Comparison of Three Information Sources for Smoking Information in Electronic Health Records
Conclusion: These findings suggest that narrative text could serve as a more reliable and comprehensive source for obtaining smoking-related information than structured data sources. PPI, the readily available structured data, could be used as a complementary source for more comprehensive patient coverage. (Source: Cancer Informatics)
Source: Cancer Informatics - December 7, 2016 Category: Cancer & Oncology Authors: Liwei Wang Xiaoyang Ruan Ping Yang Hongfang Liu Source Type: research
A Modular Repository-based Infrastructure for Simulation Model Storage and Execution Support in the Context of In Silico Oncology and In Silico Medicine
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 a...
Source: Cancer Informatics - October 26, 2016 Category: Cancer & Oncology Authors: Nikolaos A. Christodoulou Nikolaos E. Tousert Eleni Ch. Georgiadi Katerina D. Argyri Fay D. Misichroni Georgios S. Stamatakos Source Type: research
Discovering Outliers of Potential Drug Toxicities Using a Large-scale Data-driven Approach
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 ...
Source: Cancer Informatics - October 25, 2016 Category: Cancer & Oncology Authors: Jake Luo Ron A. Cisler Source Type: research
Cluster Analysis of p53 Binding Site Sequences Reveals Subsets with Different Functions
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. We find that probabilistic and alignment-based assessments of cross-...
Source: Cancer Informatics - October 24, 2016 Category: Cancer & Oncology Authors: Ji-Hyun Lim Natasha S. Latysheva Richard D. Iggo and Daniel Barker Source Type: research
A Novel Graph-based Algorithm to Infer Recurrent Copy Number Variations in Cancer
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. The algorithm has an optimal solution, which means all maximal cliques can be identified, and g...
Source: Cancer Informatics - October 8, 2016 Category: Cancer & Oncology Authors: Chen Chi Rasif Ajwad Qin Kuang Pingzhao Hu Source Type: research
Discovering MicroRNA-Regulatory Modules in Multi-Dimensional Cancer Genomic Data: A Survey of Computational Methods
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. A large amount of data generated by high-throughput meth...
Source: Cancer Informatics - October 2, 2016 Category: Cancer & Oncology Authors: Christopher J. Walsh Pingzhao Hu Jane Batt and Claudia C. dos Santos Source Type: research
Novel Biomarker Candidates for Colorectal Cancer Metastasis: A Meta-analysis of In Vitro Studies
In conclusion, we demonstrated a meta-analysis approach and successfully suggested ten biomarker candidates for future investigation. (Source: Cancer Informatics)
Source: Cancer Informatics - September 21, 2016 Category: Cancer & Oncology Authors: Nguyen Phuoc Long Wun Jun Lee Nguyen Truong Huy Seul Ji Lee Jeong Hill Park Sung Won Kwon Source Type: research
Identifying Significant Features in Cancer Methylation Data Using Gene Pathway Segmentation
In order to provide the most effective therapy for cancer, it is important to be able to diagnose whether a patient's cancer will respond to a proposed treatment. Methylation profiling could contain information from which such predictions could be made. Currently, hypothesis testing is used to determine whether possible biomarkers for cancer progression produce statistically significant results. However, this approach requires the identification of individual genes, or sets of genes, as candidate hypotheses, and with the increasing size of modern microarrays, this task is becoming progressively harder. Exhaustive testing o...
Source: Cancer Informatics - September 19, 2016 Category: Cancer & Oncology Authors: Zena M. Hira Duncan F. Gillies Source Type: research
Overlapping Group Logistic Regression with Applications to Genetic Pathway Selection
Discovering important genes that account for the phenotype of interest has long been a challenge in genome-wide expression analysis. Analyses such as gene set enrichment analysis (GSEA) that incorporate pathway information have become widespread in hypothesis testing, but pathway-based approaches have been largely absent from regression methods due to the challenges of dealing with overlapping pathways and the resulting lack of available software. The R package grpreg is widely used to fit group lasso and other group-penalized regression models; in this study, we develop an extension, grpregOverlap, to allow for overlappin...
Source: Cancer Informatics - September 14, 2016 Category: Cancer & Oncology Authors: Yaohui Zeng Patrick Breheny Source Type: research
DNA Methylation Heterogeneity Patterns in Breast Cancer Cell Lines
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, I2 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 heteroge...
Source: Cancer Informatics - September 6, 2016 Category: Cancer & Oncology Authors: Sunny Tian Karina Bertelsmann Linda Yu Shuying Sun Source Type: research
Biological Networks for Cancer Candidate Biomarkers Discovery
Due to its extraordinary heterogeneity and complexity, cancer is often proposed as a model case of a systems biology disease or network disease. There is a critical need of effective biomarkers for cancer diagnosis and/or outcome prediction from system level analyses. Methods based on integrating omics data into networks have the potential to revolutionize the identification of cancer biomarkers. Deciphering the biological networks underlying cancer is undoubtedly important for understanding the molecular mechanisms of the disease and identifying effective biomarkers. In this review, the networks constructed for cancer bio...
Source: Cancer Informatics - September 3, 2016 Category: Cancer & Oncology Authors: Wenying Yan Wenjin Xue Jiajia Chen Guang Hu Source Type: research
Characterization of Gene Expression Patterns among Artificially Developed Cancer Stem Cells Using Spherical Self-Organizing Map
We performed gene expression microarray analysis coupled with spherical self-organizing map (sSOM) for artificially developed cancer stem cells (CSCs). The CSCs were developed from human induced pluripotent stem cells (hiPSCs) with the conditioned media of cancer cell lines, whereas the CSCs were induced from primary cell culture of human cancer tissues with defined factors (OCT3/4, SOX2, and KLF4). These cells commonly expressed human embryonic stem cell (hESC)/hiPSC-specific genes (POU5F1, SOX2, NANOG, LIN28, and SALL4) at a level equivalent to those of control hiPSC 201B7. The sSOM with unsupervised method demonstrated ...
Source: Cancer Informatics - August 15, 2016 Category: Cancer & Oncology Authors: Akimasa Seno Tomonari Kasai Masashi Ikeda Arun Vaidyanath Junko Masuda Akifumi Mizutani Hiroshi Murakami Tetsuya Ishikawa Masaharu Seno Source Type: research
ExSurv: A Web Resource for Prognostic Analyses of Exons Across Human Cancers Using Clinical Transcriptomes
Survival analysis in biomedical sciences is generally performed by correlating the levels of cellular components with patients' clinical features as a common practice in prognostic biomarker discovery. While the common and primary focus of such analysis in cancer genomics so far has been to identify the potential prognostic genes, alternative splicing – a posttranscriptional regulatory mechanism that affects the functional form of a protein due to inclusion or exclusion of individual exons giving rise to alternative protein products, has increasingly gained attention due to the prevalence of splicing aberrations in cance...
Source: Cancer Informatics - August 6, 2016 Category: Cancer & Oncology Authors: Seyedsasan Hashemikhabir Gungor Budak Sarath Chandra Janga Source Type: research
Pathway-Informed Classification System (PICS) for Cancer Analysis Using Gene Expression Data
We introduce Pathway-Informed Classification System (PICS) for classifying cancers based on tumor sample gene expression levels. PICS is a computational method capable of expeditiously elucidating both known and novel biological pathway involvement specific to various cancers and uses that learned pathway information to separate patients into distinct classes. The method clearly separates a pan-cancer dataset by tissue of origin and also sub-classifies individual cancer datasets into distinct survival classes. Gene expression values are collapsed into pathway scores that reveal which biological activities are most useful f...
Source: Cancer Informatics - July 26, 2016 Category: Cancer & Oncology Authors: Michael R Young and David L Craft Source Type: research
Recursive Partitioning Method on Competing Risk Outcomes
In some cancer clinical studies, researchers have interests to explore the risk factors associated with competing risk outcomes such as recurrence-free survival. We develop a novel recursive partitioning framework on competing risk data for both prognostic and predictive model constructions. We define specific splitting rules, pruning algorithm, and final tree selection algorithm for the competing risk tree models. This methodology is quite flexible that it can corporate both semiparametric method using Cox proportional hazards model and parametric competing risk model. Both prognostic and predictive tree models are develo...
Source: Cancer Informatics - July 25, 2016 Category: Cancer & Oncology Authors: Wei Xu Jiahua Che and Qin Kong Source Type: research
