2013 ACM Conference on Bioinformatics, Computational Biology and Biomedical Informatics : ACM - BCB 2013 : Washington, D.C., U.S.A., September 22 - 25, 2013. ACM Conference on Bioinformatics, Computational Biology and Biomedical Informa...最新文献

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An Island-Based Approach for Differential Expression Analysis. 基于岛的差异表达分析方法。

2013 ACM Conference on Bioinformatics, Computational Biology and Biomedical Informatics : ACM - BCB 2013 : Washington, D.C., U.S.A., September 22 - 25, 2013. ACM Conference on Bioinformatics, Computational Biology and Biomedical Informa... Pub Date : 2013-12-31 DOI: 10.1145/2506583.2506589

Abdallah M Eteleeb, Robert M Flight, Benjamin J Harrison, Jeffrey C Petruska, Eric C Rouchka

{"title":"An Island-Based Approach for Differential Expression Analysis.","authors":"Abdallah M Eteleeb, Robert M Flight, Benjamin J Harrison, Jeffrey C Petruska, Eric C Rouchka","doi":"10.1145/2506583.2506589","DOIUrl":"https://doi.org/10.1145/2506583.2506589","url":null,"abstract":"High-throughput mRNA sequencing (also known as RNA-Seq) promises to be the technique of choice for studying transcriptome profiles. This technique provides the ability to develop precise methodologies for transcript and gene expression quantification, novel transcript and exon discovery, and splice variant detection. One of the limitations of current RNA-Seq methods is the dependency on annotated biological features (e.g. exons, transcripts, genes) to detect expression differences across samples. This forces the identification of expression levels and the detection of significant changes to known genomic regions. Any significant changes that occur in unannotated regions will not be captured. To overcome this limitation, we developed a novel segmentation approach, Island-Based (IB), for analyzing differential expression in RNA-Seq and targeted sequencing (exome capture) data without specific knowledge of an isoform. The IB segmentation determines individual islands of expression based on windowed read counts that can be compared across experimental conditions to determine differential island expression. In order to detect differentially expressed genes, the significance of islands (p-values) are combined using Fisher's method. We tested and evaluated the performance of our approach by comparing it to the existing differentially expressed gene (DEG) methods: CuffDiff, DESeq, and edgeR using two benchmark MAQC RNA-Seq datasets. The IB algorithm outperforms all three methods in both datasets as illustrated by an increased auROC.","PeriodicalId":90404,"journal":{"name":"2013 ACM Conference on Bioinformatics, Computational Biology and Biomedical Informatics : ACM - BCB 2013 : Washington, D.C., U.S.A., September 22 - 25, 2013. ACM Conference on Bioinformatics, Computational Biology and Biomedical Informa...","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2013-12-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1145/2506583.2506589","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"33012563","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 3

Classification of Alzheimer Diagnosis from ADNI Plasma Biomarker Data. 基于ADNI血浆生物标志物数据的阿尔茨海默病诊断分类。

Jue Mo, Stuart Maudsley, Bronwen Martin, Sana Siddiqui, Huey Cheung, Calvin A Johnson

{"title":"Classification of Alzheimer Diagnosis from ADNI Plasma Biomarker Data.","authors":"Jue Mo, Stuart Maudsley, Bronwen Martin, Sana Siddiqui, Huey Cheung, Calvin A Johnson","doi":"10.1145/2506583.2506637","DOIUrl":"10.1145/2506583.2506637","url":null,"abstract":"Research into modeling the progression of Alzheimer's disease (AD) has made recent progress in identifying plasma proteomic biomarkers to identify the disease at the pre-clinical stage. In contrast with cerebral spinal fluid (CSF) biomarkers and PET imaging, plasma biomarker diagnoses have the advantage of being cost-effective and minimally invasive, thereby improving our understanding of AD and hopefully leading to early interventions as research into this subject advances. The Alzheimer's Disease Neuroimaging Initiative* (ADNI) has collected data on 190 plasma analytes from individuals diagnosed with AD as well subjects with mild cognitive impairment and cognitively normal (CN) controls. We propose an approach to classify subjects as AD or CN via an ensemble of classifiers trained and validated on ADNI data. Classifier performance is enhanced by an augmentation of a selective biomarker feature space with principal components obtained from the entire set of biomarkers. This procedure yields accuracy of 89% and area under the ROC curve of 94%.","PeriodicalId":90404,"journal":{"name":"2013 ACM Conference on Bioinformatics, Computational Biology and Biomedical Informatics : ACM - BCB 2013 : Washington, D.C., U.S.A., September 22 - 25, 2013. ACM Conference on Bioinformatics, Computational Biology and Biomedical Informa...","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2013-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4295502/pdf/nihms627205.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"32984673","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Three-Dimensional Spot Detection in Ratiometric Fluorescence Imaging For Measurement of Subcellular Organelles. 用于亚细胞器测量的比例荧光成像三维斑点检测。

William W Lau, Calvin A Johnson, Sara Lioi, Joseph A Mindell

{"title":"Three-Dimensional Spot Detection in Ratiometric Fluorescence Imaging For Measurement of Subcellular Organelles.","authors":"William W Lau, Calvin A Johnson, Sara Lioi, Joseph A Mindell","doi":"10.1145/2506583.2512387","DOIUrl":"https://doi.org/10.1145/2506583.2512387","url":null,"abstract":"Lysosomes are subcellular organelles playing a vital role in the endocytosis process of the cell. Lysosomal acidity is an important factor in assuring proper functioning of the enzymes within the organelle, and can be assessed by labeling the lysosomes with pH-sensitive fluorescence probes. To enhance our understanding of the acidification mechanisms, the goal of this work is to develop a method that can accurately detect and characterize the acidity of each lysosome captured in ratiometric fluorescence images. We present an algorithm that utilizes the h-dome transformation and reconciles spots detected independently from two wavelength channels. We evaluated our algorithm using simulated images for which the exact locations were known. The h-dome algorithm achieved an f-score as high as 0.890. We also computed the fluorescence ratios from lysosomes in live HeLa cell images with known lysosomal pHs. Using leave-one-out cross-validation, we demonstrated that the new algorithm was able to achieve much better pH prediction accuracy than the conventional method.","PeriodicalId":90404,"journal":{"name":"2013 ACM Conference on Bioinformatics, Computational Biology and Biomedical Informatics : ACM - BCB 2013 : Washington, D.C., U.S.A., September 22 - 25, 2013. ACM Conference on Bioinformatics, Computational Biology and Biomedical Informa...","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2013-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1145/2506583.2512387","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"33003520","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 1