BMC Bioinformatics (original) (raw)

Volume 17 Supplement 1

Selected articles from the Fourteenth Asia Pacific Bioinformatics Conference (APBC 2016): Bioinformatics

Proceedings

Publication of this supplement has not been supported by sponsorship. Information about the source of funding for publication charges can be found in the individual articles. Articles have undergone the journal's standard peer review process. The Supplement Editors declare that they have no competing interests.

San Francisco, CA, USA11 - 13 January 2016

Edited by Jijun Tang, Lu Tian and Yi-Ping Phoebe Chen.

Related articles have been published in supplements to BMC Genomics and BMC Systems Biology.

  1. Weakly supervised learning of biomedical information extraction from curated data

Numerous publicly available biomedical databases derive data by curating from literatures. The curated data can be useful as training examples for information extraction, but curated data usually lack the exac...
Authors:
Citation: BMC Bioinformatics 201617(Suppl 1):S1
Content type: PROCEEDINGS Published on: 11 January 2016

  1. Drug repositioning for non-small cell lung cancer by using machine learning algorithms and topological graph theory

Non-small cell lung cancer (NSCLC) is one of the leading causes of death globally, and research into NSCLC has been accumulating steadily over several years. Drug repositioning is the current trend in the phar...
Authors: Chien-Hung Huang, Peter Mu-Hsin Chang, Chia-Wei Hsu, Chi-Ying F. Huang and Ka-Lok Ng
Citation: BMC Bioinformatics 201617(Suppl 1):S2
Content type: Proceedings Published on: 11 January 2016

  1. Gene expression profiling identifies candidate biomarkers for active and latent tuberculosis

Tuberculosis (TB) is a serious infectious disease in that 90 % of those latently infected with Mycobacterium tuberculosis present no symptoms, but possess a 10 % lifetime chance of developing active TB. To pre...
Authors: Shih-Wei Lee, Lawrence Shih-Hsin Wu, Guan-Mau Huang, Kai-Yao Huang, Tzong-Yi Lee and Julia Tzu-Ya Weng
Citation: BMC Bioinformatics 201617(Suppl 1):S3
Content type: Proceedings Published on: 11 January 2016

  1. Predicting transcription factor site occupancy using DNA sequence intrinsic and cell-type specific chromatin features

Understanding the mechanisms by which transcription factors (TF) are recruited to their physiological target sites is crucial for understanding gene regulation. DNA sequence intrinsic features such as predicte...
Authors: Sunil Kumar and Philipp Bucher
Citation: BMC Bioinformatics 201617(Suppl 1):S4
Content type: Proceedings Published on: 11 January 2016

  1. A semi-parametric statistical model for integrating gene expression profiles across different platforms

Determining differentially expressed genes (DEGs) between biological samples is the key to understand how genotype gives rise to phenotype. RNA-seq and microarray are two main technologies for profiling gene e...
Authors: Yafei Lyu and Qunhua Li
Citation: BMC Bioinformatics 201617(Suppl 1):S5
Content type: Proceedings Published on: 11 January 2016

  1. The diagnostic application of RNA sequencing in patients with thyroid cancer: an analysis of 851 variants and 133 fusions in 524 genes

Thyroid carcinomas are known to harbor oncogenic driver mutations and advances in sequencing technology now allow the detection of these in fine needle aspiration biopsies (FNA). Recent work by The Cancer Geno...
Authors: Moraima Pagan, Richard T. Kloos, Chu-Fang Lin, Kevin J. Travers, Hajime Matsuzaki, Ed Y. Tom, Su Yeon Kim, Mei G. Wong, Andrew C. Stewart, Jing Huang, P. Sean Walsh, Robert J. Monroe and Giulia C. Kennedy
Citation: BMC Bioinformatics 201617(Suppl 1):S6
Content type: Proceedings Published on: 11 January 2016

  1. A full Bayesian partition model for identifying hypo- and hyper-methylated loci from single nucleotide resolution sequencing data

DNA methylation is an epigenetic modification that plays important roles on gene regulation. Study of whole-genome bisulfite sequencing and reduced representation bisulfite sequencing brings the availability o...
Authors: Henan Wang, Chong He, Garima Kushwaha, Dong Xu and Jing Qiu
Citation: BMC Bioinformatics 201617(Suppl 1):S7
Content type: PROCEEDINGS Published on: 11 January 2016

  1. PredRSA: a gradient boosted regression trees approach for predicting protein solvent accessibility

Protein solvent accessibility prediction is a pivotal intermediate step towards modeling protein tertiary structures directly from one-dimensional sequences. It also plays an important part in identifying prot...
Authors: Chao Fan, Diwei Liu, Rui Huang, Zhigang Chen and Lei Deng
Citation: BMC Bioinformatics 201617(Suppl 1):S8
Content type: PROCEEDINGS Published on: 11 January 2016

  1. Learning a hierarchical representation of the yeast transcriptomic machinery using an autoencoder model

A living cell has a complex, hierarchically organized signaling system that encodes and assimilates diverse environmental and intracellular signals, and it further transmits signals that control cellular respo...
Authors: Lujia Chen, Chunhui Cai, Vicky Chen and Xinghua Lu
Citation: BMC Bioinformatics 201617(Suppl 1):S9
Content type: Proceedings Published on: 11 January 2016

  1. Missing value imputation for microRNA expression data by using a GO-based similarity measure

Missing values are commonly present in microarray data profiles. Instead of discarding genes or samples with incomplete expression level, missing values need to be properly imputed for accurate data analysis. ...
Authors: Yang Yang, Zhuangdi Xu and Dandan Song
Citation: BMC Bioinformatics 201617(Suppl 1):S10
Content type: PROCEEDINGS Published on: 11 January 2016

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