Omics-based molecular target and biomarker identification.
ABSTRACT Genomic, proteomic, and other omic-based approaches are now broadly used in biomedical research to facilitate the understanding of disease mechanisms and identification of molecular targets and biomarkers for therapeutic and diagnostic development. While the Omics technologies and bioinformatics tools for analyzing Omics data are rapidly advancing, the functional analysis and interpretation of the data remain challenging due to the inherent nature of the generally long workflows of Omics experiments. We adopt a strategy that emphasizes the use of curated knowledge resources coupled with expert-guided examination and interpretation of Omics data for the selection of potential molecular targets. We describe a downstream workflow and procedures for functional analysis that focus on biological pathways, from which molecular targets can be derived and proposed for experimental validation.
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- "Various machine learning techniques have been proposed to identify biomarkers that can accurately predict phenotypic classes by learning the cryptic pattern from -omics data . There are three main categories of machine learning methods for biomarker selection and phenotypic classification , namely, filter, wrapper, and embedded . "
ABSTRACT: Identification of suitable biomarkers for accurate prediction of phenotypic outcomes is a goal for personalized medicine. However, current machine learning approaches are either too complex or perform poorly. Here, a novel two-step machine-learning framework is presented to address this need. First, a Naïve Bayes estimator is used to rank features from which the top-ranked will most likely contain the most informative features for prediction of the underlying biological classes. The top-ranked features are then used in a Hidden Naïve Bayes classifier to construct a classification prediction model from these filtered attributes. In order to obtain the minimum set of the most informative biomarkers, the bottom-ranked features are successively removed from the Naïve Bayes-filtered feature list one at a time, and the classification accuracy of the Hidden Naïve Bayes classifier is checked for each pruned feature set. The performance of the proposed two-step Bayes classification framework was tested on different types of -omics datasets including gene expression microarray, single nucleotide polymorphism microarray (SNParray), and surface-enhanced laser desorption/ionization time-of-flight (SELDI-TOF) proteomic data. The proposed two-step Bayes classification framework was equal to and, in some cases, outperformed other classification methods in terms of prediction accuracy, minimum number of classification markers, and computational time.09/2013; 2013:148014. DOI:10.1155/2013/148014
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- "To extract further information from these experimental data, they were linked with an AIB1 interaction network generated from published data . A computational global AIB1 protein interaction network can be constructed from 91 AIB1 interaction partners (first neighbors) based on the literature published since AIB1 was first described in 1997 . "
ABSTRACT: Estrogen is a known growth promoter for estrogen receptor (ER)-positive breast cancer cells. Paradoxically, in breast cancer cells that have been chronically deprived of estrogen stimulation, re-introduction of the hormone can induce apoptosis. Here, we sought to identify signaling networks that are triggered by estradiol (E2) in isogenic MCF-7 breast cancer cells that undergo apoptosis (MCF-7:5C) versus cells that proliferate upon exposure to E2 (MCF-7). The nuclear receptor co-activator AIB1 (Amplified in Breast Cancer-1) is known to be rate-limiting for E2-induced cell survival responses in MCF-7 cells and was found here to also be required for the induction of apoptosis by E2 in the MCF-7:5C cells. Proteins that interact with AIB1 as well as complexes that contain tyrosine phosphorylated proteins were isolated by immunoprecipitation and identified by mass spectrometry (MS) at baseline and after a brief exposure to E2 for two hours. Bioinformatic network analyses of the identified protein interactions were then used to analyze E2 signaling pathways that trigger apoptosis versus survival. Comparison of MS data with a computationally-predicted AIB1 interaction network showed that 26 proteins identified in this study are within this network, and are involved in signal transduction, transcription, cell cycle regulation and protein degradation. G-protein-coupled receptors, PI3 kinase, Wnt and Notch signaling pathways were most strongly associated with E2-induced proliferation or apoptosis and are integrated here into a global AIB1 signaling network that controls qualitatively distinct responses to estrogen.PLoS ONE 06/2011; 6(6):e20410. DOI:10.1371/journal.pone.0020410 · 3.23 Impact Factor
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ABSTRACT: Ionizing radiation is a known human carcinogen that can induce a variety of biological effects depending on the physical nature, duration, doses and dose-rates of exposure. However, the magnitude of health risks at low doses and dose-rates (below 100mSv and/or 0.1mSvmin(-1)) remains controversial due to a lack of direct human evidence. It is anticipated that significant insights will emerge from the integration of epidemiological and biological research, made possible by molecular epidemiology studies incorporating biomarkers and bioassays. A number of these have been used to investigate exposure, effects and susceptibility to ionizing radiation, albeit often at higher doses and dose rates, with each reflecting time-limited cellular or physiological alterations. This review summarises the multidisciplinary work undertaken in the framework of the European project DoReMi (Low Dose Research towards Multidisciplinary Integration) to identify the most appropriate biomarkers for use in population studies. In addition to logistical and ethical considerations for conducting large-scale epidemiological studies, we discuss the relevance of their use for assessing the effects of low dose ionizing radiation exposure at the cellular and physiological level. We also propose a temporal classification of biomarkers that may be relevant for molecular epidemiology studies which need to take into account the time elapsed since exposure. Finally, the integration of biology with epidemiology requires careful planning and enhanced discussions between the epidemiology, biology and dosimetry communities in order to determine the most important questions to be addressed in light of pragmatic considerations including the appropriate population to be investigated (occupationally, environmentally or medically exposed), and study design. The consideration of the logistics of biological sample collection, processing and storing and the choice of biomarker or bioassay, as well as awareness of potential confounding factors, are also essential.Mutation Research/Fundamental and Molecular Mechanisms of Mutagenesis 06/2012; 751(2):258-86. DOI:10.1016/j.mrrev.2012.05.003 · 4.44 Impact Factor