Biocomputing 2013 - Proceedings Of The Pacific Symposium. (Kayıt no. 15556)
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| Sabit Uzunluktaki Kontrol Alanı | 07270nam a22004453i 4500 |
| 001 - KONTROL NUMARASI | |
| Control Dosyası | EBC6383185 |
| 003 - KONTROL NUMARASI KİMLİĞİ | |
| Kontrol Alanı | MiAaPQ |
| 005 - EN SON İŞLEM TARİHİ ve ZAMANI | |
| Kontrol Alanı | 20220623112327.0 |
| 006 - FIXED-LENGTH DATA ELEMENTS--ADDITIONAL MATERIAL CHARACTERISTICS | |
| fixed length control field | m o d | |
| 007 - PHYSICAL DESCRIPTION FIXED FIELD--GENERAL INFORMATION | |
| fixed length control field | cr cnu|||||||| |
| 008 - SABİT UZUNLUKTAKİ VERİ ÖGELERİ - GENEL BİLGİ | |
| Sabit Alan | 220617s2012 xx o ||||0 eng d |
| 020 ## - ISBN - ULUSLARARASI STANDART KİTAP NUMARASI | |
| Isbn | 9789814447973 |
| -- | (electronic bk.) |
| 020 ## - ISBN - ULUSLARARASI STANDART KİTAP NUMARASI | |
| Cancelled/invalid ISBN | 9789814596367 |
| 035 ## - SİSTEM KONTROL NUMARASI | |
| Sistem Kontrol Numarası | (MiAaPQ)EBC6383185 |
| 035 ## - SİSTEM KONTROL NUMARASI | |
| Sistem Kontrol Numarası | (Au-PeEL)EBL6383185 |
| 035 ## - SİSTEM KONTROL NUMARASI | |
| Sistem Kontrol Numarası | (OCoLC)1165177092 |
| 040 ## - KATALOGLAMA KAYNAĞI | |
| Özgün Kataloglama Kurumu | MiAaPQ |
| Kataloglama Dili | eng |
| Açıklama Kuralları | rda |
| -- | pn |
| Çeviri Kurumu | MiAaPQ |
| Değiştiren Kurum | MiAaPQ |
| 100 1# - KİŞİ ADI | |
| Yazar Adı (Kişi adı) | Altman, Russ B. |
| 245 10 - ESER ADI BİLDİRİMİ | |
| Başlık | Biocomputing 2013 - Proceedings Of The Pacific Symposium. |
| 264 #1 - PRODUCTION, PUBLICATION, DISTRIBUTION, MANUFACTURE, AND COPYRIGHT NOTICE | |
| Place of production, publication, distribution, manufacture | Singapore : |
| Name of producer, publisher, distributor, manufacturer | World Scientific Publishing Company, |
| Date of production, publication, distribution, manufacture, or copyright notice | 2012. |
| 264 #4 - PRODUCTION, PUBLICATION, DISTRIBUTION, MANUFACTURE, AND COPYRIGHT NOTICE | |
| Date of production, publication, distribution, manufacture, or copyright notice | �2012. |
| 300 ## - FİZİKSEL TANIMLAMA | |
| Sayfa, Cilt vb. | 1 online resource (471 pages) |
| 336 ## - CONTENT TYPE | |
| Content type term | text |
| Content type code | txt |
| Source | rdacontent |
| 337 ## - MEDIA TYPE | |
| Media type term | computer |
| Media type code | c |
| Source | rdamedia |
| 338 ## - CARRIER TYPE | |
| Carrier type term | online resource |
| Carrier type code | cr |
| Source | rdacarrier |
| 505 0# - İÇİNDEKİLER NOTU | |
| İçindekiler Notu | Intro -- Modeling cell heterogeneity: from single-cell variations to mixed cells populations 445 -- Computational Challenges of Mass Phenotyping 454 -- The Future of Genome-Based Medicine 456 -- 0session-intro-cdr.pdf -- 1cheng -- 1.   Introduction -- 2.   Methods -- 2.1.   Data sources and data processing -- 2.2.   Pair-wise similarity scores -- 2.3.   Method nomenclature -- 2.4.   AUCs and p-values -- 2.5.   Expression signal strength -- 3.   Results -- 4.   Discussion -- 5.   Acknowledgments -- 2felciano -- 3phatak -- 4shi -- 5wang -- 0intro-epigenomics.pdf -- 1ahn -- 2luo -- 3sahu -- 1gabr -- 2gevaert -- 3kim -- 1.   Introduction -- 2.   Methods -- 2.1.   Introduction of the Module Cover Problem -- 2.2.   Integrated Module Cover -- 2.3.   Two-Step Module Cover -- 3.   Results -- 3.1.   Analysis of Glioblastoma Multiforme Data from GMDI -- 3.1.1.   Comparison of the Module Cover approaches. -- We applied the integrated greedy module cover algorithm with k = 300 and = 1, allowing 5 samples (3%) to be covered less than k times to exclude outliers. We discuss the more detailed parameter selection in online Appendix Section 2. In particular, we found that the number of non-trivial modules (i.e. ≥ 3 genes) starts to level with k = 300, prompting us to choose this parameter value for our main analysis. We obtained 249 modules that contained a total of 513 genes including 41 non-singleton. |
| 505 8# - İÇİNDEKİLER NOTU | |
| İçindekiler Notu | We also computed the entropy of association profiles for each module. Since entropy measures the uncertainty of data, a good quality module (with only a few strong associations) is expected to have low entropy while entropy increases as data is more uniformly distributed. Formally, for each module M, we partitioned the range from 0 to strength (M) into 10 bins of equal sizes and assigned loci according to their significance. In each bin, we computed the percentage of loci and defined the entr -- For an association to be specific in a given module, only a few regulatory associations should have highly significant p-values while the remaining loci are expected to have insignificant p-values. Thus, we defined the specificity of a module M as the area of a cumulative histogram of association significance values. Specifically, we partitioned the range from 0 to strength (M) into 10 bins of equal sizes and defined cj to be the cumulative percentage of j-th bin. Then the specificity is defined -- 3.1.2.   Analysis of GBM data -- 3.1.3.   Analysis of Ovarian Cancer Data -- 4.   Discussion -- Uncovering modules that are associated with genomic alterations in a disease is a challenging task as well as an important step to understand complex diseases. To address this challenge we introduced a novel technique - module cover - that extends the concept of set cover to network modules. We provided a mathematical formalization of the problem and developed two heuristic solutions: the Integrated Module Cover approach, which greedily selects genes to cover disease cases while simultaneously d. |
| 505 8# - İÇİNDEKİLER NOTU | |
| İçindekiler Notu | In general, the module cover approach is especially helpful in analyzing and classifying heterogeneous disease cases by exploring the way different combinations of dys-regulated of modules relate to a particular disease subcategory. Indeed, our analysis indicated that the gene set selected by module cover approach may be used for classification. Equally important, the selected module covers may help to interpret classifications that were obtained with other methods. -- 5.   Materials -- 5.1 Data Treatment for Glioblastoma Multiforme Data from GMDI -- Differentially Expressed Genes: Briefly, all samples were profiled using HG-U133 Plus 2.0 arrays that were normalized at the probe level with dChip (16, 19). Among probes representing each gene, we chose the probeset with the highest mean intensity in the tumor and control samples. We determined genes that are differentially expressed in each disease case compared to the non-tumor control cases with a Z-test. For a gene g and case c, we define cover(c, g) to be 1 if nominal p-value < -- 0.01 and 0 -- eQTL Profiles: To detect copy number alterations, samples were hybridized on the Genechip Human Mapping 100K arrays, and copy numbers were calculated using Affymetrix Copy Number Analysis Tool (CNAT 4). After probe-level normalization and summarization, calculated log2-tranformed ratios were used to estimate raw copy numbers. Using a Gaussian approach, raw SNP profiles were smoothed (> -- 500 kb window by default) and segmented with a Hidden Markov Model approach (20-22). We first performed local c -- 5.2 Data Treatment for Ovarian Cancer Data from TCGA -- 4pendergrass -- 5perez-rathke -- 0intro-pm-rev.pdf -- 1biswas -- 2crawford -- 3flores -- 4huang -- 5li. |
| 505 8# - İÇİNDEKİLER NOTU | |
| İçindekiler Notu | Alzheimer's disease (AD) is one of the leading causes of death for older people in US with rapidly increasing incidence. AD irreversibly and progressively damages the brain, but there are treatments in clinical trials to potentially slow the developme... -- 1. Introduction -- 2.1 Utilizing VARiant Informing MEDicine (VARIMED) -- 3 Result -- 5 Acknowledgments -- 6province -- 0intro-ppg.pdf -- 1bayzid -- 2degnan -- 3kopelman -- 4lin -- 5roch -- 1brown -- 2ding -- 3moore -- 4schrider -- 5singh -- 0intro-text.pdf -- 1bush -- 2holzinger -- 3hu -- 4kolchinsky -- 5seedorff -- 6verspoor -- 1modeling.pdf -- 3ccmp -- 4pm. |
| 590 ## - LOCAL NOTE (RLIN) | |
| Local note | Electronic reproduction. Ann Arbor, Michigan : ProQuest Ebook Central, 2022. Available via World Wide Web. Access may be limited to ProQuest Ebook Central affiliated libraries. |
| 655 #4 - INDEX TERM--GENRE/FORM | |
| Genre/form data or focus term | Electronic books. |
| 700 1# - EK GİRİŞ - KİŞİ ADI | |
| Yazar Adı (Kişi adı) | Dunker, A Keith. |
| 700 1# - EK GİRİŞ - KİŞİ ADI | |
| Yazar Adı (Kişi adı) | Hunter, Lawrence. |
| 700 1# - EK GİRİŞ - KİŞİ ADI | |
| Yazar Adı (Kişi adı) | Murray, Tiffany A. |
| 700 1# - EK GİRİŞ - KİŞİ ADI | |
| Yazar Adı (Kişi adı) | Klein, Teri E. |
| 776 08 - ADDITIONAL PHYSICAL FORM ENTRY | |
| Display text | Print version: |
| Main entry heading | Altman, Russ B |
| Title | Biocomputing 2013 - Proceedings Of The Pacific Symposium |
| Place, publisher, and date of publication | Singapore : World Scientific Publishing Company,c2012 |
| International Standard Book Number | 9789814596367 |
| 797 2# - LOCAL ADDED ENTRY--CORPORATE NAME (RLIN) | |
| Corporate name or jurisdiction name as entry element | ProQuest (Firm) |
| 856 40 - ELEKTRONİK YER ve ERİŞİM | |
| Kaynak Tanımlayıcı (URL) | <a href="https://ebookcentral.proquest.com/lib/ostimteknik/detail.action?docID=6383185">https://ebookcentral.proquest.com/lib/ostimteknik/detail.action?docID=6383185</a> |
| Public note | Click to View |
| 588 ## - SOURCE OF DESCRIPTION NOTE | |
| Ekli Tam Metin | Description based on publisher supplied metadata and other sources. |
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