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11th ACM Conference on Bioinformatics, Computational Biology, and Health Informatics (ACM-BCB)

作者： Krieger, Spencer Kececioglu, John Univ Arizona Comp Sci Tucson AZ 85721 USA

ISBN: (纸本)9781450379649

protein secondary structure prediction is a fundamental task in computational biology, basic to many bioinformatics workflows, with a diverse collection of tools currently available. An approach from machine learning with the potential to capitalize on such a collection is ensemble prediction, which runs multiple predictors and combines their predictions into one, output by the ensemble. We conduct a thorough study of seven different approaches to ensemble secondary structure prediction, several of which are novel, and show we can indeed obtain an ensemble method that significantly exceeds the accuracy of individual state-of-the-art tools. The best approaches build on a recent technique known as feature-based accuracy estimation, which estimates the unknown true accuracy of a prediction, here using features of both the prediction output and the internal state of the prediction method. In particular, a hybrid approach to ensemble prediction that leverages accuracy estimation is now the most accurate method currently available: on average over standard CASP and PDB benchmarks, it exceeds the state-of-the-artQ3 accuracy for 3-state prediction by nearly 4%, and exceeds the Q8 accuracy for 8-state prediction by more than 8%. A preliminary implementation of our approach to ensemble protein secondary structure prediction, in a new tool we call Ssylla, is available free for non-commercial use at ***.

关键词： protein secondary structure prediction ensemble methods feature-based accuracy estimation method hybridization

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A two-stage classifier for protein β-turn prediction using support vector machines

A two-stage classifier for protein β-turn prediction using ...

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IEEE International Conference on Granular Computing

作者： Chiu, Hua-Sheng Lin, Hsin-Nan Lo, Allan Sung, Ting-Yi Hsu, Wen-Lian Acad Sinica Inst Informat Sci Bioinformat Lab Taipei 115 Taiwan

ISBN: (纸本)142440133X

beta-turns play an important role in protein structures not only because of their sheer abundance, which is estimated to be approximately 25% of all protein residues, but also because of their significance in high-order structures of proteins. In this study, we introduce a new method of beta-turn prediction that uses a two-stage classification scheme and an integrated framework for input features. Ten-fold cross validation based on a benchmark dataset of 426 non-homologue protein chains is used to evaluate our method's performance. The experimental results demonstrate that it achieves substantial improvements over BetaTurn, the current best method. The prediction accuracy, Q(total), and the Matthews correlation coefficient (MCC) of our approach are 79% and 0.47 respectively, compared to 77% and 0.45 respectively for BetaTurn.

关键词： beta-turn prediction classification protein secondary structure prediction support vector machines

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Data Mining Techniques to Predict protein secondary structures

Data Mining Techniques to Predict Protein Secondary Structur...

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5th International Conference on Modeling, Simulation and Applied Optimization (ICMSAO)

作者： Fayech, Sond Es Essoussi, Nadia Limam, Mohamed Univ Tunis LARODEC ISG Tunis Tunisia

ISBN: (纸本)9781467358149;9781467358125

protein secondary structure prediction is a key step in prediction of protein tertiary structure. There have emerged many methods based on machine learning techniques, such as neural networks (NN) and support vector machines (SVM), to focus on the prediction of the secondary structures. In this paper a new method, DM-pred, was proposed based on a protein clustering method to detect homologous sequences, a sequential pattern mining method to detect frequent patterns, features extraction and quantification approaches to prepare features and SVM method to predict structures. When tested on the most popular secondary structure datasets, DM-pred achieved a Q3 accuracy of 78.20% and a SOV of 76.49% which illustrates that it is one of the top range methods for protein secondary structure prediction.

关键词： protein secondary structure prediction clustering sequential pattern mining features SVM

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An Improved CBA prediction Algorithm in Compound Pyramid Model

An Improved CBA Prediction Algorithm in Compound Pyramid Mod...

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21st Chinese Control and Decision Conference

作者： Zhou Zhun Yang Bingru Hou Wei Univ Sci & Technol Beijing Informat Engn Sch Beijing 100083 Peoples R China

ISBN: (纸本)9781424427239

As one of KDTICM[8] theory researches, this paper propose an improved algorithm -- CBA, which is based on KDD* model and combined with KAAPRO method, for protein secondary structure prediction problem. Further, multi-layer systematic prediction model--Compound Pyramid Model, is proposed. The kernel of this model is CBA which is a classic association rules analysis algorithm. Domain knowledge is used through the model, and the phy-chemical attributes is chosen by Causal Cellular Automation. In experiment, the proteins bias alpha/beta structure are precisely predicted. The structures of amino acids, whose structure are obscure, are predicted well by the improved CBA. Finally, the result of this model is satisfied.

关键词： Association Rule protein secondary structure prediction Compound Pyramid Model CBA algorithm

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Convolutional Neural Networks in Combination with Support Vector Machines for Complex Sequential Data Classification 27th

Convolutional Neural Networks in Combination with Support Ve...

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27th International Conference on Artificial Neural Networks (ICANN)

作者： Dionysiou, Antreas Agathocleous, Michalis Christodoulou, Chris Promponas, Vasilis Univ Cyprus Dept Comp Sci POB 20537 CY-1678 Nicosia Cyprus Univ Cyprus Dept Biol Sci POB 20537 CY-1678 Nicosia Cyprus

ISBN: (纸本)9783030014216

Trying to extract features from complex sequential data for classification and prediction problems is an extremely difficult task. Deep Machine Learning techniques, such as Convolutional Neural Networks (CNNs), have been exclusively designed to face this class of problems. Support Vector Machines (SVMs) are a powerful technique for general classification problems, regression, and outlier detection. In this paper we present the development and implementation of an innovative by design combination of CNNs with SVMs as a solution to the protein secondary structure prediction problem, with a novel two dimensional (2D) input representation method, where Multiple Sequence Alignment profile vectors are placed one under another. This 2D input is used to train the CNNs achieving preliminary results of 80.40% per residue accuracy (Q3), which are expected to increase with the use of larger training datasets and more sophisticated ensemble methods.

关键词： Convolutional Neural Networks Support Vector Machines Deep learning Machine learning Bioinformatics protein secondary structure prediction

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Optimized Local protein structure with Support Vector Machine to Predict protein secondary structure

Optimized Local Protein Structure with Support Vector Machin...

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3rd Conference of Knowledge Technology Week (KTW 2011)

作者： Chin, Yin Fai Hassan, Rohayanti Mohamad, Mohd Saberi Univ Teknologi Malaysia Artificial Intelligence & Bioinformat Res Grp Fac Comp Sci & Informat Syst Skudai 81310 Johor Malaysia

ISBN: (纸本)9783642328251

protein includes many substances, such as enzymes, hormones and antibodies that are necessary for the organisms. Living cells are controlled by proteins and genes that interact through complex molecular pathways to achieve a specific function. These proteins have different shapes and structures which distinct them from each other. By having unique structures, only proteins able to carried out their function efficiently. Therefore, determination of protein structure is fundamental for the understanding of the cell's functions. The function of a protein is also largely determined by its structure. The importance of understanding protein structure has fueled the development of protein structure databases and prediction tools. Computational methods which were able to predict protein structure for the determination of protein function efficiently and accurately are in high demand. In this study, local protein structure with Support Vector Machine is proposed to predict protein secondary structure.

关键词： Local protein structure Support Vector Machine protein secondary structure prediction

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PS8-Net: A Deep Convolutional Neural Network to Predict the Eight-State protein secondary structure

PS8-Net: A Deep Convolutional Neural Network to Predict the ...

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Conference on Digital Image Computing - Techniques and Applications (DICTA)

作者： Ratul, Aminur Rab Elahi, Maryam Tavakol Mozaffari, M. Hamed Lee, WonSook Univ Ottawa Sch Elect Engn & Comp Sci SITE Ottawa ON Canada

ISBN: (纸本)9781728191089

protein secondary structure is crucial to creating an information bridge between the primary and tertiary structures. Precise prediction of eight-state protein secondary structure (PSS) has been significantly utilized in the structural and functional analysis of proteins. Deep learning techniques have been recently applied in this area and raised the eight-state (Q8) protein secondary structure prediction accuracy remarkably. Nevertheless, from a theoretical standpoint, there are still many rooms for improvement, specifically in the eight-state PSS prediction. In this study, we have presented a new deep convolutional neural network called PS8-Net, to enhance the accuracy of eight-class PSS prediction. The input of this architecture is a carefully constructed feature matrix from the proteins sequence features and profile features. We introduce a new PS8 module with skip connection to extracting the long-term inter-dependencies from higher layers, obtaining local contexts in earlier layers, and achieving global information during secondary structure prediction. This architecture enables the efficient processing of local and global interdependencies between amino acids to make an accurate prediction of each class. To the best of our knowledge, our proposed PS8-Net experiment results demonstrate that it outperforms all the state-of-the-art methods on the benchmark CullPdb6133, CB513, CASP10, and CASP11 datasets.

关键词： Deep Convolutional Neural Network protein secondary structure prediction Bioinformatics Skip Connection

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Synonymous codon usage in genes coding for proteins with different folding types

Synonymous codon usage in genes coding for proteins with dif...

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24th Annual International Conference of the Engineering-in-Medicine-and-Biology-Society/Annual Fall Meeting of the Biomedical-Engineering-Society (EMBS 2002 BMES)

作者： Gu, WJ Ma, JM Zhou, T Sun, X Lu, ZH Southeast Univ Chien Shiung Wu Lab Nanjing 210096 Jiangsu Prov Peoples R China

ISBN: (纸本)0780376129

195 genes coding for proteins in four different folding types have been analyzed in terms of variance analysis, in which there are 50 genes coding for all alpha proteins, 66 entries for all beta proteins, 37 entries for alpha+beta proteins and 42 entries for alpha/beta proteins. We have observed that codon usage in different gene classes coding for differently folded proteins is significantly different. Similar to amino acid propensities in different protein classes, folding type specific proteins have specific codon usage patterns. With the help of codon usage pattern, we can improve the accuracy of prediction of protein folding type and further prediction of protein secondary structure.

关键词： synonymous codon usage variance analysis protein folding type protein secondary structure prediction

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SOV_refine: A further refined definition of segment overlap score and its significance for protein structure similarity

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SOURCE CODE FOR BIOLOGY AND MEDICINE 2018年第1期13卷 1-10页

作者： Liu, Tong Wang, Zheng Univ Miami Dept Comp Sci 1365 Mem Dr Coral Gables FL 33124 USA

Background: The segment overlap score (SOV) has been used to evaluate the predicted protein secondary structures, a sequence composed of helix (H), strand (E), and coil (C), by comparing it with the native or reference secondary structures, another sequence of H, E, and C. SOV's advantage is that it can consider the size of continuous overlapping segments and assign extra allowance to longer continuous overlapping segments instead of only judging from the percentage of overlapping individual positions as Q3 score does. However, we have found a drawback from its previous definition, that is, it cannot ensure increasing allowance assignment when more residues in a segment are further predicted accurately. Results: A new way of assigning allowance has been designed, which keeps all the advantages of the previous SOV score definitions and ensures that the amount of allowance assigned is incremental when more elements in a segment are predicted accurately. Furthermore, our improved SOV has achieved a higher correlation with the quality of protein models measured by GDT-TS score and TM-score, indicating its better abilities to evaluate tertiary structure quality at the secondary structure level. We analyzed the statistical significance of SOV scores and found the threshold values for distinguishing two protein structures (SOV_refine > 0.19) and indicating whether two proteins are under the same CATH fold (SOV_refine > 0.94 and > 0.90 for three-and eight-state secondary structures respectively). We provided another two example applications, which are when used as a machine learning feature for protein model quality assessment and comparing different definitions of topologically associating domains. We proved that our newly defined SOV score resulted in better performance. Conclusions: The SOV score can be widely used in bioinformatics research and other fields that need to compare two sequences of letters in which continuous segments have important meanings. We also genera

关键词： Segment overlap score SOV score protein secondary structure prediction Assessment of protein secondary structure predictions protein structure similarity Similarity of segmented biological sequences Comparing different definitions of topologically associating domains

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Training Bidirectional Recurrent Neural Network Architectures with the Scaled Conjugate Gradient Algorithm 1

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25th International Conference on Artificial Neural Networks (ICANN)

作者： Agathocleous, Michalis Christodoulou, Chris Promponas, Vasilis Kountouris, Petros Vassiliades, Vassilis Univ Cyprus Dept Comp Sci POB 20537 CY-1678 Nicosia Cyprus Univ Cyprus Dept Biol Sci POB 20537 CY-1678 Nicosia Cyprus Cyprus Inst Neurol & Genet Nicosia Cyprus Inria Nancy Grand Est Villers Les Nancy France

ISBN: (数字)9783319447780

ISBN: (纸本)9783319447780;9783319447773

predictions on sequential data, when both the upstream and downstream information is important, is a difficult and challenging task. The Bidirectional Recurrent Neural Network (BRNN) architecture has been designed to deal with this class of problems. In this paper, we present the development and implementation of the Scaled Conjugate Gradient (SCG) learning algorithm for BRNN architectures. The model has been tested on the protein secondary structure prediction (PSSP) and Transmembrane protein Topology prediction problems (TMPTP). Our method currently achieves preliminary results close to 73% correct predictions for the PSSP problem and close to 79% for the TMPTP problem, which are expected to increase with larger datasets, external rules, ensemble methods and filtering techniques. Importantly, the SCG algorithm is training the BRNN architecture approximately 3 times faster than the Backpropagation Through Time (BPTT) algorithm.

关键词： Scaled Conjugate Gradient Bidirectional Recurrent Neural Networks protein secondary structure prediction Transmembrane protein Topology prediction Computational intelligence Bioinformatics

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