AIMC Topic: Benchmarking
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Predicting protein conformational changes for unbound and homology docking: learning from intrinsic and induced flexibility.
Proteins
Dec 5, 2016
Predicting protein conformational changes from unbound structures or even homology models to bound structures remains a critical challenge for protein docking. Here we present a study directly addressing the challenge by reducing the dimensionality a...
Multilevel Weighted Support Vector Machine for Classification on Healthcare Data with Missing Values.
PloS one
May 19, 2016
This work is motivated by the needs of predictive analytics on healthcare data as represented by Electronic Medical Records. Such data is invariably problematic: noisy, with missing entries, with imbalance in classes of interests, leading to serious ...
Inverse treatment planning for spinal robotic radiosurgery: an international multi-institutional benchmark trial.
Journal of applied clinical medical physics
May 8, 2016
Stereotactic radiosurgery (SRS) is the accurate, conformal delivery of high-dose radiation to well-defined targets while minimizing normal structure doses via steep dose gradients. While inverse treatment planning (ITP) with computerized optimization...
Enrichr: a comprehensive gene set enrichment analysis web server 2016 update.
Nucleic acids research
May 3, 2016
Enrichment analysis is a popular method for analyzing gene sets generated by genome-wide experiments. Here we present a significant update to one of the tools in this domain called Enrichr. Enrichr currently contains a large collection of diverse gen...
Recombination spot identification Based on gapped k-mers.
Scientific reports
Mar 31, 2016
Recombination is crucial for biological evolution, which provides many new combinations of genetic diversity. Accurate identification of recombination spots is useful for DNA function study. To improve the prediction accuracy, researchers have propos...
An Integrated Method Based on PSO and EDA for the Max-Cut Problem.
Computational intelligence and neuroscience
Feb 18, 2016
The max-cut problem is NP-hard combinatorial optimization problem with many real world applications. In this paper, we propose an integrated method based on particle swarm optimization and estimation of distribution algorithm (PSO-EDA) for solving th...
Self-Trained LMT for Semisupervised Learning.
Computational intelligence and neuroscience
Dec 29, 2015
The most important asset of semisupervised classification methods is the use of available unlabeled data combined with a clearly smaller set of labeled examples, so as to increase the classification accuracy compared with the default procedure of sup...
Particle Swarm Optimization with Double Learning Patterns.
Computational intelligence and neuroscience
Dec 27, 2015
Particle Swarm Optimization (PSO) is an effective tool in solving optimization problems. However, PSO usually suffers from the premature convergence due to the quick losing of the swarm diversity. In this paper, we first analyze the motion behavior o...
Budget constrained non-monotonic feature selection.
Neural networks : the official journal of the International Neural Network Society
Sep 4, 2015
Feature selection is an important problem in machine learning and data mining. We consider the problem of selecting features under the budget constraint on the feature subset size. Traditional feature selection methods suffer from the "monotonic" pro...
New enhanced artificial bee colony (JA-ABC5) algorithm with application for reactive power optimization.
TheScientificWorldJournal
Mar 23, 2015
The standard artificial bee colony (ABC) algorithm involves exploration and exploitation processes which need to be balanced for enhanced performance. This paper proposes a new modified ABC algorithm named JA-ABC5 to enhance convergence speed and imp...
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