1,525 research outputs found
DeepFrag-k: A Fragment-Based Deep Learning Approach for Protein Fold Recognition
Background: One of the most essential problems in structural bioinformatics is protein fold recognition. In this paper, we design a novel deep learning architecture, so-called DeepFrag-k, which identifies fold discriminative features at fragment level to improve the accuracy of protein fold recognition. DeepFrag-k is composed of two stages: the first stage employs a multi-modal Deep Belief Network (DBN) to predict the potential structural fragments given a sequence, represented as a fragment vector, and then the second stage uses a deep convolutional neural network (CNN) to classify the fragment vector into the corresponding fold.
Results: Our results show that DeepFrag-k yields 92.98% accuracy in predicting the top-100 most popular fragments, which can be used to generate discriminative fragment feature vectors to improve protein fold recognition.
Conclusions: There is a set of fragments that can serve as structural âkeywordsâ distinguishing between major protein folds. The deep learning architecture in DeepFrag-k is able to accurately identify these fragments as structure features to improve protein fold recognition
Highly Accurate Fragment Library for Protein Fold Recognition
Proteins play a crucial role in living organisms as they perform many vital tasks in every living cell. Knowledge of protein folding has a deep impact on understanding the heterogeneity and molecular functions of proteins. Such information leads to crucial advances in drug design and disease understanding. Fold recognition is a key step in the protein structure discovery process, especially when traditional computational methods fail to yield convincing structural homologies. In this work, we present a new protein fold recognition approach using machine learning and data mining methodologies.
First, we identify a protein structural fragment library (Frag-K) composed of a set of backbone fragments ranging from 4 to 20 residues as the structural âkeywordsâ that can effectively distinguish between major protein folds. We firstly apply randomized spectral clustering and random forest algorithms to construct representative and sensitive protein fragment libraries from a large-scale of high-quality, non-homologous protein structures available in PDB. We analyze the impacts of clustering cut-offs on the performance of the fragment libraries. Then, the Frag-K fragments are employed as structural features to classify protein structures in major protein folds defined by SCOP (Structural Classification of Proteins). Our results show that a structural dictionary with ~400 4- to 20-residue Frag-K fragments is capable of classifying major SCOP folds with high accuracy.
Then, based on Frag-k, we design a novel deep learning architecture, so-called DeepFrag-k, which identifies fold discriminative features to improve the accuracy of protein fold recognition. DeepFrag-k is composed of two stages: the first stage employs a multimodal Deep Belief Network (DBN) to predict the potential structural fragments given a sequence, represented as a fragment vector, and then the second stage uses a deep convolution neural network (CNN) to classify the fragment vectors into the corresponding folds. Our results show that DeepFrag-k yields 92.98% accuracy in predicting the top-100 most popular fragments, which can be used to generate discriminative fragment feature vectors to improve protein fold recognition
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A survey of swarm intelligence for dynamic optimization: algorithms and applications
Swarm intelligence (SI) algorithms, including ant colony optimization, particle swarm optimization, bee-inspired algorithms, bacterial foraging optimization, firefly algorithms, fish swarm optimization and many more, have been proven to be good methods to address difficult optimization problems under stationary environments. Most SI algorithms have been developed to address stationary optimization problems and hence, they can converge on the (near-) optimum solution efficiently. However, many real-world problems have a dynamic environment that changes over time. For such dynamic optimization problems (DOPs), it is difficult for a conventional SI algorithm to track the changing optimum once the algorithm has converged on a solution. In the last two decades, there has been a growing interest of addressing DOPs using SI algorithms due to their adaptation capabilities. This paper presents a broad review on SI dynamic optimization (SIDO) focused on several classes of problems, such as discrete, continuous, constrained, multi-objective and classification problems, and real-world applications. In addition, this paper focuses on the enhancement strategies integrated in SI algorithms to address dynamic changes, the performance measurements and benchmark generators used in SIDO. Finally, some considerations about future directions in the subject are given
VISIO-HAPTIC DEFORMABLE MODEL FOR HAPTIC DOMINANT PALPATION SIMULATOR
Vision and haptic are two most important modalities in a medical simulation. While
visual cues assist one to see his actions when performing a medical procedure, haptic
cues enable feeling the object being manipulated during the interaction. Despite their
importance in a computer simulation, the combination of both modalities has not been
adequately assessed, especially that in a haptic dominant environment. Thus, resulting
in poor emphasis in resource allocation management in terms of effort spent in
rendering the two modalities for simulators with realistic real-time interactions.
Addressing this problem requires an investigation on whether a single modality
(haptic) or a combination of both visual and haptic could be better for learning skills
in a haptic dominant environment such as in a palpation simulator. However, before
such an investigation could take place one main technical implementation issue in
visio-haptic rendering needs to be addresse
Scalarized Preferences in Multi-objective Optimization
Multikriterielle Optimierungsprobleme verfĂŒgen ĂŒber keine Lösung, die optimal in jeder Zielfunktion ist. Die Schwierigkeit solcher Probleme liegt darin eine Kompromisslösung zu finden, die den PrĂ€ferenzen des Entscheiders genĂŒgen, der den Kompromiss implementiert. Skalarisierung â die Abbildung des Vektors der Zielfunktionswerte auf eine reelle Zahl â identifiziert eine einzige Lösung als globales PrĂ€ferenzenoptimum um diese Probleme zu lösen. Allerdings generieren Skalarisierungsmethoden keine zusĂ€tzlichen Informationen ĂŒber andere Kompromisslösungen, die die PrĂ€ferenzen des Entscheiders bezĂŒglich des globalen Optimums verĂ€ndern könnten. Um dieses Problem anzugehen stellt diese Dissertation eine theoretische und algorithmische Analyse skalarisierter PrĂ€ferenzen bereit. Die theoretische Analyse besteht aus der Entwicklung eines Ordnungsrahmens, der PrĂ€ferenzen als Problemtransformationen charakterisiert, die prĂ€ferierte Untermengen der Paretofront definieren. Skalarisierung wird als Transformation der Zielmenge in diesem Ordnungsrahmen dargestellt. Des Weiteren werden Axiome vorgeschlagen, die wĂŒnschenswerte Eigenschaften von Skalarisierungsfunktionen darstellen. Es wird gezeigt unter welchen Bedingungen existierende Skalarisierungsfunktionen diese Axiome erfĂŒllen. Die algorithmische Analyse kennzeichnet PrĂ€ferenzen anhand des Resultats, das ein Optimierungsalgorithmus generiert. Zwei neue Paradigmen werden innerhalb dieser Analyse identifiziert. FĂŒr beide Paradigmen werden Algorithmen entworfen, die skalarisierte PrĂ€ferenzeninformationen verwenden: PrĂ€ferenzen-verzerrte Paretofrontapproximationen verteilen Punkte ĂŒber die gesamte Paretofront, fokussieren aber mehr Punkte in Regionen mit besseren Skalarisierungswerten; multimodale PrĂ€ferenzenoptima sind Punkte, die lokale Skalarisierungsoptima im Zielraum darstellen. Ein Drei-Stufen-Algorith\-mus wird entwickelt, der lokale Skalarisierungsoptima approximiert und verschiedene Methoden werden fĂŒr die unterschiedlichen Stufen evaluiert. Zwei Realweltprobleme werden vorgestellt, die die NĂŒtzlichkeit der beiden Algorithmen illustrieren. Das erste Problem besteht darin FahrplĂ€ne fĂŒr ein Blockheizkraftwerk zu finden, die die erzeugte ElektrizitĂ€t und WĂ€rme maximieren und den Kraftstoffverbrauch minimiert. PrĂ€ferenzen-verzerrte Approximationen generieren mehr Energie-effiziente Lösungen, unter denen der Entscheider seine favorisierte Lösung auswĂ€hlen kann, indem er die Konflikte zwischen den drei Zielen abwĂ€gt. Das zweite Problem beschĂ€ftigt sich mit der Erstellung von FahrplĂ€nen fĂŒr GerĂ€te in einem WohngebĂ€ude, so dass Energiekosten, Kohlenstoffdioxidemissionen und thermisches Unbehagen minimiert werden. Es wird gezeigt, dass lokale Skalarisierungsoptima FahrplĂ€ne darstellen, die eine gute Balance zwischen den drei Zielen bieten. Die Analyse und die Experimente, die in dieser Arbeit vorgestellt werden, ermöglichen es Entscheidern bessere Entscheidungen zu treffen indem Methoden angewendet werden, die mehr Optionen generieren, die mit den PrĂ€ferenzen der Entscheider ĂŒbereinstimmen
Computer vision and optimization methods applied to the measurements of in-plane deformations
fi=vertaisarvioitu|en=peerReviewed
Algorithm Engineering for Realistic Journey Planning in Transportation Networks
Diese Dissertation beschĂ€ftigt sich mit der Routenplanung in Transportnetzen. Es werden neue, effiziente algorithmische AnsĂ€tze zur Berechnung optimaler Verbindungen in öffentlichen Verkehrsnetzen, StraĂennetzen und multimodalen Netzen, die verschiedene Transportmodi miteinander verknĂŒpfen, eingefĂŒhrt. Im Fokus der Arbeit steht dabei die PraktikabilitĂ€t der AnsĂ€tze, was durch eine ausfĂŒhrliche experimentelle Evaluation belegt wird
Dispatching and Rescheduling Tasks and Their Interactions with Travel Demand and the Energy Domain: Models and Algorithms
Abstract The paper aims to provide an overview of the key factors to consider when performing reliable modelling of rail services. Given our underlying belief that to build a robust simulation environment a rail service cannot be considered an isolated system, also the connected systems, which influence and, in turn, are influenced by such services, must be properly modelled. For this purpose, an extensive overview of the rail simulation and optimisation models proposed in the literature is first provided. Rail simulation models are classified according to the level of detail implemented (microscopic, mesoscopic and macroscopic), the variables involved (deterministic and stochastic) and the processing techniques adopted (synchronous and asynchronous). By contrast, within rail optimisation models, both planning (timetabling) and management (rescheduling) phases are discussed. The main issues concerning the interaction of rail services with travel demand flows and the energy domain are also described. Finally, in an attempt to provide a comprehensive framework an overview of the main metaheuristic resolution techniques used in the planning and management phases is shown
Numerical and Evolutionary Optimization 2020
This book was established after the 8th International Workshop on Numerical and Evolutionary Optimization (NEO), representing a collection of papers on the intersection of the two research areas covered at this workshop: numerical optimization and evolutionary search techniques. While focusing on the design of fast and reliable methods lying across these two paradigms, the resulting techniques are strongly applicable to a broad class of real-world problems, such as pattern recognition, routing, energy, lines of production, prediction, and modeling, among others. This volume is intended to serve as a useful reference for mathematicians, engineers, and computer scientists to explore current issues and solutions emerging from these mathematical and computational methods and their applications
Evolution through reputation: noise-resistant selection in evolutionary multi-agent systems
Little attention has been paid, in depth, to the relationship between fitness evaluation
in evolutionary algorithms and reputation mechanisms in multi-agent systems, but if
these could be related it opens the way for implementation of distributed evolutionary
systems via multi-agent architectures. Our investigation concentrates on the effectiveness
with which social selection, in the form of reputation, can replace direct
fitness observation as the selection bias in an evolutionary multi-agent system. We do
this in two stages: In the first, we implement a peer-to-peer, adaptive Genetic Algorithm
(GA), in which agents act as individual GAs that, in turn, evolve dynamically
themselves in real-time, using the traditional evolutionary operators of fitness-based
selection, crossover and mutation. In the second stage, we replace the fitness-based
selection operator with a reputation-based one, in which agents choose their mates
based on the collective past experiences of themselves and their peers. Our investigation
shows that this simple model of distributed reputation can be successful as the
evolutionary drive in such a system, exhibiting practically identical performance and
scalability to direct fitness observation. Further, we discuss the effect of noise (in the
form of âdefectiveâ agents) in both models. We show that the reputation-based model
is significantly better at identifying the defective agents, thus showing an increased
level of resistance to noise
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