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Ben S. Wittner

dblp:23/6832 · DBLP profile ↗
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3ranked-venue papers
1as first author
0since 2021 · last 2006
—ORCID · unresolved

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

Artificial intelligence and machine learning · 2 · 1 first-authorApplied, 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
2 papers
Bioinformatics and computational biology · 99% Computing education · 1%
Artificial intelligence
2 papers
Deep learning architectures and training · 67% Efficient and distributed learning · 33%

Topics — the 4 heaviest of 6, each with the papers that count most for it

TopicWeightPapersLastEvidence papers
Bioinformatics and computational biology › gene expression analysis › gene expression meta-analysis
differential expression meta-analysis
0.112006
RankProd: a bioconductor package for detecting differentially expressed genes in meta-analysis · Bioinform. 2006
Bioinformatics and computational biology
gene expression analysis
0.112006
RankProd: a bioconductor package for detecting differentially expressed genes in meta-analysis · Bioinform. 2006
Machine learning › Efficient and distributed learning
model compression
0.011987
Network Generality, Training Required, and Precision Required · NIPS 1987
Machine learning › Deep learning architectures and training › feedforward neural network
multilayer neural network
0.011987
Strategies for Teaching Layered Networks Classification Tasks · NIPS 1987

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

rank product · 0.1permutation test · 0.1false discovery rate · 0.1
YearPublicationVenuePosition
2006 RankProd: a bioconductor package for detecting differentially expressed genes in meta-analysis
abstract
UNLABELLED: While meta-analysis provides a powerful tool for analyzing microarray experiments by combining data from multiple studies, it presents unique computational challenges. The Bioconductor package RankProd provides a new and intuitive tool for this purpose in detecting differentially expressed genes under two experimental conditions. The package modifies and extends the rank product method proposed by Breitling et al., [(2004) FEBS Lett., 573, 83-92] to integrate multiple microarray studies from different laboratories and/or platforms. It offers several advantages over t-test based methods and accepts pre-processed expression datasets produced from a wide variety of platforms. The significance of the detection is assessed by a non-parametric permutation test, and the associated P-value and false discovery rate (FDR) are included in the output alongside the genes that are detected by user-defined criteria. A visualization plot is provided to view actual expression levels for each gene with estimated significance measurements. AVAILABILITY: RankProd is available at Bioconductor http://www.bioconductor.org. A web-based interface will soon be available at http://cactus.salk.edu/RankProd
Fangxin Hong, Rainer Breitling, Connor W. McEntee, Ben S. Wittner, Jennifer L. Nemhauser, Joanne Chory
Bioinform.4
1987 Network Generality, Training Required, and Precision Required
John S. Denker, Ben S. Wittner
NIPS2
1987 Strategies for Teaching Layered Networks Classification Tasks
Ben S. Wittner, John S. Denker
NIPS1