Applying Multivariate Adaptive Splines to Identify Genes With Expressions Varying After Diagnosis in Microarray Experiments
Conclusions:
In this article, we applied the nonparametric multivariate adaptive splines method to a publicly available gene expression data and successfully identified genes with expressions varying before and after breast cancer diagnosis. (Source: Cancer Informatics)
Source: Cancer Informatics - May 4, 2017 Category: Cancer & Oncology Authors: Fenghai Duan Ye Xu Source Type: research
A mixture copula Bayesian network model for multimodal genomic data
Gaussian Bayesian networks have become a widely used framework to estimate directed associations between joint Gaussian variables, where the network structure encodes the decomposition of multivariate normal density into local terms. However, the resulting estimates can be inaccurate when the normality assumption is moderately or severely violated, making it unsuitable for dealing with recent genomic data such as the Cancer Genome Atlas data. In the present paper, we propose a mixture copula Bayesian network model which provides great flexibility in modeling non-Gaussian and multimodal data for causal inference. The parame...
Source: Cancer Informatics - April 12, 2017 Category: Cancer & Oncology Authors: Qingyang. Zhang Xuan. Shi Source Type: research
miR-10a and miR-204 as a Potential Prognostic Indicator in Low-Grade Gliomas
This study aimed to identify and characterize microRNAs (miRNAs) that are related to radiosensitivity in low-grade gliomas (LGGs). The miRNA expression levels in radiosensitive and radioresistant LGGs were compared using The Cancer Genome Atlas database, and differentially expressed miRNAs were identified using the EBSeq package. The miRNA target genes were predicted using Web databases. Fifteen miRNAs were differentially expressed between the groups, with miR-10a and miR-204 being related to overall survival (OS) of patients with LGG. Patients with upregulated miR-10a expression had a higher mortality rate and shorter OS ...
Source: Cancer Informatics - April 12, 2017 Category: Cancer & Oncology Authors: Ju Cheol. Son Hyoung Oh. Jeong Deaui. Park Sang Gyoon. No Eun Kyeong. Lee Jaewon. Lee Hae Young. Chung Source Type: research
Bioinformatics Education in Pathology Training: Current Scope and Future Direction
Training anatomic and clinical pathology residents in the principles of bioinformatics is a challenging endeavor. Most residents receive little to no formal exposure to bioinformatics during medical education, and most of the pathology training is spent interpreting histopathology slides using light microscopy or focused on laboratory regulation, management, and interpretation of discrete laboratory data. At a minimum, residents should be familiar with data structure, data pipelines, data manipulation, and data regulations within clinical laboratories. Fellowship-level training should incorporate advanced principles unique...
Source: Cancer Informatics - April 10, 2017 Category: Cancer & Oncology Authors: Michael R. Clay Kevin E. Fisher Source Type: research
Therapeutic Interventions of Cancers Using Intrinsically Disordered Proteins as Drug Targets: c-Myc as Model System
The concept of protein intrinsic disorder has taken the driving seat to understand regulatory proteins in general. Reports suggest that in mammals nearly 75% of signalling proteins contain long disordered regions with greater than 30 amino acid residues. Therefore, intrinsically disordered proteins (IDPs) have been implicated in several human diseases and should be considered as potential novel drug targets. Moreover, intrinsic disorder provides a huge multifunctional capability to hub proteins such as c-Myc and p53. c-Myc is the hot spot for understanding and developing therapeutics against cancers and cancer stem cells. ...
Source: Cancer Informatics - March 15, 2017 Category: Cancer & Oncology Authors: Deepak. Kumar Nitin. Sharma Rajanish. Giri Source Type: research
Roadmap to a Comprehensive Clinical Data Warehouse for Precision Medicine Applications in Oncology
Leading institutions throughout the country have established Precision Medicine programs to support personalized treatment of patients. A cornerstone for these programs is the establishment of enterprise-wide Clinical Data Warehouses. Working shoulder-to-shoulder, a team of physicians, systems biologists, engineers, and scientists at Rutgers Cancer Institute of New Jersey have designed, developed, and implemented the Warehouse with information originating from data sources, including Electronic Medical Records, Clinical Trial Management Systems, Tumor Registries, Biospecimen Repositories, Radiology and Pathology archives, ...
Source: Cancer Informatics - March 1, 2017 Category: Cancer & Oncology Authors: David J. Foran Wenjin. Chen Huiqi. Chu Evita. Sadimin Doreen. Loh Gregory. Riedlinger Lauri A. Goodell Shridar. Ganesan Kim. Hirshfield Lorna. Rodriguez Robert S. DiPaola Source Type: research
Integrative Analysis of Gene Networks and Their Application to Lung Adenocarcinoma Studies
The construction of gene regulatory networks (GRNs) is an essential component of biomedical research to determine disease mechanisms and identify treatment targets. Gaussian graphical models (GGMs) have been widely used for constructing GRNs by inferring conditional dependence among a set of gene expressions. In practice, GRNs obtained by the analysis of a single data set may not be reliable due to sample limitations. Therefore, it is important to integrate multiple data sets from comparable studies to improve the construction of a GRN. In this article, we introduce an equivalent measure of partial correlation coefficients...
Source: Cancer Informatics - February 22, 2017 Category: Cancer & Oncology Authors: Sangin. Lee Faming. Liang Ling. Cai Guanghua. Xiao Source Type: research
Identification of Prognostic Genes and Pathways in Lung Adenocarcinoma Using a Bayesian Approach
In this study, we analyse The Cancer Genome Atlas lung adenocarcinoma data using a Bayesian approach incorporating the pathway information as well as the interconnections among genes. The top 11 pathways have been found to play significant roles in lung adenocarcinoma prognosis, including pathways in mitogen-activated protein kinase signalling, cytokine-cytokine receptor interaction, and ubiquitin-mediated proteolysis. We have also located key gene signatures such as RELB, MAP4K1, and UBE2C. These results indicate that the Bayesian approach may facilitate discovery of important genes and pathways that are tightly associate...
Source: Cancer Informatics - February 20, 2017 Category: Cancer & Oncology Authors: Yu. Jiang Yuan. Huang Yinhao. Du Yinjun. Zhao Jie. Ren Shuangge. Ma Cen. Wu Source Type: research
Significant Prognostic Features and Patterns of Somatic Mutations in Human Cancers
This study resulted in several novel findings. They include the following: (1) similar to previously reported cases in breast cancer, the mutations in exons 1 to 4 of TP53 were more lethal than those in exons 5 to 9 for the patients with lung adenocarcinomas; (2) TP53 mutants tended to be negatively selected in mammalian evolution, but the evolutionary conservation had various clinical implications for different cancers; (3) conserved correlation patterns (ie, consistent co-occurrence or consistent mutual exclusivity) between TP53 mutations and the alterations in several other cancer genes (ie, PIK3CA, PTEN, KRAS, APC, CDK...
Source: Cancer Informatics - February 19, 2017 Category: Cancer & Oncology Authors: Wensheng. Zhang Andrea. Edwards Erik K. Flemington Kun. Zhang Source Type: research
Improving Gastric Cancer Outcome Prediction Using Single Time-Point Artificial Neural Network Models
This study proposes an ANN model for predicting gastric cancer survivability, considering the censored data. Five separate single time-point ANN models were developed to predict the outcome of patients after 1, 2, 3, 4, and 5 years. The performance of ANN model in predicting the probabilities of death is consistently high for all time points according to the accuracy and the area under the receiver operating characteristic curve. (Source: Cancer Informatics)
Source: Cancer Informatics - February 15, 2017 Category: Cancer & Oncology Authors: Hamid. Nilsaz-Dezfouli Mohd Rizam. Abu-Bakar Jayanthi. Arasan Mohd Bakri. Adam Mohamad Amin. Pourhoseingholi Source Type: research
A Numerical Handling of the Boundary Conditions Imposed by the Skull on an Inhomogeneous Diffusion-Reaction Model of Glioblastoma Invasion Into the Brain: Clinical Validation Aspects
A novel explicit triscale reaction-diffusion numerical model of glioblastoma multiforme tumor growth is presented. The model incorporates the handling of Neumann boundary conditions imposed by the cranium and takes into account both the inhomogeneous nature of human brain and the complexity of the skull geometry. The finite-difference time-domain method is adopted. To demonstrate the workflow of a possible clinical validation procedure, a clinical case/scenario is addressed. A good agreement of the in silico calculated value of the doubling time (ie, the time for tumor volume to double) with the value of the same quantity ...
Source: Cancer Informatics - February 2, 2017 Category: Cancer & Oncology Authors: Georgios S. Stamatakos Stavroula G. Giatili Source Type: research
Unified Least Squares Methods for the Evaluation of Diagnostic Tests With the Gold Standard
The article proposes a unified least squares method to estimate the receiver operating characteristic (ROC) parameters for continuous and ordinal diagnostic tests, such as cancer biomarkers. The method is based on a linear model framework using the empirically estimated sensitivities and specificities as input “data.” It gives consistent estimates for regression and accuracy parameters when the underlying continuous test results are normally distributed after some monotonic transformation. The key difference between the proposed method and the method of Tang and Zhou lies in the response variable. The response variable...
Source: Cancer Informatics - February 2, 2017 Category: Cancer & Oncology Authors: Liansheng Larry. Tang Ao. Yuan John. Collins Xuan. Che Leighton. Chan Source Type: research
Epithelial Ovarian Cancer Diagnosis of Second-Harmonic Generation Images: A Semiautomatic Collagen Fibers Quantification Protocol
A vast number of human pathologic conditions are directly or indirectly related to tissular collagen structure remodeling. The nonlinear optical microscopy second-harmonic generation has become a powerful tool for imaging biological tissues with anisotropic hyperpolarized structures, such as collagen. During the past years, several quantification methods to analyze and evaluate these images have been developed. However, automated or semiautomated solutions are necessary to ensure objectivity and reproducibility of such analysis. This work describes automation and improvement methods for calculating the anisotropy (using fa...
Source: Cancer Informatics - February 2, 2017 Category: Cancer & Oncology Authors: Angel A. Zeitoune Johana SJ. Luna Kynthia. Sanchez Salas Luciana. Erbes Carlos L. Cesar Liliana ALA. Andrade Hernades F. Carvahlo F átima. Bottcher-Luiz Victor H. Casco Javier. Adur Source Type: research
Identification of Genetic and Epigenetic Variants Associated with Breast Cancer Prognosis by Integrative Bioinformatics Analysis
Conclusions: Thus, the analysis based on DNA methylation and SNPs have resulted in the identification of novel susceptible loci that hold prognostic relevance in breast cancer. (Source: Cancer Informatics)
Source: Cancer Informatics - January 8, 2017 Category: Cancer & Oncology Authors: Arunima Shilpi Yingtao Bi Segun Jung Samir K. Patra Ramana V. Davuluri Source Type: research
ROC Estimation from Clustered Data with an Application to Liver Cancer Data
In this article, we propose a regression model to compare the performances of different diagnostic methods having clustered ordinal test outcomes. The proposed model treats ordinal test outcomes (an ordinal categorical variable) as grouped-survival time data and uses random effects to explain correlation among outcomes from the same cluster. To compare different diagnostic methods, we introduce a set of covariates indicating diagnostic methods and compare their coefficients. We find that the proposed model defines a Lehmann family and can also introduce a location-scale family of a receiver operating characteristic (ROC) c...
Source: Cancer Informatics - December 21, 2016 Category: Cancer & Oncology Authors: Joungyoun Kim Sung-Cheol Yun Johan Lim Moo-Song Lee Won Son DoHwan Park Source Type: research
