Improving Performance of Protein Structure Similarity Searching by Distributing Computations in Hierarchical Multi-Agent System

Momot, Alina; Małysiak-Mrozek, Bożena; Kozielski, Stanisław; Hera, Łukasz; Górczyńska-Kosiorz, Sylwia; Momot, M.

doi:10.1007/978-3-642-16693-8_34

Cited by 12 publications

(3 citation statements)

References 13 publications

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“…For example, other effective machine learning algorithms, such as ensemble learning algorithms and deep learning algorithms, have recently showed they can achieve better performance than traditional algorithms (Mrozek et al, 2009a; Wei et al, 2017b, 2018a; Wang et al, 2018). On the other hand, feature representation learning has demonstrated that it can exploit more informative features and improve the performance in multiple bioinformatics problems (Mrozek et al, 2009b; Momot et al, 2010; Liu et al, 2015; Wei et al, 2018b, 2019; Tang et al, 2019).…”

Section: Discussionmentioning

confidence: 99%

k-Skip-n-Gram-RF: A Random Forest Based Method for Alzheimer's Disease Protein Identification

Liang

Liao

et al. 2019

Front. Genet.

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In this paper, a computational method based on machine learning technique for identifying Alzheimer's disease genes is proposed. Compared with most existing machine learning based methods, existing methods predict Alzheimer's disease genes by using structural magnetic resonance imaging (MRI) technique. Most methods have attained acceptable results, but the cost is expensive and time consuming. Thus, we proposed a computational method for identifying Alzheimer disease genes by use of the sequence information of proteins, and classify the feature vectors by random forest. In the proposed method, the gene protein information is extracted by adaptive k-skip-n-gram features. The proposed method can attain the accuracy to 85.5% on the selected UniProt dataset, which has been demonstrated by the experimental results.

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Section: Discussionmentioning

confidence: 99%

k-Skip-n-Gram-RF: A Random Forest Based Method for Alzheimer's Disease Protein Identification

Liang

Liao

et al. 2019

Front. Genet.

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“…An SVM is a supervised learning model related to learning algorithms and has achieved good performance in several bioinformatics (Momot et al, 2010; Cao et al, 2014; Ding et al, 2016; Li et al, 2016a; Wang et al, 2017b, 2018; Wei et al, 2017a,b, 2018c; Chen and Chuang, 2018; Liu et al, 2018c; Tang et al, 2018a; Shen et al, 2019; Zhu et al, 2019) and biomedicine (Zeng et al, 2018a; Zhang et al, 2018) studies. The dual-classification problem of an SVM can be broadly divided into three cases: linear separable, approximate linear separable, and non-linear separable.…”

Section: Methodsmentioning

confidence: 99%

Predicting Ion Channels Genes and Their Types With Machine Learning Techniques

et al. 2019

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Motivation: The number of ion channels is increasing rapidly. As many of them are associated with diseases, they are the targets of more than 700 drugs. The discovery of new ion channels is facilitated by computational methods that predict ion channels and their types from protein sequences. Methods: We used the SVMProt and the k-skip-n-gram methods to extract the feature vectors of ion channels, and obtained 188- and 400-dimensional features, respectively. The 188- and 400-dimensional features were combined to obtain 588-dimensional features. We then employed the maximum-relevance-maximum-distance method to reduce the dimensions of the 588-dimensional features. Finally, the support vector machine and random forest methods were used to build the prediction models to evaluate the classification effect. Results: Different methods were employed to extract various feature vectors, and after effective dimensionality reduction, different classifiers were used to classify the ion channels. We extracted the ion channel data from the Universal Protein Resource (UniProt, http://www.uniprot.org/ ) and Ligand-Gated Ion Channel databases ( http://www.ebi.ac.uk/compneur-srv/LGICdb/LGICdb.php ), and then verified the performance of the classifiers after screening. The findings of this study could inform the research and development of drugs.

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“…The second group of approaches consists of solutions that utilize farms of computers or virtual machines to perform protein structure comparison. Examples of such solutions are MAS4PSi [27], Cloud4Psi [28,34], and CloudPSR [32]. All of them rely on fold-based methods for 3D protein structure comparison.…”

Section: Scalable Solutions For 3d Protein Structure Similarity Searcmentioning

confidence: 99%

High-throughput and scalable protein function identification with Hadoop and Map-only pattern of the MapReduce processing model

Mrozek

Suwała

2018

Knowl Inf Syst

Self Cite

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Efficient computational solutions for identification of protein functions or finding structural homologs of proteins gain importance in the era of structural genomics and in the face of growing volumes of biological data. Structural alignments, which underlie these two processes, take a lot of time to complete, especially when performed for large collections of 3D protein structures. Fortunately, structural alignments can be carried out on well-separable and independent subsets of the whole macromolecular data repository, which perfectly fits the MapReduce processing paradigm of bringing computations to data. In this paper, we show how the protein function identification and finding structural homologs can be efficiently accelerated with the use of the MapReduce procedure executed on Hadoop cluster established in a virtualized compute environment or a private cloud. For this purpose, we propose Map-only processing pattern of the MapReduce procedure, which is formally defined in this paper. The solution that we show joins advantages of performing computations in small virtualized compute environments with large-scale computations in public clouds, thus allowing to perform structural alignments for a number of usage scenarios, including comparison of pairs of 3D protein structures during evaluation of predicted protein models, one-to-many comparisons while identifying possible functions of the given structure, or all-to-all alignments while investigating the divergence between known protein structures and classifying proteins by their fold. In this paper, we also present results of performance tests when scaling up nodes of the Hadoop cluster and increasing the degree of parallelism with the intention of improving efficiency of the computations. Keywords Bioinformatics • Big data • Proteins • Scalable computations and MapReduce • 3D protein structures • Cloud computing 148 D. Mrozek et al.

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Improving Performance of Protein Structure Similarity Searching by Distributing Computations in Hierarchical Multi-Agent System

Cited by 12 publications

References 13 publications

k-Skip-n-Gram-RF: A Random Forest Based Method for Alzheimer's Disease Protein Identification

k-Skip-n-Gram-RF: A Random Forest Based Method for Alzheimer's Disease Protein Identification

Predicting Ion Channels Genes and Their Types With Machine Learning Techniques

High-throughput and scalable protein function identification with Hadoop and Map-only pattern of the MapReduce processing model

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