A Bayesian Framework to Improve MicroRNA Target Prediction by Incorporating External Information
In this study, we integrated four important sequence and structural features of miRNA targeting with paired miRNA and mRNA expression data to improve miRNA-target prediction in a Bayesian framework. We have applied this approach to a gene-expression study of liver cancer patients and examined the posterior probability of each miRNA–mRNA interaction being functional in the development of liver cancer. Our method achieved better performance, in terms of the number of true targets identified, than did other methods. (Source: Cancer Informatics)
Source: Cancer Informatics - November 18, 2014 Category: Cancer & Oncology Authors: Zixing WangWenlong XuHaifeng ZhuYin Liu Source Type: research
Type I Error Control for Tree Classification
Binary tree classification has been useful for classifying the whole population based on the levels of outcome variable that is associated with chosen predictors. Often we start a classification with a large number of candidate predictors, and each predictor takes a number of different cutoff values. Because of these types of multiplicity, binary tree classification method is subject to severe type I error probability. Nonetheless, there have not been many publications to address this issue. In this paper, we propose a binary tree classification method to control the probability to accept a predictor below certain level, s...
Source: Cancer Informatics - November 16, 2014 Category: Cancer & Oncology Authors: Sin-Ho JungYong ChenHongshik Ahn Source Type: research
Conducting Retrospective Ontological Clinical Trials in ICD-9-CM in the Age of ICD-10-CM
Conclusions: The mandated delay is an opportunity for organizations to better understand areas of financial risk with regards to data management via backward coding. Our methodology is relevant for all healthcare-related coding data, and can be replicated by organizations as a strategy to mitigate financial risk. (Source: Cancer Informatics)
Source: Cancer Informatics - November 9, 2014 Category: Cancer & Oncology Authors: Neeta K. VenepalliArdaman ShergillParvaneh DorestaniAndrew D. Boyd Source Type: research
In Silico Prediction of Synthetic Lethality by Meta-Analysis of Genetic Interactions, Functions, and Pathways in Yeast and Human Cancer
A major goal in cancer medicine is to find selective drugs with reduced side effect. A pair of genes is called synthetic lethality (SL) if mutations of both genes will kill a cell while mutation of either gene alone will not. Hence, a gene in SL interactions with a cancer-specific mutated gene will be a promising drug target with anti-cancer selectivity. Wet-lab screening approach is still so costly that even for yeast only a small fraction of gene pairs has been covered. Computational methods are therefore important for large-scale discovery of SL interactions. Most existing approaches focus on individual features or mach...
Source: Cancer Informatics - November 5, 2014 Category: Cancer & Oncology Authors: Min WuXuejuan LiFan ZhangXiaoli LiChee-Keong KwohJie Zheng Source Type: research
A Novel Subset of Human Tumors That Simultaneously Overexpress Multiple E2F-responsive Genes Found in Breast, Ovarian, and Prostate Cancers
Reasoning that overexpression of multiple E2F-responsive genes might be a useful marker for RB1 dysfunction, we compiled a list of E2F-responsive genes from the literature and evaluated their expression in publicly available gene expression microarray data of patients with breast cancer, serous ovarian cancer, and prostate cancer. In breast cancer, a group of tumors was identified, each of which simultaneously overexpressed multiple E2F-responsive genes. Seventy percent of these genes were concerned with cell cycle progression, DNA repair, or mitosis. These E2F-responsive gene overexpressing (ERGO) tumors frequently exhibi...
Source: Cancer Informatics - November 3, 2014 Category: Cancer & Oncology Authors: Stanley E. ShackneySalim Akhter ChowdhuryRussell Schwartz Source Type: research
Predicting Cancer Prognosis Using Functional Genomics Data Sets
Elucidating the molecular basis of human cancers is an extremely complex and challenging task. A wide variety of computational tools and experimental techniques have been used to address different aspects of this characterization. One major hurdle faced by both clinicians and researchers has been to pinpoint the mechanistic basis underlying a wide range of prognostic outcomes for the same type of cancer. Here, we provide an overview of various computational methods that have leveraged different functional genomics data sets to identify molecular signatures that can be used to predict prognostic outcome for various human ca...
Source: Cancer Informatics - November 2, 2014 Category: Cancer & Oncology Authors: Jishnu DasKaitlyn M. GayvertHaiyuan Yu Source Type: research
Correction to: Prognostic Features of Signal Transducer and Activator of Transcription 3 in an ER(+) Breast Cancer Model System
(Source: Cancer Informatics)
Source: Cancer Informatics - November 2, 2014 Category: Cancer & Oncology Authors: Li-Yu D. LiuLi-Yun ChangWen-Hung KuoHsiao-Lin HwaYi-Shing LinMeei-Huey JengDon A. RothKing-Jen ChangFon-Jou Hsieh Source Type: research
Propensity Score Method for Partially Matched Omics Studies
This paper focuses on the problem of partially matched samples in the presence of confounders. We propose using propensity score matching to adjust for confounding factors for the subset of data with incomplete pairs, followed by integrating the P-values computed from the complete and incomplete paired samples, respectively. Several simulations and a case study on DNA methylation are considered to evaluate the operating characteristics of the proposed method. (Source: Cancer Informatics)
Source: Cancer Informatics - October 29, 2014 Category: Cancer & Oncology Authors: Pei-Fen Kuan Source Type: research
A Pan-Cancer Modular Regulatory Network Analysis to Identify Common and Cancer-Specific Network Components
We present a module- and network-based characterization of transcriptional patterns in six different cancers being studied in TCGA: breast, colon, rectal, kidney, ovarian, and endometrial. Our approach uses a recently developed regulatory network reconstruction algorithm, modular regulatory network learning with per gene information (MERLIN), within a stability selection framework to predict regulators for individual genes and gene modules. Our module-based analysis identifies a common theme of immune system processes in each cancer study, with modules statistically enriched for immune response processes as well as targets...
Source: Cancer Informatics - October 28, 2014 Category: Cancer & Oncology Authors: Sara A. KnaackAlireza Fotuhi Siahpiraniand Sushmita Roy Source Type: research
Learning Dysregulated Pathways in Cancers from Differential Variability Analysis
Analysis of gene sets can implicate activity in signaling pathways that is responsible for cancer initiation and progression, but is not discernible from the analysis of individual genes. Multiple methods and software packages have been developed to infer pathway activity from expression measurements for set of genes targeted by that pathway. Broadly, three major methodologies have been proposed: over-representation, enrichment, and differential variability. Both over-representation and enrichment analyses are effective techniques to infer differentially regulated pathways from gene sets with relatively consistent differen...
Source: Cancer Informatics - October 23, 2014 Category: Cancer & Oncology Authors: Bahman AfsariDonald GemanElana J. Fertig Source Type: research
eMBI: Boosting Gene Expression-based Clustering for Cancer Subtypes
In this study, we developed many effective strategies to improve MBI and designed a new program called enhanced MBI (eMBI), which is more effective and efficient to identify cancer subtypes. Our tests on several gene expression profiling datasets of cancer patients consistently indicate that eMBI achieves significant improvements in comparison with MBI, in terms of cancer subtype prediction accuracy, robustness, and running time. In addition, the performance of eMBI is much better than another widely used matrix factorization method called nonnegative matrix factorization (NMF) and the method of hierarchical clustering, wh...
Source: Cancer Informatics - October 21, 2014 Category: Cancer & Oncology Authors: Zheng ChangZhenjia WangCody AshbyChuan ZhouGuojun LiShuzhong ZhangXiuzhen Huang Source Type: research
Classification of Images Acquired with Colposcopy Using Artificial Neural Networks
Conclusion: Although the preliminary results still exhibit an average efficiency, the present approach is an innovative and promising technique that should be deeply explored in the context of the present study. (Source: Cancer Informatics)
Source: Cancer Informatics - October 21, 2014 Category: Cancer & Oncology Authors: Priscyla W. SimõesNarjara B. IzumiRamon S. CasagrandeRamon VensonCarlos D. VeroneziGustavo P. MorettiEdroaldo L. da RochaCristian CechinelLuciane B. CerettaEros ComunelloPaulo J. MartinsRogério A. CasagrandeMaria L. SnoeyerSandra A. Manenti Source Type: research
The Axis of Progression of Disease
Starting with genetic or environmental perturbations, disease progression can involve a linear sequence of changes within individual cells. More often, however, a labyrinth of branching consequences emanates from the initial events. How can one repair an entity so fine and so complex that its organization and functions are only partially known? How, given the many redundancies of metabolic pathways, can interventions be effective before the last redundant element has been irreversibly damaged? Since progression ultimately proceeds beyond a point of no return, therapeutic goals must target earlier events. A key goal is ther...
Source: Cancer Informatics - October 19, 2014 Category: Cancer & Oncology Authors: Alan M. TartakoffDi Wu Source Type: research
Co-expression Network Analysis of Human lncRNAs and Cancer Genes
We used gene co-expression network analysis to functionally annotate long noncoding RNAs (lncRNAs) and identify their potential cancer associations. The integrated microarray data set from our previous study was used to extract the expression profiles of 1,865 lncRNAs. Known cancer genes were compiled from the Catalogue of Somatic Mutations in Cancer and UniProt databases. Co-expression analysis identified a list of previously uncharacterized lncRNAs that showed significant correlation in expression with core cancer genes. To further annotate the lncRNAs, we performed a weighted gene co-expression network analysis, which r...
Source: Cancer Informatics - October 19, 2014 Category: Cancer & Oncology Authors: Steven B. CogillLiangjiang Wang Source Type: research
Network Analysis of Cancer-focused Association Network Reveals Distinct Network Association Patterns
Cancer is a complex and heterogeneous disease. Genetic methods have uncovered thousands of complex tissue-specific mutation-induced effects and identified multiple disease gene targets. Important associations between cancer and other biological entities (eg, genes and drugs) in cancer network, however, are usually scattered in biomedical publications. Systematic analyses of these cancer-specific associations can help highlight the hidden associations between different cancer types and related genes/drugs. In this paper, we proposed a novel network-based computational framework to identify statistically over-expressed subne...
Source: Cancer Informatics - October 16, 2014 Category: Cancer & Oncology Authors: Yuji ZhangCui Tao Source Type: research
