algorithm that combines simple classification rules with "mediocre" performance in terms of misclassification error rate to produce a highly accurate classification rule. Stochastic gradient boosting provides an enhancement which incorporates a random mechanism at each boosting step showing an improvement in performance and speed in generating the ensemble. ada is an R package that implements three popular variants of boosting, together with a version of stochastic gradient boosting. In addition, useful plots for data analytic purposes are provided along with an extension to the multi-class case. The algorithms are illustrated with synthetic and real data sets.

Classifying pairs with trees for supervised biological network inference

Networks are ubiquitous in biology and computational approaches have been largely investigated for their inference. In particular, supervised machine learning methods can be used to complete a partially known network by integrating various measurements. Two main supervised frameworks have been proposed: the local approach, which trains a separate model for each network node, and the global approach, which trains a single model over pairs of nodes. Here, we systematically investigate, theoretically and empirically, the exploitation of tree-based ensemble methods in the context of these two approaches for biological network inference. We first formalize the problem of network inference as classification of pairs, unifying in the process homogeneous and bipartite graphs and discussing two main sampling schemes. We then present the global and the local approaches, extending the later for the prediction of interactions between two unseen network nodes, and discuss their specializations to tree-based ensemble methods, highlighting their interpretability and drawing links with clustering techniques. Extensive computational experiments are carried out with these methods on various biological networks that clearly highlight that these methods are competitive with existing methods.Comment: 22 page

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

An Empirical Analysis of Predictive Machine Learning Algorithms on High-Dimensional Microarray Cancer Data

This research evaluates pattern recognition techniques on a subclass of big data where the dimensionality of the input space p is much larger than the number of observations n. Seven gene-expression microarray cancer datasets, where the ratio κ = n/p is less than one, were chosen for evaluation. The statistical and computational challenges inherent with this type of high-dimensional low sample size (HDLSS) data were explored. The capability and performance of a diverse set of machine learning algorithms is presented and compared. The sparsity and collinearity of the data being employed, in conjunction with the complexity of the algorithms studied, demanded rigorous and careful tuning of the hyperparameters and regularization parameters. This necessitated several extensions of cross-validation to be investigated, with the purpose of culminating in the best predictive performance. For the techniques evaluated in this thesis, regularization or kernelization, and often both, produced lower classification error rates than randomized ensemble for all datasets used in this research. However, no one technique evaluated for classifying HDLSS microarray cancer data emerged as the universally best technique for predicting the generalization error.1 From the empirical analysis performed in this thesis, the following fundamentals emerged as being instrumental in consistently resulting in lower error rates when estimating the generalization error in this HDLSS microarray cancer data: • Thoroughly investigate and understand the data • Stratify during all sampling due to the uneven classes and extreme sparsity of this data. • Perform 3 to 5 replicates of stratified cross-validation, implementing an adaptive K-fold, to determine the optimal tuning parameters. • To estimate the generalization error in HDLSS data, replication is paramount. Replicate R=500 or R=1000 times with training and test sets of 2/3 and 1/3, respectively, to get the best generalization error estimate. • Whenever possible, obtain an independent validation dataset. • Seed the data for a fair and unbiased comparison among techniques. • Define a methodology or standard set of process protocols to apply to machine learning research. This would prove very beneficial in ensuring reproducibility and would enable better comparisons among techniques. _____ 1A predominant portion of this research was published in the Serdica Journal of Computing (Volume 8, Number 2, 2014) as proceedings from the 2014 Flint International Statistical Conference at Kettering University, Michigan, USA

A cDNA Microarray Gene Expression Data Classifier for Clinical Diagnostics Based on Graph Theory

Despite great advances in discovering cancer molecular profiles, the proper application of microarray technology to routine clinical diagnostics is still a challenge. Current practices in the classification of microarrays' data show two main limitations: the reliability of the training data sets used to build the classifiers, and the classifiers' performances, especially when the sample to be classified does not belong to any of the available classes. In this case, state-of-the-art algorithms usually produce a high rate of false positives that, in real diagnostic applications, are unacceptable. To address this problem, this paper presents a new cDNA microarray data classification algorithm based on graph theory and is able to overcome most of the limitations of known classification methodologies. The classifier works by analyzing gene expression data organized in an innovative data structure based on graphs, where vertices correspond to genes and edges to gene expression relationships. To demonstrate the novelty of the proposed approach, the authors present an experimental performance comparison between the proposed classifier and several state-of-the-art classification algorithm