| LDR | | 01719nmm uu200385 4500 |
| 001 | | 000000330869 |
| 005 | | 20240805162957 |
| 008 | | 181129s2018 |||||||||||||||||c||eng d |
| 020 | |
▼a 9780438126626 |
| 035 | |
▼a (MiAaPQ)AAI10903056 |
| 035 | |
▼a (MiAaPQ)umichrackham:001260 |
| 040 | |
▼a MiAaPQ
▼c MiAaPQ
▼d 248032 |
| 082 | 0 |
▼a 310 |
| 100 | 1 |
▼a Guidici, Teal. |
| 245 | 10 |
▼a Methods for Utilizing Co-expression Networks for Biological Insight. |
| 260 | |
▼a [S.l.] :
▼b University of Michigan.,
▼c 2018 |
| 260 | 1 |
▼a Ann Arbor :
▼b ProQuest Dissertations & Theses,
▼c 2018 |
| 300 | |
▼a 156 p. |
| 500 | |
▼a Source: Dissertation Abstracts International, Volume: 79-12(E), Section: B. |
| 500 | |
▼a Advisers: Charles Burant |
| 502 | 1 |
▼a Thesis (Ph.D.)--University of Michigan, 2018. |
| 520 | |
▼a The explosion of high-throughput Omics assays in past 15 years has led to a revolution in the quantity of data and the number of data types which are available to biological researchers. This has necessitated a second revolution in the developme |
| 520 | |
▼a The primary goal of this dissertation is to develop techniques for identifying and characterizing patterns of co-expression. In our first project, we develop a Differentially Weighted Factor Model for estimating covariance matrices related throu |
| 590 | |
▼a School code: 0127. |
| 650 | 4 |
▼a Statistics. |
| 690 | |
▼a 0463 |
| 710 | 20 |
▼a University of Michigan.
▼b Statistics. |
| 773 | 0 |
▼t Dissertation Abstracts International
▼g 79-12B(E). |
| 773 | |
▼t Dissertation Abstract International |
| 790 | |
▼a 0127 |
| 791 | |
▼a Ph.D. |
| 792 | |
▼a 2018 |
| 793 | |
▼a English |
| 856 | 40 |
▼u http://www.riss.kr/pdu/ddodLink.do?id=T15000554
▼n KERIS |
| 980 | |
▼a 201812
▼f 2019 |
| 990 | |
▼a 관리자 |