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Nicola Lama

dblp:44/3573 · DBLP profile ↗
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1ranked-venue papers
1as first author
0since 2021 · last 2008
—ORCID · unresolved

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

Applied, interdisciplinary, general and emerging computing · 1 · 1 first-author

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 · 75% Computational science and engineering · 25%
Artificial intelligence
1 paper
Probabilistic and Bayesian machine learning · 100%

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

TopicWeightPapersLastEvidence papers
Bioinformatics and computational biology
gene expression analysis		 0.112008
vbmp: Variational Bayesian Multinomial Probit Regression for multi-class classification in R · Bioinform. 2008
Bioinformatics and computational biology › gene expression analysis › gene expression classification
microarray classification		 0.112008
vbmp: Variational Bayesian Multinomial Probit Regression for multi-class classification in R · Bioinform. 2008
Computational science and engineering › pattern recognition
multiclass classification		 0.112008
vbmp: Variational Bayesian Multinomial Probit Regression for multi-class classification in R · Bioinform. 2008
Bioinformatics and computational biology › biostatistics › statistical bioinformatics
statistical genomics		 0.112008
vbmp: Variational Bayesian Multinomial Probit Regression for multi-class classification in R · Bioinform. 2008
Machine learning › Probabilistic and Bayesian machine learning › stochastic processes › gaussian process
gaussian process classification		 0.012008
vbmp: Variational Bayesian Multinomial Probit Regression for multi-class classification in R · Bioinform. 2008

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

variational bayesian inference · 0.2automatic relevance determination · 0.2
YearPublicationVenuePosition
2008 vbmp: Variational Bayesian Multinomial Probit Regression for multi-class classification in R
abstract
SUMMARY: Vbmp is an R package for Gaussian Process classification of data over multiple classes. It features multinomial probit regression with Gaussian Process priors and estimates class posterior probabilities employing fast variational approximations to the full posterior. This software also incorporates feature weighting by means of Automatic Relevance Determination. Being equipped with only one main function and reasonable default values for optional parameters, vbmp combines flexibility with ease of usage as is demonstrated on a breast cancer microarray study. AVAILABILITY: The R library vbmp implementing this method is part of Bioconductor and can be downloaded from http://www.dcs.gla.ac.uk/~girolami
Nicola Lama, Mark A. Girolami
 Bioinform.1