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

A comprehensive comparison of random forests and support vector machines for microarray-based cancer classification

<p>Abstract</p> <p>Background</p> <p>Cancer diagnosis and clinical outcome prediction are among the most important emerging applications of gene expression microarray technology with several molecular signatures on their way toward clinical deployment. Use of the most accurate classification algorithms available for microarray gene expression data is a critical ingredient in order to develop the best possible molecular signatures for patient care. As suggested by a large body of literature to date, support vector machines can be considered "best of class" algorithms for classification of such data. Recent work, however, suggests that random forest classifiers may outperform support vector machines in this domain.</p> <p>Results</p> <p>In the present paper we identify methodological biases of prior work comparing random forests and support vector machines and conduct a new rigorous evaluation of the two algorithms that corrects these limitations. Our experiments use 22 diagnostic and prognostic datasets and show that support vector machines outperform random forests, often by a large margin. Our data also underlines the importance of sound research design in benchmarking and comparison of bioinformatics algorithms.</p> <p>Conclusion</p> <p>We found that both on average and in the majority of microarray datasets, random forests are outperformed by support vector machines both in the settings when no gene selection is performed and when several popular gene selection methods are used.</p

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

Identification of an Efficient Gene Expression Panel for Glioblastoma Classification.

We present here a novel genetic algorithm-based random forest (GARF) modeling technique that enables a reduction in the complexity of large gene disease signatures to highly accurate, greatly simplified gene panels. When applied to 803 glioblastoma multiforme samples, this method allowed the 840-gene Verhaak et al. gene panel (the standard in the field) to be reduced to a 48-gene classifier, while retaining 90.91% classification accuracy, and outperforming the best available alternative methods. Additionally, using this approach we produced a 32-gene panel which allows for better consistency between RNA-seq and microarray-based classifications, improving cross-platform classification retention from 69.67% to 86.07%. A webpage producing these classifications is available at http://simplegbm.semel.ucla.edu

An Improved Parallelized mRMR for Gene Subset Selection in Cancer Classification

DNA microarray technique has become a more attractive tool for cancer classification in the scientific and industrial fields. Based on the previous researchers, the conventional approach for cancer classification is primarily based on morphological appearance of the tumor. The limitations of this approach are bias in identify the tumors by expert and faced the difficulty in differentiate the cancer subtypes due to most cancers being highly related to the specific biological insight.  Thus, this study propose an improved parallelized Minimum Redundancy Maximum Relevance (mRMR), which is a particularly fast feature selection method for finding a set of both relevant and complementary features. The mRMR can identify genes more relevance to biological context that leads to richer biological interpretations. The proposed method is expected to achieve accurate classification performance using small number of predictive genes when tested using two datasets from Cancer Genome Project and compared to previous methods

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

classification of oncologic data with genetic programming

Discovering the models explaining the hidden relationship between genetic material and tumor pathologies is one of the most important open challenges in biology and medicine. Given the large amount of data made available by the DNA Microarray technique, Machine Learning is becoming a popular tool for this kind of investigations. In the last few years, we have been particularly involved in the study of Genetic Programming for mining large sets of biomedical data. In this paper, we present a comparison between four variants of Genetic Programming for the classification of two different oncologic datasets: the first one contains data from healthy colon tissues and colon tissues affected by cancer; the second one contains data from patients affected by two kinds of leukemia (acute myeloid leukemia and acute lymphoblastic leukemia). We report experimental results obtained using two different fitness criteria: the receiver operating characteristic and the percentage of correctly classified instances. These results, and their comparison with the ones obtained by three nonevolutionary Machine Learning methods (Support Vector Machines, MultiBoosting, and Random Forests) on the same data, seem to hint that Genetic Programming is a promising technique for this kind of classification

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

PRETICTIVE BIOINFORMATIC METHODS FOR ANALYZING GENES AND PROTEINS

Since large amounts of biological data are generated using various high-throughput technologies, efficient computational methods are important for understanding the biological meanings behind the complex data. Machine learning is particularly appealing for biological knowledge discovery. Tissue-specific gene expression and protein sumoylation play essential roles in the cell and are implicated in many human diseases. Protein destabilization is a common mechanism by which mutations cause human diseases. In this study, machine learning approaches were developed for predicting human tissue-specific genes, protein sumoylation sites and protein stability changes upon single amino acid substitutions. Relevant biological features were selected for input vector encoding, and machine learning algorithms, including Random Forests and Support Vector Machines, were used for classifier construction. The results suggest that the approaches give rise to more accurate predictions than previous studies and can provide valuable information for further experimental studies. Moreover, seeSUMO and MuStab web servers were developed to make the classifiers accessible to the biological research community. Structure-based methods can be used to predict the effects of amino acid substitutions on protein function and stability. The nonsynonymous Single Nucleotide Polymorphisms (nsSNPs) located at the protein binding interface have dramatic effects on protein-protein interactions. To model the effects, the nsSNPs at the interfaces of 264 protein-protein complexes were mapped on the protein structures using homology-based methods. The results suggest that disease-causing nsSNPs tend to destabilize the electrostatic component of the binding energy and nsSNPs at conserved positions have significant effects on binding energy changes. The structure-based approach was developed to quantitatively assess the effects of amino acid substitutions on protein stability and protein-protein interaction. It was shown that the structure-based analysis could help elucidate the mechanisms by which mutations cause human genetic disorders. These new bioinformatic methods can be used to analyze some interesting genes and proteins for human genetic research and improve our understanding of their molecular mechanisms underlying human diseases