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1. BioVLAB-mCpG-SNP-EXPRESS: A system for multi-level and multi-perspective analysis and exploration of DNA methylation, sequence variation (SNPs), and gene expression from multi-omics data

Author:
Chae, Heejoon; Lee, Sangseon; Seo, Seokjun; Jung, Daekyoung; Chang, Hyeonsook; Nephew, Kenneth P.; Kim, Sun
Source:
Methods 2016 v.111 pp. 64-71
ISSN:
1046-2023
Subject:
DNA; DNA methylation; Internet; bioinformatics; breast neoplasms; cell lines; data collection; gene expression; genes; high-throughput nucleotide sequencing; neoplasm cells; nucleotide sequences; phenotype; sequence diversity
Abstract:
... Measuring gene expression, DNA sequence variation, and DNA methylation status is routinely done using high throughput sequencing technologies. To analyze such multi-omics data and explore relationships, reliable bioinformatics systems are much needed. Existing systems are either for exploring curated data or for processing omics data in the form of a library such as R. Thus scientists have much di ...
DOI:
10.1016/j.ymeth.2016.07.019

2. DeF-GPU: Efficient and effective deletions finding in hepatitis B viral genomic DNA using a GPU architecture

Author:
Cheng, Chun-Pei; Lan, Kuo-Lun; Liu, Wen-Chun; Chang, Ting-Tsung; Tseng, Vincent S.
Source:
Methods 2016 v.111 pp. 56-63
ISSN:
1046-2023
Subject:
DNA; data collection; hepatitis B; hepatoma; high-throughput nucleotide sequencing; immune system; risk; viral proteins; viruses
Abstract:
... Hepatitis B viral (HBV) infection is strongly associated with an increased risk of liver diseases like cirrhosis or hepatocellular carcinoma (HCC). Many lines of evidence suggest that deletions occurring in HBV genomic DNA are highly associated with the activity of HBV via the interplay between aberrant viral proteins release and human immune system. Deletions finding on the HBV whole genome seque ...
DOI:
10.1016/j.ymeth.2016.07.020

3. Disease types discovery from a large database of inpatient records: A sepsis study

Author:
Gligorijevic, Djordje; Stojanovic, Jelena; Obradovic, Zoran
Source:
Methods 2016 v.111 pp. 45-55
ISSN:
1046-2023
Subject:
databases; early diagnosis; inflammation; models; mortality; phenotype; precision medicine; prediction; sepsis (infection); United States
Abstract:
... Data-driven phenotype discoveries on Electronic Health Records (EHR) data have recently drawn benefits across many aspects of clinical practice. In the method described in this paper, we map a very large EHR database containing more than a million inpatient cases into a low dimensional space where diseases with similar phenotypes have similar representation. This embedding allows for an effective ...
DOI:
10.1016/j.ymeth.2016.07.021

4. Feature selection methods for big data bioinformatics: A survey from the search perspective

Author:
Wang, Lipo; Wang, Yaoli; Chang, Qing
Source:
Methods 2016 v.111 pp. 21-31
ISSN:
1046-2023
Subject:
bioinformatics; data collection; surveys; systematic review
Abstract:
... This paper surveys main principles of feature selection and their recent applications in big data bioinformatics. Instead of the commonly used categorization into filter, wrapper, and embedded approaches to feature selection, we formulate feature selection as a combinatorial optimization or search problem and categorize feature selection methods into exhaustive search, heuristic search, and hybrid ...
DOI:
10.1016/j.ymeth.2016.08.014

5. Hessian regularization based symmetric nonnegative matrix factorization for clustering gene expression and microbiome data

Author:
Ma, Yuanyuan; Hu, Xiaohua; He, Tingting; Jiang, Xingpeng
Source:
Methods 2016 v.111 pp. 80-84
ISSN:
1046-2023
Subject:
algorithms; data collection; gene expression; microbiome
Abstract:
... Nonnegative matrix factorization (NMF) has received considerable attention due to its interpretation of observed samples as combinations of different components, and has been successfully used as a clustering method. As an extension of NMF, Symmetric NMF (SNMF) inherits the advantages of NMF. Unlike NMF, however, SNMF takes a nonnegative similarity matrix as an input, and two lower rank nonnegativ ...
DOI:
10.1016/j.ymeth.2016.06.017

6. Immune signaling-based Cascade Propagation approach re-stratifies HNSCC patients

Author:
Liu, Keqin; Chyr, Jacqueline; Zhao, Weiling; Zhou, Xiaobo
Source:
Methods 2016 v.111 pp. 72-79
ISSN:
1046-2023
Subject:
T-lymphocytes; bioinformatics; computational methodology; disease course; genes; genomics; head and neck neoplasms; immune response; medical records; patients; phosphatidylinositol 3-kinase; prognosis; protein-protein interactions; signal transduction; somatic mutation; squamous cell carcinoma
Abstract:
... The availability of high-throughput genomic assays and rich electronic medical records allows us to identify cancer subtypes with greater accuracy and resolution. The integration of multiplatform, heterogenous, and high dimensional data remains an enormous challenge in using big data in bioinformatics research. Previous methods have been developed for patient stratification, however, these approac ...
DOI:
10.1016/j.ymeth.2016.06.018

7. INDEED: Integrated differential expression and differential network analysis of omic data for biomarker discovery

Author:
Zuo, Yiming; Cui, Yi; Di Poto, Cristina; Varghese, Rency S.; Yu, Guoqiang; Li, Ruijiang; Ressom, Habtom W.
Source:
Methods 2016 v.111 pp. 12-20
ISSN:
1046-2023
Subject:
biomarkers; breast neoplasms; cell adhesion molecules; data collection; gene expression; gene expression regulation; glycomics; hepatoma; liquid chromatography; mass spectrometry; patients; prediction; proteomics; regression analysis; transcriptomics
Abstract:
... Differential expression (DE) analysis is commonly used to identify biomarker candidates that have significant changes in their expression levels between distinct biological groups. One drawback of DE analysis is that it only considers the changes on single biomolecule level. Recently, differential network (DN) analysis has become popular due to its capability to measure the changes on biomolecular ...
DOI:
10.1016/j.ymeth.2016.08.015

8. Modeling and interoperability of heterogeneous genomic big data for integrative processing and querying

Author:
Masseroli, Marco; Kaitoua, Abdulrahman; Pinoli, Pietro; Ceri, Stefano
Source:
Methods 2016 v.111 pp. 3-11
ISSN:
1046-2023
Subject:
DNA; copy number variation; data collection; gene expression; genes; genomic islands; genomics; high-throughput nucleotide sequencing; models; sequence alignment; somatic mutation; transcription (genetics)
Abstract:
... While a huge amount of (epi)genomic data of multiple types is becoming available by using Next Generation Sequencing (NGS) technologies, the most important emerging problem is the so-called tertiary analysis, concerned with sense making, e.g., discovering how different (epi)genomic regions and their products interact and cooperate with each other. We propose a paradigm shift in tertiary analysis, ...
DOI:
10.1016/j.ymeth.2016.09.002

9. Perspectives on making big data analytics work for oncology

Author:
El Naqa, Issam
Source:
Methods 2016 v.111 pp. 32-44
ISSN:
1046-2023
Subject:
artificial intelligence; biomarkers; case studies; data analysis; data collection; databases; decision making; demographic statistics; disease incidence; image analysis; neoplasms; patients
Abstract:
... Oncology, with its unique combination of clinical, physical, technological, and biological data provides an ideal case study for applying big data analytics to improve cancer treatment safety and outcomes. An oncology treatment course such as chemoradiotherapy can generate a large pool of information carrying the 5Vs hallmarks of big data. This data is comprised of a heterogeneous mixture of patie ...
DOI:
10.1016/j.ymeth.2016.08.010