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Marshall Lieberfarb

dblp:24/157 · DBLP profile ↗
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1ranked-venue papers
0as first author
0since 2021 · last 2004
—ORCID · none

Domains — the database's venue-derived domains; a paper can count in several

Applied, interdisciplinary, general and emerging computing · 1

Expertise — from the expertise taxonomy: the topics of the expert's papers under the CCF categories. A weight counts papers with recency: 1 for a paper about the topic, 0.3 when the topic is its context, halved every five years.

Interdisciplinary, comprehensive, and emerging computing
1 paper
Bioinformatics and computational biology · 100%

Topics — the 5 heaviest of 5, each with the papers that count most for it

TopicWeightPapersLastEvidence papers
Bioinformatics and computational biology
cancer genomics
0.012004
dChipSNP: significance curve and clustering of SNP-array-based loss-of-heterozygosity data · Bioinform. 2004
Bioinformatics and computational biology › genomics
genome visualization
0.012004
dChipSNP: significance curve and clustering of SNP-array-based loss-of-heterozygosity data · Bioinform. 2004
Bioinformatics and computational biology › statistical genetics
genotype data analysis
0.012004
dChipSNP: significance curve and clustering of SNP-array-based loss-of-heterozygosity data · Bioinform. 2004
Bioinformatics and computational biology › cancer genomics › chromosomal aberration detection
loss of heterozygosity detection
0.012004
dChipSNP: significance curve and clustering of SNP-array-based loss-of-heterozygosity data · Bioinform. 2004
Bioinformatics and computational biology › statistical genetics
single nucleotide polymorphism analysis
0.012004
dChipSNP: significance curve and clustering of SNP-array-based loss-of-heterozygosity data · Bioinform. 2004

Methods — techniques the papers use, named apart from their topics

statistical inference · 0.0clustering · 0.0
YearPublicationVenuePosition
2004 dChipSNP: significance curve and clustering of SNP-array-based loss-of-heterozygosity data
abstract
MOTIVATION: Oligonucleotide microarrays allow genotyping of thousands of single-nucleotide polymorphisms (SNPs) in parallel. Recently, this technology has been applied to loss-of-heterozygosity (LOH) analysis of paired normal and tumor samples. However, methods and software for analyzing such data are not fully developed. RESULT: Here, we report automated methods for pooling SNP array replicates to make LOH calls, visualizing SNP and LOH data along chromosomes in the context of genes and cytobands, making statistical inference to identify shared LOH regions, clustering samples based on LOH profiles and correlating the clustering results to clinical variables. Application of these methods to prostate and breast cancer datasets generates biologically important results. AVAILABILITY: The software module dChipSNP implementing these methods is available at http://biosun1.harvard.edu/complab/dchip/snp/ SUPPLEMENTARY INFORMATION: The breast cancer data are provided by Andrea L. Richardson, Zhigang C. Wang and James D. Iglehart.
Lee-Jen Wei, William R. Sellers, Marshall Lieberfarb, Wing Hung Wong, Cheng Li 0016
Bioinform.4