3,766 research outputs found
Every which way? On predicting tumor evolution using cancer progression models
Successful prediction of the likely paths of tumor progression is valuable for diagnostic,
prognostic, and treatment purposes. Cancer progression models (CPMs) use cross-sectional samples to identify restrictions in the order of accumulation of driver mutations and
thus CPMs encode the paths of tumor progression. Here we analyze the performance of
four CPMs to examine whether they can be used to predict the true distribution of paths of
tumor progression and to estimate evolutionary unpredictability. Employing simulations we
show that if fitness landscapes are single peaked (have a single fitness maximum) there is
good agreement between true and predicted distributions of paths of tumor progression
when sample sizes are large, but performance is poor with the currently common much
smaller sample sizes. Under multi-peaked fitness landscapes (i.e., those with multiple fitness maxima), performance is poor and improves only slightly with sample size. In all
cases, detection regime (when tumors are sampled) is a key determinant of performance.
Estimates of evolutionary unpredictability from the best performing CPM, among the four
examined, tend to overestimate the true unpredictability and the bias is affected by detection
regime; CPMs could be useful for estimating upper bounds to the true evolutionary unpredictability. Analysis of twenty-two cancer data sets shows low evolutionary unpredictability
for several of the data sets. But most of the predictions of paths of tumor progression are
very unreliable, and unreliability increases with the number of features analyzed. Our results
indicate that CPMs could be valuable tools for predicting cancer progression but that, currently, obtaining useful predictions of paths of tumor progression from CPMs is dubious, and
emphasize the need for methodological work that can account for the probably multi-peaked
fitness landscapes in cancerWork partially supported by BFU2015-
67302-R (MINECO/FEDER, EU) to RDU. CV
supported by PEJD-2016-BMD-2116 from
Comunidad de Madrid to RD
The Szeg\"o curve and Laguerre polynomials with large negative parameters
We study the asymptotic zero distribution of the rescaled Laguerre
polynomials, , with the parameter
varying in such a way that . The connection with the so-called Szeg\"{o} curve will
be showed
Cellular covers of local groups
We prove that, in the category of groups, the composition of a cellularization and a localization functor need not be idempotent. This provides a negative answer to a question of Emmanuel Dror Farjoun.Ministerio de Educación y CienciaJunta de Andalucí
On soft/hard handoff for packet data services in cellular CDMA mobiles systems
Benefits of macrodiversity operation for packet data services in third generation mobile systems are not obvious. Retransmission procedures to enhance link performance and higher downlink bandwidth requirements could question macrodiversity usage. This paper describes a simple methodology to compare soft and hard handoff performance in terms of transmission delay for packet data services. The handover procedures are based exclusively on power criteria and hysteresis margins.Peer ReviewedPostprint (published version
On the explosion breakdown rate of the maximum bias function of some scale and location estimates
Multiple slot allocation for voice/data transmission over PRMA++ applied to FRAMES multiple access mode 1
This paper presents some simulation results of PRMA++ for voice and data transmission over a physical air interface platform defined in the ACTS European project FRAMES. Variations on the statistics of speech sources (activity factor, petition rate) are studied and conclusions are obtained for optimal frame dimensioning. For data transmission, a multiple slot allocation scheme is presented and results are shown for different source rates and packet lengths.Peer ReviewedPostprint (published version
OncoSimulR: Genetic simulation with arbitrary epistasis and mutator genes in asexual populations
OncoSimulR implements forward-time genetic simulations of biallelic loci in asexual
populations with special focus on cancer progression. Fitness can be defined as an arbitrary function
of genetic interactions between multiple genes or modules of genes, including epistasis, restrictions
in the order of accumulation of mutations, and order effects. Mutation rates can differ
among genes, and can be affected by (anti)mutator genes. Also available are sampling from simulations
(including single-cell sampling), plotting the genealogical relationships of clones and generating
and plotting fitness landscapesSupported by BFU2015-67302-R (MINECO/FEDER, EU
La obra histórica de Al-Zayyaní sobre los 'alawíes y su influencia en la historiografía marroquí
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