1,237 research outputs found
LIPIcs, Volume 251, ITCS 2023, Complete Volume
LIPIcs, Volume 251, ITCS 2023, Complete Volum
Design of new algorithms for gene network reconstruction applied to in silico modeling of biomedical data
Programa de Doctorado en Biotecnología, Ingeniería y Tecnología QuímicaLínea de Investigación: Ingeniería, Ciencia de Datos y BioinformáticaClave Programa: DBICódigo Línea: 111The root causes of disease are still poorly understood. The success of current therapies is limited because persistent diseases are frequently treated based on their symptoms rather than the underlying cause of the disease. Therefore, biomedical research is experiencing a technology-driven shift to data-driven holistic approaches to better characterize the molecular mechanisms causing disease. Using omics data as an input, emerging disciplines like network biology attempt to model the relationships between biomolecules. To this effect, gene co- expression networks arise as a promising tool for deciphering the relationships between genes in large transcriptomic datasets. However, because of their low specificity and high false positive rate, they demonstrate a limited capacity to retrieve the disrupted mechanisms that lead to disease onset, progression, and maintenance. Within the context of statistical modeling, we dove deeper into the reconstruction of gene co-expression networks with the specific goal of discovering disease-specific features directly from expression data. Using ensemble techniques, which combine the results of various metrics, we were able to more precisely capture biologically significant relationships between genes. We were able to find de novo potential disease-specific features with the help of prior biological knowledge and the development of new network inference techniques.
Through our different approaches, we analyzed large gene sets across multiple samples and used gene expression as a surrogate marker for the inherent biological processes, reconstructing robust gene co-expression networks that are simple to explore. By mining disease-specific gene co-expression networks we come up with a useful framework for identifying new omics-phenotype associations from conditional expression datasets.In this sense, understanding diseases from the perspective of biological network perturbations will improve personalized medicine, impacting rational biomarker discovery, patient stratification and drug design, and ultimately leading to more targeted therapies.Universidad Pablo de Olavide de Sevilla. Departamento de Deporte e Informátic
Seamless Multimodal Biometrics for Continuous Personalised Wellbeing Monitoring
Artificially intelligent perception is increasingly present in the lives of
every one of us. Vehicles are no exception, (...) In the near future, pattern
recognition will have an even stronger role in vehicles, as self-driving cars
will require automated ways to understand what is happening around (and within)
them and act accordingly. (...) This doctoral work focused on advancing
in-vehicle sensing through the research of novel computer vision and pattern
recognition methodologies for both biometrics and wellbeing monitoring. The
main focus has been on electrocardiogram (ECG) biometrics, a trait well-known
for its potential for seamless driver monitoring. Major efforts were devoted to
achieving improved performance in identification and identity verification in
off-the-person scenarios, well-known for increased noise and variability. Here,
end-to-end deep learning ECG biometric solutions were proposed and important
topics were addressed such as cross-database and long-term performance,
waveform relevance through explainability, and interlead conversion. Face
biometrics, a natural complement to the ECG in seamless unconstrained
scenarios, was also studied in this work. The open challenges of masked face
recognition and interpretability in biometrics were tackled in an effort to
evolve towards algorithms that are more transparent, trustworthy, and robust to
significant occlusions. Within the topic of wellbeing monitoring, improved
solutions to multimodal emotion recognition in groups of people and
activity/violence recognition in in-vehicle scenarios were proposed. At last,
we also proposed a novel way to learn template security within end-to-end
models, dismissing additional separate encryption processes, and a
self-supervised learning approach tailored to sequential data, in order to
ensure data security and optimal performance. (...)Comment: Doctoral thesis presented and approved on the 21st of December 2022
to the University of Port
NeighborNet: improved algorithms and implementation
NeighborNet constructs phylogenetic networks to visualize distance data. It is a popular method used in a wide range of applications. While several studies have investigated its mathematical features, here we focus on computational aspects. The algorithm operates in three steps. We present a new simplified formulation of the first step, which aims at computing a circular ordering. We provide the first technical description of the second step, the estimation of split weights. We review the third step by constructing and drawing the network. Finally, we discuss how the networks might best be interpreted, review related approaches, and present some open questions
OpenLB User Guide: Associated with Release 1.6 of the Code
OpenLB is an object-oriented implementation of LBM. It is the first
implementation of a generic platform for LBM programming, which is shared with
the open source community (GPLv2). Since the first release in 2007, the code
has been continuously improved and extended which is documented by thirteen
releases as well as the corresponding release notes which are available on the
OpenLB website (https://www.openlb.net). The OpenLB code is written in C++ and
is used by application programmers as well as developers, with the ability to
implement custom models OpenLB supports complex data structures that allow
simulations in complex geometries and parallel execution using MPI, OpenMP and
CUDA on high-performance computers. The source code uses the concepts of
interfaces and templates, so that efficient, direct and intuitive
implementations of the LBM become possible. The efficiency and scalability has
been checked and proved by code reviews. This user manual and a source code
documentation by DoxyGen are available on the OpenLB project website
Efficient Generator of Mathematical Expressions for Symbolic Regression
We propose an approach to symbolic regression based on a novel variational
autoencoder for generating hierarchical structures, HVAE. It combines simple
atomic units with shared weights to recursively encode and decode the
individual nodes in the hierarchy. Encoding is performed bottom-up and decoding
top-down. We empirically show that HVAE can be trained efficiently with small
corpora of mathematical expressions and can accurately encode expressions into
a smooth low-dimensional latent space. The latter can be efficiently explored
with various optimization methods to address the task of symbolic regression.
Indeed, random search through the latent space of HVAE performs better than
random search through expressions generated by manually crafted probabilistic
grammars for mathematical expressions. Finally, EDHiE system for symbolic
regression, which applies an evolutionary algorithm to the latent space of
HVAE, reconstructs equations from a standard symbolic regression benchmark
better than a state-of-the-art system based on a similar combination of deep
learning and evolutionary algorithms.\v{z}Comment: 35 pages, 11 tables, 7 multi-part figures, Machine learning
(Springer) and journal track of ECML/PKDD 202
Graph Theory Applications in Advanced Geospatial Research
Geospatial sciences include a wide range of applications, from environmental
monitoring transportation to infrastructure planning, as well as location-based
analysis and services. Graph theory algorithms in mathematics have emerged as
indispensable tools in these domains due to their capability to model and
analyse spatial relationships efficiently. This article explores the
applications of graph theory algorithms in geospatial sciences, highlighting
their role in network analysis, spatial connectivity, geographic information
systems, and various other spatial problem-solving scenarios like digital twin.
The article provides a comprehensive idea about graph theory's key concepts and
algorithms that assist the geospatial modelling processes and insights into
real-world geospatial challenges and opportunities. It lists the extensive
research, innovative technologies and methodologies implemented in this domain
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