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Eugenio Marco

dblp:270/8562 · DBLP profile ↗
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1ranked-venue papers
0as first author
0since 2021 · last 2007
0000-0002-3675-263XORCID · reported

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 · 50% Computational science and engineering · 50%

Topics — the 2 heaviest of 2, each with the papers that count most for it

TopicWeightPapersLastEvidence papers
Bioinformatics and computational biology › biological imaging
cellular imaging
0.112007
Phenotypic clustering of yeast mutants based on kinetochore microtubule dynamics · Bioinform. 2007
Computational science and engineering
time series analysis
0.112007
Phenotypic clustering of yeast mutants based on kinetochore microtubule dynamics · Bioinform. 2007

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

hierarchical clustering · 0.1ARMA models · 0.1
YearPublicationVenuePosition
2007 Phenotypic clustering of yeast mutants based on kinetochore microtubule dynamics
abstract
MOTIVATION: Kinetochores are multiprotein complexes which mediate chromosome attachment to microtubules (MTs) of the mitotic spindle. They regulate MT dynamics during chromosome segregation. Our goal is to identify groups of kinetochore proteins with similar effects on MT dynamics, revealing pathways through which kinetochore proteins transform chemical and mechanical input signals into cues of MT regulation. RESULTS: We have developed a hierarchical, agglomerative clustering algorithm that groups Saccharomyces cerevisiae strains based on MT-mediated chromosome dynamics measured by high-resolution live cell microscopy. Clustering is based on parameters of autoregressive moving average (ARMA) models of the probed dynamics. We have found that the regulation of wildtype MT dynamics varies with cell cycle and temperature, but not with the chromosome an MT is attached to. By clustering the dynamics of mutants, we discovered that the three genes IPL1, DAM1 and KIP3 co-regulate MT dynamics. Our study establishes the clustering of chromosome and MT dynamics by ARMA descriptors as a sensitive framework for the systematic identification of kinetochore protein subcomplexes and pathways for the regulation of MT dynamics. AVAILABILITY: The clustering code, written in Matlab, can be downloaded from http://lccb.scripps.edu. ('download' hyperlink at bottom of website). SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.
Khuloud Jaqaman, Jonas F. Dorn, Eugenio Marco, Peter K. Sorger, Gaudenz Danuser
Bioinform.3