An Adaptive Combination of Matchers: Application to the Mapping of Biological Ontologies for Genome Annotation

2009 ◽  
pp. 113-126 ◽  
Author(s):  
Bastien Rance ◽  
Jean-François Gibrat ◽  
Christine Froidevaux
Keyword(s):  
Genome Annotation ◽  
Adaptive Combination ◽  
Biological Ontologies

scholarly journals An Effective Adaptive Combination Strategy for Distributed Learning Network

2021 ◽  
Vol 11 (12) ◽  
pp. 5723
Author(s):  
Chundong Xu ◽  
Qinglin Li ◽  
Dongwen Ying
Keyword(s):  
Minimum Variance ◽  
Iteration Scheme ◽  
Estimation Problem ◽  
Lms Algorithm ◽  
Unbiased Estimation ◽  
Mean Square ◽  
Least Mean Square ◽  
Combination Strategy ◽  
Adaptive Combination ◽  
Diffusion Networks

In this paper, we develop a modified adaptive combination strategy for the distributed estimation problem over diffusion networks. We still consider the online adaptive combiners estimation problem from the perspective of minimum variance unbiased estimation. In contrast with the classic adaptive combination strategy which exploits orthogonal projection technology, we formulate a non-constrained mean-square deviation (MSD) cost function by introducing Lagrange multipliers. Based on the Karush–Kuhn–Tucker (KKT) conditions, we derive the fixed-point iteration scheme of adaptive combiners. Illustrative simulations validate the improved transient and steady-state performance of the diffusion least-mean-square LMS algorithm incorporated with the proposed adaptive combination strategy.

scholarly journals Uncovering transcriptional dark matter via gene annotation independent single-cell RNA sequencing analysis

2021 ◽  
Vol 12 (1) ◽  
Author(s):  
Michael F. Z. Wang ◽  
Madhav Mantri ◽  
Shao-Pei Chou ◽  
Gaetano J. Scuderi ◽  
David W. McKellar ◽  
...  
Keyword(s):  
Single Cell ◽  
Genome Annotation ◽  
Gene Annotation ◽  
Active Regions ◽  
Sequencing Analysis ◽  
Biologically Relevant ◽  
Mole Rat ◽  
Genome Annotations ◽  
Cell Expression ◽  
High Quality Genome

AbstractConventional scRNA-seq expression analyses rely on the availability of a high quality genome annotation. Yet, as we show here with scRNA-seq experiments and analyses spanning human, mouse, chicken, mole rat, lemur and sea urchin, genome annotations are often incomplete, in particular for organisms that are not routinely studied. To overcome this hurdle, we created a scRNA-seq analysis routine that recovers biologically relevant transcriptional activity beyond the scope of the best available genome annotation by performing scRNA-seq analysis on any region in the genome for which transcriptional products are detected. Our tool generates a single-cell expression matrix for all transcriptionally active regions (TARs), performs single-cell TAR expression analysis to identify biologically significant TARs, and then annotates TARs using gene homology analysis. This procedure uses single-cell expression analyses as a filter to direct annotation efforts to biologically significant transcripts and thereby uncovers biology to which scRNA-seq would otherwise be in the dark.

Whole genome sequencing and genome annotation of Dermacoccus abyssi strain HZAU 226 isolated from spoiled eggs

Genomics ◽  
2020 ◽  
Author(s):  
Xinshuai Zhang ◽  
Yao Ruan ◽  
Wukang Liu ◽  
Qian Chen ◽  
Lihong Gu ◽  
...  
Keyword(s):  
Whole Genome Sequencing ◽  
Genome Sequencing ◽  
Genome Annotation ◽  
Whole Genome

Exploration of Diffusion LMS Over Static and Adaptive Combination Policy

2020 ◽  
Author(s):  
Chikwendu A. Ijeoma ◽  
Md A Hossin ◽  
Hailegiorgis A Bemnet ◽  
Alula A. Tesfaye ◽  
Amare H. Hailu ◽  
...  
Keyword(s):  
Adaptive Combination ◽  
Combination Policy

scholarly journals Genome annotation by high-throughput 5' RNA end determination

2004 ◽  
Vol 101 (6) ◽  
pp. 1650-1655 ◽  
Author(s):  
B. J. Hwang ◽  
H.-M. Muller ◽  
P. W. Sternberg
Keyword(s):  
High Throughput ◽  
Genome Annotation

scholarly journals MADAP, a flexible clustering tool for the interpretation of one-dimensional genome annotation data

2007 ◽  
Vol 35 (Web Server) ◽  
pp. W201-W205 ◽  
Author(s):  
C. D. Schmid ◽  
T. Sengstag ◽  
P. Bucher ◽  
M. Delorenzi
Keyword(s):  
Genome Annotation ◽  
One Dimensional ◽  
Annotation Data
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