scholarly journals Analysis of a complete DNA–protein affinity landscape

2009 ◽  
Vol 7 (44) ◽  
pp. 397-408 ◽  
Author(s):  
William Rowe ◽  
Mark Platt ◽  
David C. Wedge ◽  
Philip J. Day ◽  
Douglas B. Kell ◽  
...  
Keyword(s):  
Life Sciences ◽  
Fitness Landscapes ◽  
Local Optima ◽  
Binding Profile ◽  
Fine Grained ◽  
Protein Affinity ◽  
Experimental Variation ◽  
Sparse Samples ◽  
On Chip ◽  
Wide Sector

Properties of biological fitness landscapes are of interest to a wide sector of the life sciences, from ecology to genetics to synthetic biology. For biomolecular fitness landscapes, the information we currently possess comes primarily from two sources: sparse samples obtained from directed evolution experiments; and more fine-grained but less authentic information from ‘ in silico ’ models (such as NK -landscapes). Here we present the entire protein-binding profile of all variants of a nucleic acid oligomer 10 bases in length, which we have obtained experimentally by a series of highly parallel on-chip assays. The resulting complete landscape of sequence-binding pairs, comprising more than one million binding measurements in duplicate, has been analysed statistically using a number of metrics commonly applied to synthetic landscapes. These metrics show that the landscape is rugged, with many local optima, and that this arises from a combination of experimental variation and the natural structural properties of the oligonucleotides.

ShortPath: A Network-on-Chip Router with Fine-Grained Pipeline Bypassing

2016 ◽  
Vol 65 (10) ◽  
pp. 3136-3147 ◽  
Author(s):  
Anastasios Psarras ◽  
Ioannis Seitanidis ◽  
Chrysostomos Nicopoulos ◽  
Giorgos Dimitrakopoulos
Keyword(s):  
Network On Chip ◽  
Fine Grained ◽  
On Chip

A Strength Pareto Gravitational Search Algorithm for Multi-Objective Optimization Problems

2015 ◽  
Vol 29 (06) ◽  
pp. 1559010 ◽  
Author(s):  
Xiaohui Yuan ◽  
Zhihuan Chen ◽  
Yanbin Yuan ◽  
Yuehua Huang ◽  
Xiaopan Zhang
Keyword(s):  
Optimization Problems ◽  
Search Algorithm ◽  
Gravitational Search Algorithm ◽  
Population Diversity ◽  
Benchmark Problems ◽  
Multi Objective Optimization ◽  
Local Optima ◽  
Multi Objective ◽  
Fine Grained ◽  
Gravitational Search

A novel strength Pareto gravitational search algorithm (SPGSA) is proposed to solve multi-objective optimization problems. This SPGSA algorithm utilizes the strength Pareto concept to assign the fitness values for agents and uses a fine-grained elitism selection mechanism to keep the population diversity. Furthermore, the recombination operators are modeled in this approach to decrease the possibility of trapping in local optima. Experiments are conducted on a series of benchmark problems that are characterized by difficulties in local optimality, nonuniformity, and nonconvexity. The results show that the proposed SPGSA algorithm performs better in comparison with other related works. On the other hand, the effectiveness of two subtle means added to the GSA are verified, i.e. the fine-grained elitism selection and the use of SBX and PMO operators. Simulation results show that these measures not only improve the convergence ability of original GSA, but also preserve the population diversity adequately, which enables the SPGSA algorithm to have an excellent ability that keeps a desirable balance between the exploitation and exploration so as to accelerate the convergence speed to the true Pareto-optimal front.

scholarly journals Application of an Interpretable Classification Model on Early Folding Residues during Protein Folding

2018 ◽  
Author(s):  
Sebastian Bittrich ◽  
Marika Kaden ◽  
Christoph Leberecht ◽  
Florian Kaiser ◽  
Thomas Villmann ◽  
...  
Keyword(s):  
Machine Learning ◽  
Protein Folding ◽  
Learning Strategies ◽  
Life Sciences ◽  
Classification Model ◽  
Classification Problems ◽  
Hydrophobic Residues ◽  
Imbalanced Classification ◽  
Fine Grained ◽  
Generalized Matrix

AbstractBackgroundMachine learning strategies are prominent tools for data analysis. Especially in life sciences, they have become increasingly important to handle the growing datasets collected by the scientific community. Meanwhile, algorithms improve in performance, but also gain complexity, and tend to neglect interpretability and comprehensiveness of the resulting models.ResultsGeneralized Matrix Learning Vector Quantization (GMLVQ) is a supervised, prototype-based machine learning method and provides comprehensive visualization capabilities not present in other classifiers which allow for a fine-grained interpretation of the data. In contrast to commonly used machine learning strategies, GMLVQ is well-suited for imbalanced classification problems which are frequent in life sciences. We present a Weka plug-in implementing GMLVQ. The feasibility of GMLVQ is demonstrated on a dataset of Early Folding Residues (EFR) that have been shown to initiate and guide the protein folding process. Using 27 features, an area under the receiver operating characteristic of 76.6% was achieved which is comparable to other state-of-the-art classifiers.ConclusionsThe application on EFR prediction demonstrates how an easy interpretation of classification models can promote the comprehension of biological mechanisms. The results shed light on the special features of EFR which were reported as most influential for the classification: EFR are embedded in ordered secondary structure elements and they participate in networks of hydrophobic residues. Visualization capabilities of GMLVQ are presented as we demonstrate how to interpret the results.

scholarly journals Surfing on Fitness Landscapes: A Boost on Optimization by Fourier Surrogate Modeling

Entropy ◽  
2020 ◽  
Vol 22 (3) ◽  
pp. 285 ◽  
Author(s):  
Luca Manzoni ◽  
Daniele M. Papetti ◽  
Paolo Cazzaniga ◽  
Simone Spolaor ◽  
Giancarlo Mauri ◽  
...  
Keyword(s):  
Fitness Landscape ◽  
Optimization Problems ◽  
Partial Information ◽  
Fitness Function ◽  
Surrogate Modeling ◽  
Global Optimum ◽  
Fitness Landscapes ◽  
Low Frequencies ◽  
Local Optima ◽  
Self Tuning

Surfing in rough waters is not always as fun as wave riding the “big one”. Similarly, in optimization problems, fitness landscapes with a huge number of local optima make the search for the global optimum a hard and generally annoying game. Computational Intelligence optimization metaheuristics use a set of individuals that “surf” across the fitness landscape, sharing and exploiting pieces of information about local fitness values in a joint effort to find out the global optimum. In this context, we designed surF, a novel surrogate modeling technique that leverages the discrete Fourier transform to generate a smoother, and possibly easier to explore, fitness landscape. The rationale behind this idea is that filtering out the high frequencies of the fitness function and keeping only its partial information (i.e., the low frequencies) can actually be beneficial in the optimization process. We prove our theory by combining surF with a settings free variant of Particle Swarm Optimization (PSO) based on Fuzzy Logic, called Fuzzy Self-Tuning PSO. Specifically, we introduce a new algorithm, named F3ST-PSO, which performs a preliminary exploration on the surrogate model followed by a second optimization using the actual fitness function. We show that F3ST-PSO can lead to improved performances, notably using the same budget of fitness evaluations.

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