76,494 research outputs found
Essential guidelines for computational method benchmarking
In computational biology and other sciences, researchers are frequently faced
with a choice between several computational methods for performing data
analyses. Benchmarking studies aim to rigorously compare the performance of
different methods using well-characterized benchmark datasets, to determine the
strengths of each method or to provide recommendations regarding suitable
choices of methods for an analysis. However, benchmarking studies must be
carefully designed and implemented to provide accurate, unbiased, and
informative results. Here, we summarize key practical guidelines and
recommendations for performing high-quality benchmarking analyses, based on our
experiences in computational biology.Comment: Minor update
Essential guidelines for computational method benchmarking
In computational biology and other sciences, researchers are frequently faced with a choice between several computational methods for performing data analyses. Benchmarking studies aim to rigorously compare the performance of different methods using well-characterized benchmark datasets, to determine the strengths of each method or to provide recommendations regarding suitable choices of methods for an analysis. However, benchmarking studies must be carefully designed and implemented to provide accurate, unbiased, and informative results. Here, we summarize key practical guidelines and recommendations for performing high-quality benchmarking analyses, based on our experiences in computational biology
Evaluation of analytical methodologies to derive vulnerability functions
The recognition of fragility functions as a fundamental tool in seismic risk assessment has led to the
development of more and more complex and elaborate procedures for their computation. Although vulnerability
functions have been traditionally produced using observed damage and loss data, more recent studies propose the
employment of analytical methodologies as a way to overcome the frequent lack of post-earthquake data. The
variation of the structural modelling approaches on the estimation of building capacity has been the target of
many studies in the past, however, its influence in the resulting vulnerability model, impact in loss estimations or
propagation of the uncertainty to the seismic risk calculations has so far been the object of restricted scrutiny.
Hence, in this paper, an extensive study of static and dynamic procedures for estimating the nonlinear response
of buildings has been carried out in order to evaluate the impact of the chosen methodology on the resulting
vulnerability and risk outputs. Moreover, the computational effort and numerical stability provided by each
approach were evaluated and conclusions were obtained regarding which one offers the optimal balance between
accuracy and complexity
Combining local regularity estimation and total variation optimization for scale-free texture segmentation
Texture segmentation constitutes a standard image processing task, crucial to
many applications. The present contribution focuses on the particular subset of
scale-free textures and its originality resides in the combination of three key
ingredients: First, texture characterization relies on the concept of local
regularity ; Second, estimation of local regularity is based on new multiscale
quantities referred to as wavelet leaders ; Third, segmentation from local
regularity faces a fundamental bias variance trade-off: In nature, local
regularity estimation shows high variability that impairs the detection of
changes, while a posteriori smoothing of regularity estimates precludes from
locating correctly changes. Instead, the present contribution proposes several
variational problem formulations based on total variation and proximal
resolutions that effectively circumvent this trade-off. Estimation and
segmentation performance for the proposed procedures are quantified and
compared on synthetic as well as on real-world textures
Supervised nonlinear spectral unmixing using a post-nonlinear mixing model for hyperspectral imagery
This paper presents a nonlinear mixing model for hyperspectral image unmixing. The proposed model assumes that the pixel reflectances are nonlinear functions of pure spectral components contaminated by an additive white Gaussian noise. These nonlinear functions are approximated using polynomial functions leading to a polynomial postnonlinear mixing model. A Bayesian algorithm and optimization methods are proposed to estimate the parameters involved in the model. The performance of the unmixing strategies is evaluated by simulations conducted on synthetic and real data
Unremarkable AI: Fitting Intelligent Decision Support into Critical, Clinical Decision-Making Processes
Clinical decision support tools (DST) promise improved healthcare outcomes by
offering data-driven insights. While effective in lab settings, almost all DSTs
have failed in practice. Empirical research diagnosed poor contextual fit as
the cause. This paper describes the design and field evaluation of a radically
new form of DST. It automatically generates slides for clinicians' decision
meetings with subtly embedded machine prognostics. This design took inspiration
from the notion of "Unremarkable Computing", that by augmenting the users'
routines technology/AI can have significant importance for the users yet remain
unobtrusive. Our field evaluation suggests clinicians are more likely to
encounter and embrace such a DST. Drawing on their responses, we discuss the
importance and intricacies of finding the right level of unremarkableness in
DST design, and share lessons learned in prototyping critical AI systems as a
situated experience
Species-level functional profiling of metagenomes and metatranscriptomes.
Functional profiles of microbial communities are typically generated using comprehensive metagenomic or metatranscriptomic sequence read searches, which are time-consuming, prone to spurious mapping, and often limited to community-level quantification. We developed HUMAnN2, a tiered search strategy that enables fast, accurate, and species-resolved functional profiling of host-associated and environmental communities. HUMAnN2 identifies a community's known species, aligns reads to their pangenomes, performs translated search on unclassified reads, and finally quantifies gene families and pathways. Relative to pure translated search, HUMAnN2 is faster and produces more accurate gene family profiles. We applied HUMAnN2 to study clinal variation in marine metabolism, ecological contribution patterns among human microbiome pathways, variation in species' genomic versus transcriptional contributions, and strain profiling. Further, we introduce 'contributional diversity' to explain patterns of ecological assembly across different microbial community types
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