Computational Systems Bioinformatics and Bioimaging for Pathway Analysis and Drug Screening

Optimal Bayesian Filtering for Biomarker Discovery: Performance and Robustness

Optimal Bayesian feature filtering (OBF) is a fast and memory-efficient algorithm that optimally identifies markers with distributional differences between treatment groups under Gaussian models. Here, we study the performance and robustness of OBF for biomarker discovery. Our contributions are twofold: (1) we examine how OBF performs on data that violates modeling assumptions, and (2) we provide guidelines on how to set input parameters for robust performance. Contribution (1) addresses an important, relevant, and commonplace problem in computational biology, where it is often impossible to validate an algorithm's core assumptions. To accomplish both tasks, we present a battery of simulations that implement OBF with different inputs and challenge each assumption made by OBF. In particular, we examine the robustness of OBF with respect to incorrect input parameters, false independence, imbalanced sample size, and we address the Gaussianity assumption by considering performance on an extensive family of non-Gaussian distributions. We address advantages and disadvantages between different priors and optimization criteria throughout. Finally, we evaluate the utility of OBF in biomarker discovery using acute myeloid leukemia (AML) and colon cancer microarray datasets, and show that OBF is successful at identifying well-known biomarkers for these diseases that rank low under moderated t-test.

Metallomic and metalloproteomic strategies in elucidating the molecular mechanisms of metallodrugs

Advances in the mechanistic studies of metallodrugs by metallomic and metalloproteomic approaches will improve our understanding of the mechanism of action and allow more metallodrugs to be developed.

microRNA Regulation and Its Consequences in Cancer

MicroRNA (miRNA) function has been studied extensively in the last two decades. These short, non-coding RNAs influence a variety of cellular processes through repression of target genes. With the number of genes that a single miRNA can target, the biological effects of one miRNA alone can be vast. In cancer, aberrant miRNA expression is ubiquitous and consequently it can provoke progression of the disease. Though much is known about the downstream effects of miRNA, the mechanisms that control the level of miRNA expression itself are not well documented. In this review, we will focus on how miRNAs are regulated as well as potential therapeutic targets that can be exploited for cancer therapy.

Image analysis tools and emerging algorithms for expression proteomics

Since their origins in academic endeavours in the 1970s, computational analysis tools have matured into a number of established commercial packages that underpin research in expression proteomics. In this paper we describe the image analysis pipeline for the established 2-DE technique of protein separation, and by first covering signal analysis for MS, we also explain the current image analysis workflow for the emerging high-throughput 'shotgun' proteomics platform of LC coupled to MS (LC/MS). The bioinformatics challenges for both methods are illustrated and compared, whereas existing commercial and academic packages and their workflows are described from both a user's and a technical perspective. Attention is given to the importance of sound statistical treatment of the resultant quantifications in the search for differential expression. Despite wide availability of proteomics software, a number of challenges have yet to be overcome regarding algorithm accuracy, objectivity and automation, generally due to deterministic spot-centric approaches that discard information early in the pipeline, propagating errors. We review recent advances in signal and image analysis algorithms in 2-DE, MS, LC/MS and Imaging MS. Particular attention is given to wavelet techniques, automated image-based alignment and differential analysis in 2-DE, Bayesian peak mixture models, and functional mixed modelling in MS, and group-wise consensus alignment methods for LC/MS.

Transcriptional regulation of mammalian miRNA genes

MicroRNAs (miRNAs) are members of a growing family of non-coding transcripts, 21-23 nucleotides long, which regulate a diverse collection of biological processes and various diseases by RNA-mediated gene-silencing mechanisms. While currently many studies focus on defining the regulatory functions of miRNAs, few are directed towards how miRNA genes are themselves transcriptionally regulated. Recent studies of miRNA transcription have elucidated RNA polymerase II as the major polymerase of miRNAs, however, little is known of the structural features of miRNA promoters, especially those of mammalian miRNAs. Here, we review the current literature regarding features conserved among miRNA promoters useful for their detection and the current novel methodologies available to enable researchers to advance our understanding of the transcriptional regulation of miRNA genes.

Imaging informatics for personalised medicine: applications and challenges

Imaging informatics has emerged as a major research theme in biomedicine in the last few decades. Currently, personalised, predictive and preventive patient care is believed to be one of the top priorities in biomedical research and practice. Imaging informatics plays a major role in biomedicine studies. This paper reviews main applications and challenges of imaging informatics in biomedicine.