Protein modeling research is driven by the need to aid wet laboratories in characterizing equilibrium protein dynamics. In principle, structural excursions of a protein can be directly observed via simulation of its dynamics, but the disparate temporal scales involved in such excursions make this approach computationally impractical. Aug 20, 2020 · Domain Separation in Density Functional Theory Probing Competing Decay Pathways in Malonaldehyde with Transient X-Ray Absorption Hole-hole Tamm-Dancoff-approximated density functional theory: a highly efficient electronic structure method incorporating dynamic and static correlation The findings from the multi-objective optimization were used to provide with a general strategy for achieving desirable operation points, resulting in a productivity ranging between 0.61 and 0.75 kg Eu/(m3 column h) and a pool concentration between 0.52 and 0.79 kg Eu/m3, while maintaining a purity above 99% and never falling below an 80% yield ... Mar 22, 2013 · A multi-objective evolutionary algorithm is used with one (PA) or two (P&CC) objectives: to reflect that selection is stronger on network performance than connection costs, the P&CC cost objective affects selection probabilistically only 25 per cent of the time, although the results are robust to substantial changes to this value (§4). Two ... Sep 30, 2016 · Furthermore, ProBMoTd resolves the parametric uncertainty by using standard multi-objective optimization methods, as the ones used by optimization-based approaches 8,11,13. It fits the values of ... Multi-objective optimization is an integral part of optimization activities and has a tremendous practical importance, since almost all real-world optimization problems are ideally suited to be modeled using multiple conflicting objectives. Bayesian optimization is a principled approach for globally optimizing expensive, black-box functions by using a surrogate model of the objective. However, each step of Bayesian optimization involves solving an inner optimization problem, in which we maximize an acquisition function derived from the surrogate model to decide where to query next. Sep 26, 2010 · In this thesis we have the general objective of adapting the Pittsburgh model to handle successfully these kind of datasets. This general objective is split in three: (1) Improving the generalization capacity of the model, (2) Reducing the run-time of the system and (3) Proposing representations for real-valued attributes. Predictive Entropy Search for Multi-Objective Bayesian Optimization Conference. Proceedings of the 33rd International Conference on Machine Learning (ICML), 2016, (arXiv:1511.05467 [stat.ML]). Abstract | Links | BibTeX Mar 22, 2013 · A multi-objective evolutionary algorithm is used with one (PA) or two (P&CC) objectives: to reflect that selection is stronger on network performance than connection costs, the P&CC cost objective affects selection probabilistically only 25 per cent of the time, although the results are robust to substantial changes to this value (§4). Two ... A multi-objective optimisation approach accurately resolves protein domain architectures Article (PDF Available) in Bioinformatics 32(3) · October 2015 with 31 Reads How we measure 'reads' Aug 20, 2020 · Domain Separation in Density Functional Theory Probing Competing Decay Pathways in Malonaldehyde with Transient X-Ray Absorption Hole-hole Tamm-Dancoff-approximated density functional theory: a highly efficient electronic structure method incorporating dynamic and static correlation This thesis introduces a novel approach to rapid Design Space Exploration (DSE) and presents a formalized High Level Synthesis (HLS) design flow with multi parametric optimization objective using the same design space exploration approach. The proposed approach resolves Several recent methods have proposed to evaluate domains in the context of other domain predictions, using higher-order scoring systems and additional information; these include a Markov model of sequential domain co-occurrence (Coin et al., 2003), a protein clustering approach based on domain architectures that can discover missing domains (Beaussart et al., 2007), a domain prediction filter based on pairwise co-occurrence (CODD, Terrapon et al., 2009), and a multi-objective optimization ... A Switched Systems Approach to Consensus of a Distributed Multi-Agent System with Intermittent Communication (I) Zegers, Federico: University of Florida: Chen, Hsi-Yuan: University of Florida: Deptula, Patryk: University of Florida: Dixon, Warren E. University of Florida Multi-objective optimization is an integral part of optimization activities and has a tremendous practical importance, since almost all real-world optimization problems are ideally suited to be modeled using multiple conflicting objectives. lenging nonconvex optimization domains is demonstrated in a series of simulation studies in Sec. 4.2. The remainder of this paper is organized as follows: In Sec. 2, we review the existing literature addressing the problem of geo-metric accuracy optimization in AM processes and the multi-objective optimization techniques. In Sec. 3, the proposed Feb 01, 2016 · A multi-objective optimization approach accurately resolves protein domain architectures Bernardes, J.S.; Vieira, F.R.J.; Zaverucha, G.; Carbone, A. 2016-02-01 00:00:00 Motivation: Given a protein sequence and a number of potential domains matching it, what are the domain content and the most likely domain architecture for the sequence? This problem is of fundamental importance in protein annotation, constituting one of the main steps of all predictive annotation strategies. Multi-Objective Self-Paced Learning / 1802 Hao Li, Maoguo Gong, Deyu Meng, Qiguang Miao. Scalable Sequential Spectral Clustering / 1809 Yeqing Li, Junzhou Huang, Wei Liu. Towards Safe Semi-Supervised Learning for Multivariate Performance Measures / 1816 Yu-Feng Li, James T. Kwok, Zhi-Hua Zhou This page is the first in a series of pieces of long-form content highlighting the van der Schaar Lab’s primary “research pillars.” As a group that develops new and powerful machine learning tools and techniques for healthcare, our aim is to ensure that these are put to practical use, and that this is done in the service of a longer-term vision. Multi-objective Optimization I Multi-objective optimization (MOO) is the optimization of conﬂicting objectives. I In some problems, it is possible to ﬁnd a way of combining the objectives into a single objective. I But, in some other problems, it is not possible to do so. I Sometimes the differences are qualitative and the relative Apr 28, 2016 · Author Summary This paper provides an overview of recent advancements in computational methods for modeling macromolecular structure and dynamics. The focus is on methods aimed at providing efficient representations of macromolecular structure spaces for the purpose of characterizing equilibrium dynamics. The overview is meant to provide a summary of state-of-the-art capabilities of these ... Jul 05, 2004 · Multi-objective evolutionary triclustering with constraints of time-series gene expression data Integrated Computer-Aided Engineering, Vol. 26, No. 4 Coordination Analysis of Gene Expression Points to the Relative Impact of Different Regulators During Endoplasmic Reticulum Stress A Switched Systems Approach to Consensus of a Distributed Multi-Agent System with Intermittent Communication (I) Zegers, Federico: University of Florida: Chen, Hsi-Yuan: University of Florida: Deptula, Patryk: University of Florida: Dixon, Warren E. University of Florida Jul 05, 2004 · Multi-objective evolutionary triclustering with constraints of time-series gene expression data Integrated Computer-Aided Engineering, Vol. 26, No. 4 Coordination Analysis of Gene Expression Points to the Relative Impact of Different Regulators During Endoplasmic Reticulum Stress Feb 07, 2018 · The approach was tested on six open source programs and compared against existing mono-objective and multi-objective approaches, as well as a manual refactoring approach. The majority of the suggested refactorings were considered by the users to be feasible, efficient in terms of improving quality of the design and to make sense. The objective being to schedule jobs in a sequence-dependent or non-sequence-dependent setup environment in order to maximize the volume of production while minimizing penalties such as tardiness. Satellite communication scheduling for the NASA Deep Space Network was shown to benefit from genetic algorithms. Multi-class protein fold classification using a new ensemble machine learning approach: mlpy: Machine learning python: A machine learning framework for network anomaly detection using SVM and GA: Image analysis and machine learning applied to breast cancer diagnosis and prognosis A multi-domain and multi-step topology optimization approach has been developed to address a wide range of structural design problems with manufacturability and other application concerns. The potential applications have been demonstrated in our previous work [1, 2]. In this paper, we try to extend this method for vehicle crash design problem. Loop is the open research network that increases the discoverability and impact of researchers and their work. Loop enables you to stay up-to-date with the latest discoveries and news, connect with researchers and form new collaborations. We deal with the problem of protein superfamily classification in which the family membership of newly discovered amino acid sequence is predicted. Correct prediction is a matter of great concern for the researchers and drug analyst which helps them in discovery of new drugs. As this problem falls broadly under the category of pattern classification problem, we have made all efforts to ... Bernardes JS, Vieira FR, Zaverucha G, Carbone A (2016) A multi-objective optimization approach accurately resolves protein domain architectures. Bioinformatics 32(3):345–353 CrossRef Google Scholar 15. Using computational tools, fast and accurate predictions of building performance are increasingly possible. In parallel, the expectations of a high-performance building have been rising in contemporary architecture, as designers must synthesize many inputs to arrive at a design that fulfills a wide range of requirements. ESTECO’s integration platform for multi-objective and multi-disciplinary optimization offers a seamless coupling with third party engineering tools, enables the automation of the design simulation process and facilitates analytic decision making. In this paper, we use and compare, for the first time, a set of representative multi-objective optimization algorithms applied to solve complex molecular docking problems. The approach followed is focused on optimizing the intermolecular and intramolecular energies as two main objectives to minimize. High-quality individualized prediction based on multi-objective optimization can help a company to direct a message to a particular individual, while the results of a global symbolic regression-based approach may help large marketing campaigns or big changes in policies, cost structures and/or product offerings. Aug 08, 2003 · "Multiobjective optimization allows a degree of freedom, which is lacking in mono-objective optimization. … The book is accessible to the novice and expert … and can be used by students, engineers and scientists working in aerospace, automotive, and mechanical and civil engineering." (Stefan Jendo, Zentralblatt MATH, Vol. 1103 (5), 2007) Identifying compound-protein interactions is one of the essential challenges in drug discovery. We developed MONN, a multi-objective neural network, which not only accurately predicts the binding affinities but also successfully captures the non-covalent interactions between compounds and proteins. MONN can prove to be a useful tool in exploring compound-protein interactions. The architectures reported in Plasmobase were obtained by applying DAMA, a multi-objective approach that accurately resolves protein domain architectures. Domain annotation in Plasmobase is realised with CLADE (Closer sequences for Annotations Directed by Evolution), an annotation tool based on a multi-source strategy where several hundreds ... Frequent keywords. The top 50 common keywords and their frequency. The average reviewer ratings and the frequency of keywords indicate that to maximize your chance to get higher ratings would be using the keywords such as compositionality, deep learning theory, or gradient descent. At the same time, many hybrid (multi-paradigm) approaches such as neuroevolution, deep learning, and genetic fuzzy systems, are employed to obtain accurate and efficient intelligent systems.This special session is concerned with novel nature-inspired approaches to design, evolution, and optimization of all types of intelligent systems.