Challenges in the Analysis of Mass-Throughput Data: A Technical Commentary from the Statistical Machine Learning Perspective

Sound data analysis is critical to the success of modern molecular medicine research that involves collection and interpretation of mass-throughput data. The novel nature and high-dimensionality in such datasets pose a series of nontrivial data analysis problems. This technical commentary discusses the problems of over-fitting, error estimation, curse of dimensionality, causal versus predictive modeling, integration of heterogeneous types of data, and lack of standard protocols for data analysis. We attempt to shed light on the nature and causes of these problems and to outline viable methodological approaches to overcome them

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

The FAST-AIMS Clinical Mass Spectrometry Analysis System

Within clinical proteomics, mass spectrometry analysis of biological samples is emerging as an important high-throughput technology, capable of producing powerful diagnostic and prognostic models and identifying important disease biomarkers. As interest in this area grows, and the number of such proteomics datasets continues to increase, the need has developed for efficient, comprehensive, reproducible methods of mass spectrometry data analysis by both experts and nonexperts. We have designed and implemented a stand-alone software system, FAST-AIMS, which seeks to meet this need through automation of data preprocessing, feature selection, classification model generation, and performance estimation. FAST-AIMS is an efficient and user-friendly stand-alone software for predictive analysis of mass spectrometry data. The present resource review paper will describe the features and use of the FAST-AIMS system. The system is freely available for download for noncommercial use

Effects of Environment, Genetics and Data Analysis Pitfalls in an Esophageal Cancer Genome-Wide Association Study

The development of new high-throughput genotyping technologies has allowed fast evaluation of single nucleotide polymorphisms (SNPs) on a genome-wide scale. Several recent genome-wide association studies employing these technologies suggest that panels of SNPs can be a useful tool for predicting cancer susceptibility and discovery of potentially important new disease loci.In the present paper we undertake a careful examination of the relative significance of genetics, environmental factors, and biases of the data analysis protocol that was used in a previously published genome-wide association study. That prior study reported a nearly perfect discrimination of esophageal cancer patients and healthy controls on the basis of only genetic information. On the other hand, our results strongly suggest that SNPs in this dataset are not statistically linked to the phenotype, while several environmental factors and especially family history of esophageal cancer (a proxy to both environmental and genetic factors) have only a modest association with the disease.The main component of the previously claimed strong discriminatory signal is due to several data analysis pitfalls that in combination led to the strongly optimistic results. Such pitfalls are preventable and should be avoided in future studies since they create misleading conclusions and generate many false leads for subsequent research

Causal graph-based analysis of genome-wide association data in rheumatoid arthritis

<p>Abstract</p> <p>Background</p> <p>GWAS owe their popularity to the expectation that they will make a major impact on diagnosis, prognosis and management of disease by uncovering genetics underlying clinical phenotypes. The dominant paradigm in GWAS data analysis so far consists of extensive reliance on methods that emphasize contribution of individual SNPs to statistical association with phenotypes. Multivariate methods, however, can extract more information by considering associations of multiple SNPs simultaneously. Recent advances in other genomics domains pinpoint multivariate causal graph-based inference as a promising principled analysis framework for high-throughput data. Designed to discover biomarkers in the local causal pathway of the phenotype, these methods lead to accurate and highly parsimonious multivariate predictive models. In this paper, we investigate the applicability of causal graph-based method TIE* to analysis of GWAS data. To test the utility of TIE*, we focus on anti-CCP positive rheumatoid arthritis (RA) GWAS datasets, where there is a general consensus in the community about the major genetic determinants of the disease.</p> <p>Results</p> <p>Application of TIE* to the North American Rheumatoid Arthritis Cohort (NARAC) GWAS data results in six SNPs, mostly from the MHC locus. Using these SNPs we develop two predictive models that can classify cases and disease-free controls with an accuracy of 0.81 area under the ROC curve, as verified in independent testing data from the same cohort. The predictive performance of these models generalizes reasonably well to Swedish subjects from the closely related but not identical Epidemiological Investigation of Rheumatoid Arthritis (EIRA) cohort with 0.71-0.78 area under the ROC curve. Moreover, the SNPs identified by the TIE* method render many other previously known SNP associations conditionally independent of the phenotype.</p> <p>Conclusions</p> <p>Our experiments demonstrate that application of TIE* captures maximum amount of genetic information about RA in the data and recapitulates the major consensus findings about the genetic factors of this disease. In addition, TIE* yields reproducible markers and signatures of RA. This suggests that principled multivariate causal and predictive framework for GWAS analysis empowers the community with a new tool for high-quality and more efficient discovery.</p> <p>Reviewers</p> <p>This article was reviewed by Prof. Anthony Almudevar, Dr. Eugene V. Koonin, and Prof. Marianthi Markatou.</p

A petri nets based design of cognitive radios using distributed signal processing

AbstractReconfigurability for transceivers for wireless access networks like Bluetooth, WiMAX and W-LAN will become increasingly important. An appropriately flexible and reliable software architecture, allowing the concurrent processing of different controlling tasks for wireless terminals will hence be an important asset. Already during the 1980s reconfigurable receivers were developed for radio intelligence in the short wave range and the concept of software radio (SR) was born. A software defined radio (SDR) is a practical version of an SR: The received signals are sampled after a suitable band selection filter, usually in the base band or a low intermediate frequency band. The signal processing in both SR and SDR requires a considerable amount of concurrent processes. Since Petri nets (PNs) are both simple and strong tools for the description and the design of such concurrent processes, it is recommendable to deploy them for SDR. SDRs have paved the way towards cognitive radios (CRs), which are based on SDRs that additionally sense their environments, track changes, and react upon their findings. A CR is an autonomous unit in a communications environment that frequently exchanges information with the networks it is able to access as well as with other CRs. In this communication, the authors will introduce a realization concept for a CR which forms the basis of a hardware/firmware demonstrator developed by the authors. This demonstrator makes use of a digital signal processor (DSP) which forms the core of the design and flexibly programmable hardware accelerators based on field programmable gate arrays (FPGAs). The authors will describe the solution also in view of the recent developments of IEEE 802.2

Strategic Applications of Gene Expression: From Drug Discovery/Development to Bedside

ABSTRACT. Gene expression is useful for identifying the molecular signature of a disease and for correlating a pharmacodynamic marker with the dose-dependent cellular responses to exposure of a drug. Gene expression offers utility to guide drug discovery by illustrating engagement of the desired cellular pathways/networks, as well as avoidance of acting on the toxicological pathways. Successful employment of gene-expression signatures in the later stages of drug development depends on their linkage to clinically meaningful phenotypic characteristics and requires a biologically meaningful mechanism combined with a stringent statistical rigor. Much of the success in clinical drug development is hinged on predefining the signature genes for their fitness for purposes of application. Specific examples are highlighted to illustrate the breadth and depth of the potential utility of gene-expression signatures in drug discovery and clinical development to targeted therapeutics at the bedside