Disease Biomarker Query from RNA-Seq Data
As a revolutionary way to unveil transcription, RNA-Seq technologies are challenging bioinformatics for its large data volumes and complexities. A large number of computational models have been proposed for differential expression (DE) analysis and normalization from different standing points. However, there were no studies available yet to conduct disease biomarker discovery for this type of high-resolution digital gene expression data, which will actually be essential to explore its potential in clinical bioinformatics. Although there were many biomarker discovery algorithms available in traditional omics communities, th...
Source: Cancer Informatics - October 14, 2014 Category: Cancer & Oncology Authors: Henry HanXiaoqian Jiang Source Type: research
Prediction of MicroRNA Precursors Using Parsimonious Feature Sets
MicroRNAs (miRNAs) are a class of short noncoding RNAs that regulate gene expression through base pairing with messenger RNAs. Due to the interest in studying miRNA dysregulation in disease and limits of validated miRNA references, identification of novel miRNAs is a critical task. The performance of different models to predict novel miRNAs varies with the features chosen as predictors. However, no study has systematically compared published feature sets. We constructed a comprehensive feature set using the minimum free energy of the secondary structure of precursor miRNAs, a set of nucleotide-structure triplets, and addit...
Source: Cancer Informatics - October 14, 2014 Category: Cancer & Oncology Authors: Petra StepanowskyEric LevyJihoon KimXiaoqian JiangLucila Ohno-Machado Source Type: research
Exploiting Literature-derived Knowledge and Semantics to Identify Potential Prostate Cancer Drugs
In this study, we report on the performance of an automated approach to discovery of potential prostate cancer drugs from the biomedical literature. We used the semantic relationships in SemMedDB, a database of structured knowledge extracted from all MEDLINE citations using SemRep, to extract potential relationships using knowledge of cancer drugs pathways. Two cancer drugs pathway schemas were constructed using these relationships extracted from SemMedDB. Through both pathway schemas, we found drugs already used for prostate cancer therapy and drugs not currently listed as the prostate cancer medications. Our study demons...
Source: Cancer Informatics - October 14, 2014 Category: Cancer & Oncology Authors: Rui ZhangMichael J. CairelliMarcelo FiszmanHalil KilicogluThomas C. RindfleschSerguei V. Pakhomov Genevieve B. Melton Source Type: research
RNAseqPS: A Web Tool for Estimating Sample Size and Power for RNAseq Experiment
Sample size and power determination is the first step in the experimental design of a successful study. Sample size and power calculation is required for applications for National Institutes of Health (NIH) funding. Sample size and power calculation is well established for traditional biological studies such as mouse model, genome wide association study (GWAS), and microarray studies. Recent developments in high-throughput sequencing technology have allowed RNAseq to replace microarray as the technology of choice for high-throughput gene expression profiling. However, the sample size and power analysis of RNAseq technology...
Source: Cancer Informatics - October 13, 2014 Category: Cancer & Oncology Authors: Yan GuoShilin ZhaoChung-I LiQuanhu ShengYu Shyr Source Type: research
Inferring the Effects of Honokiol on the Notch Signaling Pathway in SW480 Colon Cancer Cells
In a tumor cell, the development of acquired therapeutic resistance and the ability to survive in extracellular environments that differ from the primary site are the result of molecular adaptations in potentially highly plastic molecular networks. The accurate prediction of intracellular networks in a tumor remains a difficult problem in cancer informatics. In order to make truly rational patient-driven therapeutic decisions, it will be critical to develop methodologies that can accurately infer the molecular circuitry in the cells of a specific tumor. Despite enormous heterogeneity, cellular networks elicit deterministic...
Source: Cancer Informatics - October 13, 2014 Category: Cancer & Oncology Authors: Michelle L. WynnNikita ConsulSofia D. MerajverSantiago Schnell Source Type: research
Feasibility and Implementation of a Literature Information Management System for Human Papillomavirus in Head and Neck Cancers with Imaging
This work examines the feasibility and implementation of information service-orientated architecture (ISOA) on an emergent literature domain of human papillomavirus, head and neck cancer, and imaging. From this work, we examine the impact of cancer informatics and generate a full set of summarizing clinical pearls. Additionally, we describe how such an ISOA creates potential benefits in informatics education, enhancing utility for creating enduring digital content in this clinical domain. (Source: Cancer Informatics)
Source: Cancer Informatics - October 13, 2014 Category: Cancer & Oncology Authors: Dee H. WuChance L. MatthiesenAnthony M. AllemanAaron L. FournierTyler C. Gunter Source Type: research
Modeling Signal Transduction from Protein Phosphorylation to Gene Expression
In this study, we investigate computational methods that integrate proteomics and transcriptomic data to identify signaling pathways transmitting signals in response to specific stimuli. Such methods can be applied to cancer genomic data to infer perturbed signaling pathways.
Method: We proposed a novel Bayesian Network (BN) framework to integrate transcriptomic data with proteomic data reflecting protein phosphorylation states for the purpose of identifying the pathways transmitting the signal of diverse stimuli in rat and human cells. We represented the proteins and genes as nodes in a BN in which edges reflect the regul...
Source: Cancer Informatics - October 13, 2014 Category: Cancer & Oncology Authors: Chunhui CaiLujia ChenXia JiangXinghua Lu Source Type: research
Text Mining in Cancer Gene and Pathway Prioritization
Prioritization of cancer implicated genes has received growing attention as an effective way to reduce wet lab cost by computational analysis that ranks candidate genes according to the likelihood that experimental verifications will succeed. A multitude of gene prioritization tools have been developed, each integrating different data sources covering gene sequences, differential expressions, function annotations, gene regulations, protein domains, protein interactions, and pathways. This review places existing gene prioritization tools against the backdrop of an integrative Omic hierarchy view toward cancer and focuses on...
Source: Cancer Informatics - October 13, 2014 Category: Cancer & Oncology Authors: Yuan LuoGregory RiedlingerPeter Szolovits Source Type: research
Mining Cancer-Specific Disease Comorbidities from a Large Observational Health Database
In this study, we systematically mine and analyze cancer-specific comorbidity from the FDA Adverse Event Reporting System. We stratified 3,354,043 patients based on age and gender, and developed a network-based approach to extract comorbidity patterns from each patient group. We compared the comorbidity patterns among different patient groups and investigated the effect of age and gender on cancer comorbidity patterns. The results demonstrated that the comorbidity relationships between cancers and non-cancer diseases largely depend on age and gender. A few exceptions are depression, anxiety, and metabolic syndrome, whose c...
Source: Cancer Informatics - October 13, 2014 Category: Cancer & Oncology Authors: Yang ChenRong Xu Source Type: research
Optimization of Network Topology in Computer-Aided Detection Schemes Using Phased Searching with NEAT in a Time-Scaled Framework
In this study, we analyzed a classifier that evolves ANNs using genetic algorithms (GAs), which combines feature selection with the learning task. The classifier named “Phased Searching with NEAT in a Time-Scaled Framework” was analyzed using a dataset with 800 malignant and 800 normal tissue regions in a 10-fold cross-validation framework. The classification performance measured by the area under a receiver operating characteristic (ROC) curve was 0.856 ± 0.029. The result was also compared with four other well-established classifiers that include fixed-topology ANNs, support vector machines (SVMs), linear discrimina...
Source: Cancer Informatics - October 13, 2014 Category: Cancer & Oncology Authors: Maxine TanJiantao PuBin Zheng Source Type: research
Inferring Aberrant Signal Transduction Pathways in Ovarian Cancer from TCGA Data
This paper concerns a new method for identifying aberrant signal transduction pathways (STPs) in cancer using case/control gene expression-level datasets, and applying that method and an existing method to an ovarian carcinoma dataset. Both methods identify STPs that are plausibly linked to all cancers based on current knowledge. Thus, the paper is most appropriate for the cancer informatics community. Our hypothesis is that STPs that are altered in tumorous tissue can be identified by applying a new Bayesian network (BN)-based method (causal analysis of STP aberration (CASA)) and an existing method (signaling pathway impa...
Source: Cancer Informatics - October 13, 2014 Category: Cancer & Oncology Authors: Richard NeapolitanXia Jiang Source Type: research
Introductory Editorial: Computational Advances in Cancer Informatics (A)
(Source: Cancer Informatics)
Source: Cancer Informatics - October 13, 2014 Category: Cancer & Oncology Authors: Xiaoqian JiangRui ChenSamuel ChengXia JiangBairong ShenRong XuSong Yi Source Type: research
CSNK1A1 and Gli2 as Novel Targets Identified Through an Integrative Analysis of Gene Expression Data, Protein-Protein Interaction and Pathways Networks in Glioblastoma Tumors: Can These Two Be Antagonistic Proteins?
Glioblastoma (GBM) is the malignant form of glioma, and the interplay of different pathways working in concert in GBM development and progression needs to be fully understood. Wnt signaling and sonic hedgehog (SHH) signaling pathways, having basic similarities, are among the major pathways aberrantly activated in GBM, and hence, need to be targeted. It becomes imperative, therefore, to explore the functioning of these pathways in context of each other in GBM. An integrative approach may help provide new biological insights, as well as solve the problem of identifying common drug targets for simultaneous targeting of these ...
Source: Cancer Informatics - October 13, 2014 Category: Cancer & Oncology Authors: Seema Mishra Source Type: research
Combined Benefit of Prediction and Treatment: A Criterion for Evaluating Clinical Prediction Models
Clinical treatment decisions rely on prognostic evaluation of a patient's future health outcomes. Thus, predictive models under different treatment options are key factors for making good decisions. While many criteria exist for judging the statistical quality of a prediction model, few are available to measure its clinical utility. As a consequence, we may find that the addition of a clinical covariate or biomarker improves the statistical quality of the model, but has little effect on its clinical usefulness. We focus on the setting where a treatment decision may reduce a patient's risk of a poor outcome, but also comes ...
Source: Cancer Informatics - October 5, 2014 Category: Cancer & Oncology Authors: Dean BillheimerEugene W. GernerChristine E. McLarenBonnie LaFleur Source Type: research
Bayesian Disease Classification Using Copy Number Data
DNA copy number variations (CNVs) have been shown to be associated with cancer development and progression. The detection of these CNVs has the potential to impact the basic knowledge and treatment of many types of cancers, and can play a role in the discovery and development of molecular-based personalized cancer therapies. One of the most common types of high-resolution chromosomal microarrays is array-based comparative genomic hybridization (aCGH) methods that assay DNA CNVs across the whole genomic landscape in a single experiment. In this article we propose methods to use aCGH profiles to predict disease states. We em...
Source: Cancer Informatics - October 1, 2014 Category: Cancer & Oncology Authors: Subharup GuhaYuan Jiand Veerabhadran Baladandayuthapani Source Type: research
