Performance Optimization of Multi-Core Grammatical Evolution Generated Parallel Recursive Programs

Chennupati, Gopinath; Azad, R. Muhammad Atif; Ryan, Conor

doi:10.1145/2739480.2754746

Cited by 11 publications

(6 citation statements)

References 22 publications

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“…Finally, we validate the use of machine learning techniques in decoy selection, while suggesting further research in this direction for advancing the state of decoy selection. In the future, we would like to investigate the use of other machine learning strategies and/or heuristics (similar to [60]) that initially predict the difficulty of a protein and use an ensemble of algorithms in predicting the purity of the basins for the respective class of proteins.…”

Section: Resultsmentioning

confidence: 99%

Decoy selection for protein structure prediction via extreme gradient boosting and ranking

et al. 2020

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Background Identifying one or more biologically-active/native decoys from millions of non-native decoys is one of the major challenges in computational structural biology. The extreme lack of balance in positive and negative samples (native and non-native decoys) in a decoy set makes the problem even more complicated. Consensus methods show varied success in handling the challenge of decoy selection despite some issues associated with clustering large decoy sets and decoy sets that do not show much structural similarity. Recent investigations into energy landscape-based decoy selection approaches show promises. However, lack of generalization over varied test cases remains a bottleneck for these methods. Results We propose a novel decoy selection method, ML-Select, a machine learning framework that exploits the energy landscape associated with the structure space probed through a template-free decoy generation. The proposed method outperforms both clustering and energy ranking-based methods, all the while consistently offering better performance on varied test-cases. Moreover, ML-Select shows promising results even for the decoy sets consisting of mostly low-quality decoys. Conclusions ML-Select is a useful method for decoy selection. This work suggests further research in finding more effective ways to adopt machine learning frameworks in achieving robust performance for decoy selection in template-free protein structure prediction.

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Section: Resultsmentioning

confidence: 99%

Decoy selection for protein structure prediction via extreme gradient boosting and ranking

et al. 2020

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“…The line of inquiry pursued in this paper presents a promising direction for advancing decoy selection. We further explore other methods ([74,75]) to improve the decoy selection.…”

Section: Resultsmentioning

confidence: 99%

Unsupervised and Supervised Learning over the Energy Landscape for Protein Decoy Selection

et al. 2019

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The energy landscape that organizes microstates of a molecular system and governs the underlying molecular dynamics exposes the relationship between molecular form/structure, changes to form, and biological activity or function in the cell. However, several challenges stand in the way of leveraging energy landscapes for relating structure and structural dynamics to function. Energy landscapes are high-dimensional, multi-modal, and often overly-rugged. Deep wells or basins in them do not always correspond to stable structural states but are instead the result of inherent inaccuracies in semi-empirical molecular energy functions. Due to these challenges, energetics is typically ignored in computational approaches addressing long-standing central questions in computational biology, such as protein decoy selection. In the latter, the goal is to determine over a possibly large number of computationally-generated three-dimensional structures of a protein those structures that are biologically-active/native. In recent work, we have recast our attention on the protein energy landscape and its role in helping us to advance decoy selection. Here, we summarize some of our successes so far in this direction via unsupervised learning. More importantly, we further advance the argument that the energy landscape holds valuable information to aid and advance the state of protein decoy selection via novel machine learning methodologies that leverage supervised learning. Our focus in this article is on decoy selection for the purpose of a rigorous, quantitative evaluation of how leveraging protein energy landscapes advances an important problem in protein modeling. However, the ideas and concepts presented here are generally useful to make discoveries in studies aiming to relate molecular structure and structural dynamics to function.

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“…Moreover, we further want to employ unsupervised methods such as the community detection strategies [35] in order to find the clusters of programs and separate the programs that are abusive. Another interesting unsupervised direction will be to incorporate large scale nonnegative low rank representations [36] of programs graphs in order to detect the outliers, application of heuristics [37] in order to automatically generate the deep network architectures for abusive program detection.…”

Section: Discussionmentioning

confidence: 99%

Code Characterization With Graph Convolutions and Capsule Networks

et al. 2020

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We propose SiCaGCN, a learning system to predict the similarity of a given software code to a set of codes that are permitted to run on a computational resource, such as a supercomputer or a cloud server. This code characterization allows us to detect abusive codes. Our system relies on a structural analysis of the control-flow graph of the software codes and two different graph similarity measures: Graph Edit Distance (GED) and a singular values based metric. SiCaGCN combines elements of Graph Convolutional Neural Networks (GCN), Capsule networks, attention mechanism, and neural tensor networks. Our experimental results include a study of the trade-offs between the two similarity metrics and two variations of our learning networks, with and without the use of capsules. Our main findings are that the use of capsules reduces mean square error significantly for both similarity metrics. Use of capsules reduces the runtime to calculate the GED while increases the runtime of singular values calculation.

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Performance Optimization of Multi-Core Grammatical Evolution Generated Parallel Recursive Programs

Cited by 11 publications

References 22 publications

Decoy selection for protein structure prediction via extreme gradient boosting and ranking

Decoy selection for protein structure prediction via extreme gradient boosting and ranking

Unsupervised and Supervised Learning over the Energy Landscape for Protein Decoy Selection

Code Characterization With Graph Convolutions and Capsule Networks

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