A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2004; you can also visit the original URL.
The file type is
A web-based server and additional supporting information are available at http://engpub1.bu.edu/ϳjosephs. Proteins 2000;38:428 -440. ... If they align well, we would expect d 12 A to be similar to d 12 B , d 13 A to be similar to d 13 B , and d 23 A to be similar to d B 23 . ... th residue and the j B th residue in protein B; d* ij is the average of d ij A and d ij B TABLE III . ...doi:10.1002/(sici)1097-0134(20000301)38:4<428::aid-prot8>3.0.co;2-n pmid:10707029 fatcat:wlgrshjcovdo3clu7rdtkm35ba
Nodes with the labels A,B,C,D in the pathway graph and the genome (line) are matched enzymes. ... pneumoniae 24 1.2 75/190 0.39 3.1 4 0.007 B. burgdorferi 20 1.5 50/138 0.36 2.5 2 0.001 T. pallidum 15 1.2 44/152 0.29 2.9 5 0.03 Synechocystis 24 3.6 59/453 0.13 2.5 8 0.02 D. ...doi:10.1101/gr.200602 pmid:12176930 pmcid:PMC186635 fatcat:pdxyu2uzofhexoubmxk4et4x24
D ... -7543/$ -see front matter D 2005 Elsevier Inc. ... (D, E) Luciferase reporter gene assays showing the effects of differentiation on a MEF2-responsive promoter (D) and 2 kb of the PGC-1a gene promoter (E) in the absence or presence of the myogenic antagonist ...doi:10.1016/j.ygeno.2005.08.009 pmid:16300922 fatcat:uatrjxctdvbz7cl4agntkfytni
C D 7 9 A , M S 4 A 1 B c e l l N K K I R 2 D L 1 , K I R 2 D L 3 , K I R 2 D L 4 , K I R 3 D L 1 , K I R 3 D L 2 , K I R 3 D L 3 , K I R 2 D S 4 N a t u r a l k i l l e r c e l l M o n o C D 8 6 , C S ... d . ...doi:10.1101/106039 fatcat:5nlr4vnpzneknde5veu3vo3swq
Exact sample annotation in expression microarray datasets is essential for any type of pharmacogenomics research. Results: Candidate markers were explored through the application of Hartigans' dip test statistics to a publically available human whole genome microarray dataset. The marker performance was tested on 188 serial samples from 53 donors and of variable tissue origin from five public microarray datasets. A qualified transcript marker panel consisting of three probe sets for humandoi:10.1186/2050-7771-2-17 pmid:25285214 pmcid:PMC4184161 fatcat:ax7y5bcpmrd25hwfws5wgfqb2i
more »... yte antigens HLA-DQA1 (2 probe sets) and HLA-DRB4 identified sample donor identifier inconsistencies in six of the 188 test samples. About 3% of the test samples require root-cause analysis due to unresolvable inaccuracies. Conclusions: The transcript marker panel consisting of HLA-DQA1 and HLA-DRB4 represents a robust, tissue-independent composite marker to assist control donor annotation concordance at the transcript level. Allele-selectivity of HLA genes renders them good candidates for "fingerprinting" with donor specific expression pattern.
(D) Relationship of TP53 mutation to CD8+ Tcell estimates in head and neck cancer. ... D: Mutual rank-based co-regulatory network around macrophage marker VSIG4 in TCGA. VSIG4, CD163, and MS4A4A were selected to create a signature to estimate macrophage content in tumors. ...doi:10.1371/journal.pone.0179726 pmid:28749946 pmcid:PMC5531551 fatcat:wh7qjiupyvfnvkrovjl6suybye
(D) Effect of the 176 selected activators and suppressors (grey points) on mutant SRE promoter. ... (B-D) Error bars indicate standard deviations (n = 3). Figure 2 . 2 Primary and secondary screen results. ...doi:10.1371/journal.pone.0005197 pmid:19381295 pmcid:PMC2668173 fatcat:43m76qioljf4vihtoc2436ytxe
doi:10.1093/bioinformatics/btt090 pmid:23428642 fatcat:s43jbt65zjctrm2wvjwu34lciu
Szustakowski for the GeneChip data analysis, Daniel Kemp for the myogenic gene set, and Thomas Hughes for critical review of this manuscript. ... wildtype myoblasts, Mark Montminy for the shRNA PGC-1␣ adenovirus, Deborah Ahern-Ridlon and Akos Szilvasi for technical assistance with the confocal microscopy and FACS analyses, Nanguneri Nirmala and Joseph ... D: total cellular lysates were made from C2C12 myoblasts incubated in basal or SP test media at indicated time points. ...doi:10.1152/ajpcell.00428.2006 pmid:17182725 fatcat:ayszzgx2jjbahggxzdxz2hchay
Contrary to the traditional biology approach, where the expression patterns of a handful of genes are studied at a time, microarray experiments enable biologists to study the expression patterns of many genes simultaneously from gene expression profile data and decipher the underlying hidden biological mechanism from the observed gene expression changes. While the statistical significance of the gene expression data can be deduced by various methods, the biological interpretation of the datadoi:10.1186/1756-0381-1-4 pmid:18822150 pmcid:PMC2553773 fatcat:hcibsqu7lrdpxeyzdzmtraem54
more »... sents a challenge. Results: A method, called CisTransMine, is proposed to help infer the underlying biological mechanisms for the observed gene expression changes in microarray experiments. Specifically, this method will predict potential cis-regulatory elements in promoter regions which could regulate gene expression changes. This approach builds on the MotifADE method published in 2004 and extends it with two modifications: up-regulated genes and down-regulated genes are tested separately and in addition, tests have been implemented to identify combinations of transcription factors that work synergistically. The method has been applied to a genome wide expression dataset intended to study myogenesis in a mouse C2C12 cell differentiation model. The results shown here both confirm the prior biological knowledge and facilitate the discovery of new biological insights. Conclusion: The results validate that the CisTransMine approach is a robust method to uncover the hidden transcriptional regulatory mechanisms that can facilitate the discovery of mechanisms of transcriptional regulation.
(A-D) Transcriptional activity of the indicated genes was measured using Affymetrix GeneChips and data was analyzed using GeneSpring bioinformatics software. ...doi:10.1186/1471-2199-8-46 pmid:17550601 pmcid:PMC1904231 fatcat:ivqm26swszgwtb27za72sxhu5i
Let D i , i = 1,2, ... n. denote the protein sequences contained in Cluster D and let E j , j = 1,2, ..., m denote the protein sequence contained in Cluster E. ... The geometric mean distance G between Cluster D and Cluster E is defined as Equation 4: Equation 4: The hierarchical average linkage clustering works in an iterative process: it begins with each protein ...doi:10.1186/1471-2105-6-242 pmid:16202129 pmcid:PMC1261163 fatcat:gz2cehmobredzbw3qcxkcnqllu
Fabrizio D, Lieber D, Malboeuf C, Silterra J, White E, Coyne M, et al. ... Rizvi H, Sanchez-Vega F, La K, Chatila W, Jonsson P, Halpenny D, et al. ...doi:10.1101/626143 fatcat:m77jhuidefcudge2kwl7fgkuve
Large-scale molecular profiling technologies have assisted the identification of disease biomarkers and facilitated the basic understanding of cellular processes. However, samples collected from human subjects in clinical trials possess a level of complexity, arising from multiple cell types, that can obfuscate the analysis of data derived from them. Failure to identify, quantify, and incorporate sources of heterogeneity into an analysis can have widespread and detrimental effects on subsequentdoi:10.1371/journal.pone.0027156 pmid:22110609 pmcid:PMC3217948 fatcat:i5h37rlrize7hlmfkvz2pxuliu
more »... statistical studies. We describe an approach that builds upon a linear latent variable model, in which expression levels from mixed cell populations are modeled as the weighted average of expression from different cell types. We solve these equations using quadratic programming, which efficiently identifies the globally optimal solution while preserving non-negativity of the fraction of the cells. We applied our method to various existing platforms to estimate proportions of different pure cell or tissue types and gene expression profilings of distinct phenotypes, with a focus on complex samples collected in clinical trials. We tested our methods on several well controlled benchmark data sets with known mixing fractions of pure cell or tissue types and mRNA expression profiling data from samples collected in a clinical trial. Accurate agreement between predicted and actual mixing fractions was observed. In addition, our method was able to predict mixing fractions for more than ten species of circulating cells and to provide accurate estimates for relatively rare cell types (,10% total population). Furthermore, accurate changes in leukocyte trafficking associated with Fingolomid (FTY720) treatment were identified that were consistent with previous results generated by both cell counts and flow cytometry. These data suggest that our method can solve one of the open questions regarding the analysis of complex transcriptional data: namely, how to identify the optimal mixing fractions in a given experiment.
., 2006; Szustakowski et al., 2006; Tian et al., 2005; Tomfohr et al., 2005; Zahn et al., 2006) . ... The LBF test first transforms the data according to Z ij ¼ X ij À median X i ð Þ , where X ij corresponds to the jth data point from the ith sample. ...doi:10.1093/bioinformatics/btm116 pmid:17392327 fatcat:ehbl5245crhlpg3m25unu5ivsu
« Previous Showing results 1 — 15 out of 45 results