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Cancer Informatics

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https://www.readbyqxmd.com/read/28469393/bioinformatics-education-in-pathology-training-current-scope-and-future-direction
#1
REVIEW
Michael R Clay, Kevin E Fisher
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...
2017: Cancer Informatics
https://www.readbyqxmd.com/read/28469392/mir-10a-and-mir-204-as-a-potential-prognostic-indicator-in-low-grade-gliomas
#2
Ju Cheol Son, Hyoung Oh Jeong, Deaui Park, Sang Gyoon No, Eun Kyeong Lee, Jaewon Lee, Hae Young Chung
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...
2017: Cancer Informatics
https://www.readbyqxmd.com/read/28469391/a-mixture-copula-bayesian-network-model-for-multimodal-genomic-data
#3
Qingyang Zhang, Xuan Shi
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...
2017: Cancer Informatics
https://www.readbyqxmd.com/read/28469390/therapeutic-interventions-of-cancers-using-intrinsically-disordered-proteins-as-drug-targets-c-myc-as-model-system
#4
REVIEW
Deepak Kumar, Nitin Sharma, Rajanish Giri
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...
2017: Cancer Informatics
https://www.readbyqxmd.com/read/28469389/roadmap-to-a-comprehensive-clinical-data-warehouse-for-precision-medicine-applications-in-oncology
#5
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
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, and Next Generation Sequencing services...
2017: Cancer Informatics
https://www.readbyqxmd.com/read/28469388/significant-prognostic-features-and-patterns-of-somatic-tp53-mutations-in-human-cancers
#6
Wensheng Zhang, Andrea Edwards, Erik K Flemington, Kun Zhang
TP53 is the most frequently altered gene in human cancers. Numerous retrospective studies have related its mutation and abnormal p53 protein expression to poor patient survival. Nonetheless, the clinical significance of TP53 (p53) status has been a controversial issue. In this work, we aimed to characterize TP53 somatic mutations in tumor cells across multiple cancer types, primarily focusing on several less investigated features of the mutation spectra, and determine their prognostic implications. We performed an integrative study on the clinically annotated genomic data released by The Cancer Genome Atlas...
2017: Cancer Informatics
https://www.readbyqxmd.com/read/28469387/integrative-analysis-of-gene-networks-and-their-application-to-lung-adenocarcinoma-studies
#7
Sangin Lee, Faming Liang, Ling Cai, Guanghua Xiao
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...
2017: Cancer Informatics
https://www.readbyqxmd.com/read/28469386/epithelial-ovarian-cancer-diagnosis-of-second-harmonic-generation-images-a-semiautomatic-collagen-fibers-quantification-protocol
#8
Angel A Zeitoune, Johana Sj Luna, Kynthia Sanchez Salas, Luciana Erbes, Carlos L Cesar, Liliana Ala Andrade, Hernades F Carvahlo, Fátima Bottcher-Luiz, Victor H Casco, Javier Adur
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...
2017: Cancer Informatics
https://www.readbyqxmd.com/read/28469385/unified-least-squares-methods-for-the-evaluation-of-diagnostic-tests-with-the-gold-standard
#9
REVIEW
Liansheng Larry Tang, Ao Yuan, John Collins, Xuan Che, Leighton Chan
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...
2017: Cancer Informatics
https://www.readbyqxmd.com/read/28469384/improving-gastric-cancer-outcome-prediction-using-single-time-point-artificial-neural-network-models
#10
Hamid Nilsaz-Dezfouli, Mohd Rizam Abu-Bakar, Jayanthi Arasan, Mohd Bakri Adam, Mohamad Amin Pourhoseingholi
In cancer studies, the prediction of cancer outcome based on a set of prognostic variables has been a long-standing topic of interest. Current statistical methods for survival analysis offer the possibility of modelling cancer survivability but require unrealistic assumptions about the survival time distribution or proportionality of hazard. Therefore, attention must be paid in developing nonlinear models with less restrictive assumptions. Artificial neural network (ANN) models are primarily useful in prediction when nonlinear approaches are required to sift through the plethora of available information...
2017: Cancer Informatics
https://www.readbyqxmd.com/read/28469383/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
#11
Georgios S Stamatakos, Stavroula G Giatili
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 based on tomographic imaging data has been observed...
2017: Cancer Informatics
https://www.readbyqxmd.com/read/28096648/identification-of-genetic-and-epigenetic-variants-associated-with-breast-cancer-prognosis-by-integrative-bioinformatics-analysis
#12
Arunima Shilpi, Yingtao Bi, Segun Jung, Samir K Patra, Ramana V Davuluri
INTRODUCTION: Breast cancer being a multifaceted disease constitutes a wide spectrum of histological and molecular variability in tumors. However, the task for the identification of these variances is complicated by the interplay between inherited genetic and epigenetic aberrations. Therefore, this study provides an extrapolate outlook to the sinister partnership between DNA methylation and single-nucleotide polymorphisms (SNPs) in relevance to the identification of prognostic markers in breast cancer...
2017: Cancer Informatics
https://www.readbyqxmd.com/read/28050126/roc-estimation-from-clustered-data-with-an-application-to-liver-cancer-data
#13
Joungyoun Kim, Sung-Cheol Yun, Johan Lim, Moo-Song Lee, Won Son, DoHwan Park
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) curve...
2016: Cancer Informatics
https://www.readbyqxmd.com/read/27980387/comparison-of-three-information-sources-for-smoking-information-in-electronic-health-records
#14
Liwei Wang, Xiaoyang Ruan, Ping Yang, Hongfang Liu
OBJECTIVE: The primary aim was to compare independent and joint performance of retrieving smoking status through different sources, including narrative text processed by natural language processing (NLP), patient-provided information (PPI), and diagnosis codes (ie, International Classification of Diseases, Ninth Revision [ICD-9]). We also compared the performance of retrieving smoking strength information (ie, heavy/light smoker) from narrative text and PPI. MATERIALS AND METHODS: Our study leveraged an existing lung cancer cohort for smoking status, amount, and strength information, which was manually chart-reviewed...
2016: Cancer Informatics
https://www.readbyqxmd.com/read/27812280/a-modular-repository-based-infrastructure-for-simulation-model-storage-and-execution-support-in-the-context-of-in-silico-oncology-and-in-silico-medicine
#15
Nikolaos A Christodoulou, Nikolaos E Tousert, Eleni Ch Georgiadi, Katerina D Argyri, Fay D Misichroni, Georgios S Stamatakos
The plethora of available disease prediction models and the ongoing process of their application into clinical practice - following their clinical validation - have created new needs regarding their efficient handling and exploitation. Consolidation of software implementations, descriptive information, and supportive tools in a single place, offering persistent storage as well as proper management of execution results, is a priority, especially with respect to the needs of large healthcare providers. At the same time, modelers should be able to access these storage facilities under special rights, in order to upgrade and maintain their work...
2016: Cancer Informatics
https://www.readbyqxmd.com/read/27812279/discovering-outliers-of-potential-drug-toxicities-using-a-large-scale-data-driven-approach
#16
Jake Luo, Ron A Cisler
We systematically compared the adverse effects of cancer drugs to detect event outliers across different clinical trials using a data-driven approach. Because many cancer drugs are toxic to patients, better understanding of adverse events of cancer drugs is critical for developing therapies that could minimize the toxic effects. However, due to the large variabilities of adverse events across different cancer drugs, methods to efficiently compare adverse effects across different cancer drugs are lacking. To address this challenge, we present an exploration study that integrates multiple adverse event reports from clinical trials in order to systematically compare adverse events across different cancer drugs...
2016: Cancer Informatics
https://www.readbyqxmd.com/read/27812278/cluster-analysis-of-p53-binding-site-sequences-reveals-subsets-with-different-functions
#17
Ji-Hyun Lim, Natasha S Latysheva, Richard D Iggo, Daniel Barker
p53 is an important regulator of cell cycle arrest, senescence, apoptosis and metabolism, and is frequently mutated in tumors. It functions as a tetramer, where each component dimer binds to a decameric DNA region known as a response element. We identify p53 binding site subtypes and examine the functional and evolutionary properties of these subtypes. We start with over 1700 known binding sites and, with no prior labeling, identify two sets of response elements by unsupervised clustering. When combined, they give rise to three types of p53 binding sites...
2016: Cancer Informatics
https://www.readbyqxmd.com/read/27773988/a-novel-graph-based-algorithm-to-infer-recurrent-copy-number-variations-in-cancer
#18
Chen Chi, Rasif Ajwad, Qin Kuang, Pingzhao Hu
Many cancers have been linked to copy number variations (CNVs) in the genomic DNA. Although there are existing methods to analyze CNVs from individual samples, cancer-causing genes are more frequently discovered in regions where CNVs are common among tumor samples, also known as recurrent CNVs. Integrating multiple samples and locating recurrent CNV regions remain a challenge, both computationally and conceptually. We propose a new graph-based algorithm for identifying recurrent CNVs using the maximal clique detection technique...
2016: Cancer Informatics
https://www.readbyqxmd.com/read/27721651/discovering-microrna-regulatory-modules-in-multi-dimensional-cancer-genomic-data-a-survey-of-computational-methods
#19
REVIEW
Christopher J Walsh, Pingzhao Hu, Jane Batt, Claudia C Dos Santos
MicroRNAs (miRs) are small single-stranded noncoding RNA that function in RNA silencing and post-transcriptional regulation of gene expression. An increasing number of studies have shown that miRs play an important role in tumorigenesis, and understanding the regulatory mechanism of miRs in this gene regulatory network will help elucidate the complex biological processes at play during malignancy. Despite advances, determination of miR-target interactions (MTIs) and identification of functional modules composed of miRs and their specific targets remain a challenge...
2016: Cancer Informatics
https://www.readbyqxmd.com/read/27688708/dna-methylation-heterogeneity-patterns-in-breast-cancer-cell-lines
#20
Sunny Tian, Karina Bertelsmann, Linda Yu, Shuying Sun
Heterogeneous DNA methylation patterns are linked to tumor growth. In order to study DNA methylation heterogeneity patterns for breast cancer cell lines, we comparatively study four metrics: variance, I (2) statistic, entropy, and methylation state. Using the categorical metric methylation state, we select the two most heterogeneous states to identify genes that directly affect tumor suppressor genes and high- or moderate-risk breast cancer genes. Utilizing the Gene Set Enrichment Analysis software and the ConsensusPath Database visualization tool, we generate integrated gene networks to study biological relations of heterogeneous genes...
2016: Cancer Informatics
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