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5th Multi-Disciplinary International Workshop on Artificial Intelligence, MIWAI-2011

作者： Hota, Chittaranjan Ledda, Filippo Armano, Giuliano Dept Comp Sci BITS Pilani Hyderabad Campus Hyderabad Andhra Pradesh India Univ Cagliari Dept Elect & Comp Engn Cagliari Italy

ISBN: (纸本)9783642257247;9783642257254

This paper presents a novel approach called Resilient Voting Scheme (RVS), which combines different predictors (experts) with the goal of improving the overall accuracy. As combining multiple experts involves uncertainty and imprecise information, the proposed approach cancels out the impact of bad performers while computing a single collective prediction. RVS uses a genetic algorithm to assign a reliability to each expert, by using the Q3 measure as fitness function. A resilient voting is then used to improve the accuracy of the final prediction. RVS has been tested with well known datasets and has been compared with other state-of-the-art combination techniques (i.e., averaging and stacking). Experimental results demonstrate the validity of the approach.

关键词： Artificial Neural Networks Voting Resilient Protocols protein secondary structure prediction

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A hybrid genetic-neural system for predicting protein secondary structure

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BMC BIOINFORMATICS 2005年第Sup4期6卷 S3-S3页

作者： Armano, G Mancosu, G Milanesi, L Orro, A Saba, M Vargiu, E Univ Cagliari Dept Elect & Elect Engn I-09123 Cagliari Italy Shardna Life Sci I-09121 Cagliari Italy CNR Inst Biomed Technol I-20090 Segrate MI Italy

Background: Due to the strict relation between protein function and structure, the prediction of protein 3D-structure has become one of the most important tasks in bioinformatics and proteomics. In fact, notwithstanding the increase of experimental data on protein structures available in public databases, the gap between known sequences and known tertiary structures is constantly increasing. The need for automatic methods has brought the development of several prediction and modelling tools, but a general methodology able to solve the problem has not yet been devised, and most methodologies concentrate on the simplified task of predicting secondary structure. Results: In this paper we concentrate on the problem of predicting secondary structures by adopting a technology based on multiple experts. The system performs an overall processing based on two main steps: first, a "sequence-to-structure" prediction is enforced by resorting to a population of hybrid (genetic-neural) experts, and then a "structure-to-structure" prediction is performed by resorting to an artificial neural network. Experiments, performed on sequences taken from well-known protein databases, allowed to reach an accuracy of about 76%, which is comparable to those obtained by state-of-the-art predictors. Conclusion: The adoption of a hybrid technique, which encompasses genetic and neural technologies, has demonstrated to be a promising approach in the task of protein secondary structure prediction.

关键词： secondary structure protein structure prediction Multiple Expert protein secondary structure prediction Combination Manager

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HYBP_PSSP: a hybrid back propagation method for predicting protein secondary structure

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NEURAL COMPUTING & APPLICATIONS 2012年第2期21卷 337-349页

作者： Qu, Wu Yang, Bingru Jiang, Wei Wang, Lijun Univ Sci & Technol Beijing Sch Comp & Commun Engn Beijing 100083 Peoples R China

The prediction of secondary structure is an important topic in the field of bioinformatics, even if the methods have matured, and development of the algorithms is a far less active area than a decade ago. Accurate prediction is very useful to biologists in its own right, but it is worth pointing out that it is also an essential component of tertiary structure prediction, which in contrast is far from solved and continues to be a highly active area of research. In addition, sequence comparison methods have more recently incorporated local structure tracks. The extra information utilized by the new methods has led to considerable improvements in fold recognition and alignment accuracy. In this paper, a novel method for protein secondary structure prediction is presented. Using evolutionary information contained in amino acid's physicochemical properties, position-specific scoring matrix generated by PSI-BLAST and HMMER3 profiles as input to hybrid back propagation system, secondary structure can be predicted at significantly increased accuracy. Based on knowledge discovery theory based on inner cognitive mechanism (KDTICM) theory, we have constructed a compound pyramid model approach, which is composed of four layers of the intelligent interface and integrated in several ways, such as hybrid back propagation method (HBP), modified knowledge discovery in databases (KDD*), hybrid SVM method (HSVM) and so on. Experiments on three standard datasets (RS126, CB513 and CASP8) show that CPM is capable of producing the higher Q (3) and SOV scores than that achieved by existing widely used schemes such as PSIPRED, PHD, Predator, as well as previously developed prediction methods. On the RS126 and CB513 datasets, it achieves a Q (3) and SOV99 score are considerably higher than the best reported scores, respectively. It is also tested on target proteins of critical assessment of protein structure prediction experiment (CASP8) and achieves better results than the traditional metho

关键词： Intelligent prediction system protein secondary structure prediction Hybrid BP neural network Compound pyramid model

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MCP: A multi-component learning machine to predict protein secondary structure

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COMPUTERS IN BIOLOGY AND MEDICINE 2019年第0期110卷 144-155页

作者： Khalatbari, Leila Kangavari, M. R. Hosseini, Saeid Yin, Hongzhi Cheung, Ngai-Man Iran Univ Sci & Technol Sch Comp Engn Tehran Iran Singapore Univ Technol & Design ST Elect SUTD Cyber Secur Lab Singapore Singapore Univ Queensland Sch Informat Technol & Elect Engn Brisbane Qld Australia

The Gene or DNA sequence in every cell does not control genetic properties on its own;Rather, this is done through the translation of DNA into protein and subsequent formation of a certain 3D structure. The biological function of a protein is tightly connected to its specific 3D structure. prediction of the protein secondary structure is a crucial intermediate step towards elucidating its 3D structure and function. Traditional experimental methods for prediction of protein structure are expensive and time-consuming. Nevertheless, the average accuracy of the suggested solutions has hardly reached beyond 80%. The possible underlying reasons are the ambiguous sequence-structure relation, noise in input protein data, class imbalance, and the high dimensionality of the encoding schemes. Furthermore, we utilize a compound string dissimilarity measure to directly interpret protein sequence content and avoid information loss. In order to improve accuracy, we employ two different classifiers including support vector machine and fuzzy nearest neighbor and collectively aggregate the classification outcomes to infer the final protein structures. We conduct comprehensive experiments to compare our model with the current state-of-the-art approaches. The experimental results demonstrate that given a set of input sequences, our multi-component framework can accurately predict the protein structure. Nevertheless, the effectiveness of our unified model can be further enhanced through framework configuration.

关键词： protein secondary structure prediction Fuzzy k-nearest neighbor Support vector machine Ensemble prediction machine

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Flour Quality and Related Molecular Characterization of High Molecular Weight Glutenin Subunit Genes from Wild Emmer Wheat Accession TD-256

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JOURNAL OF AGRICULTURAL AND FOOD CHEMISTRY 2016年第24期64卷 5128-5136页

作者： Zhang, Da-Le He, Ting-Ting Liang, Hui-Hui Huang, Lu-Yu Su, Ya-Zhong Li, Yu-Ge Li, Suo-Ping Henan Univ Sch Life Sci Kaifeng 475001 Henan Peoples R China

To clarify the effect of high molecular weight glutenin subunit (HMW-GS) from wild emmer wheat on flour quality, which has the same mobility as that from common wheat, the composition and molecular characterization of HMW-GS from wild emmer wheat accession TD-256, as well as its flour quality, were intensively analyzed. It is found that the mobilities of Glu-A1 and Glu-B1 subunits from TD-256 are consistent with those of bread wheat cv. 'XiaoYan 6'. Nevertheless, dough theological properties of TD-256 reveal its poor flour quality. In the aspect of molecular structure from HMW-GS, only two conserved cysteine residues can be observed in the deduced protein sequence of 1Bx14* from TD-256, while most Glu-1Bx contain four conserved cysteine residues. In addition, as can be predicted from secondary structure, the quantity both of alpha-helixes and their amino acid residues of the subunits from TD-256 is fewer than those of common wheat. Though low molecular weight glutenin subunit (LMW-GS) and gliadin can also greatly influence flour quality, the protein structure of the HMW-GS revealed in this work can partly explain the poor flour quality of wild emmer accession TD-256.

关键词： Triticum dicoccoides high molecular weight glutenin subunit (HMW-GS) dough rheological properties gene cloning protein secondary structure prediction

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Distributed learning strategy based on chips for classification with large-scale dataset

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INTERNATIONAL JOURNAL OF PATTERN RECOGNITION AND ARTIFICIAL INTELLIGENCE 2007年第5期21卷 899-920页

作者： Yang, Bo Su, Xiaohong Wang, Yadong IBM Corp China Res Lab Beijing 100094 Peoples R China Sch Comp Sci & Technol Harbin Inst Technol Harbin 150001 Peoples R China

Learning with very large-scale datasets is always necessary when handling real problems using artificial neural networks. However, it is still an open question how to balance computing efficiency and learning stability, when traditional neural networks spend a large amount of running time and memory to solve a problem with large-scale learning dataset. In this paper, we report the. rst evaluation of neural network distributed-learning strategies in large-scale classification over protein secondary structure. Our accomplishments include: (1) an architecture analysis on distributed-learning, (2) the development of scalable distributed system for large-scale dataset classification, (3) the description of a novel distributed-learning strategy based on chips, (4) a theoretical analysis of distributed-learning strategies for structure-distributed and data-distributed, (5) an investigation and experimental evaluation of distributed-learning strategy based-on chips with respect to time complexity and their effect on the classification accuracy of artificial neural networks. It is demonstrated that the novel distributed-learning strategy is better-balanced in parallel computing efficiency and stability as compared with the previous algorithms. The application of the protein secondary structure prediction demonstrates that this method is feasible and effective in practical applications.

关键词： distributed learning strategy artificial neural network classification multiagent protein secondary structure prediction

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Deep learning model with ensemble techniques to compute the secondary structure of proteins

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JOURNAL OF SUPERCOMPUTING 2021年第5期77卷 5104-5119页

作者： AlGhamdi, Rayed Aziz, Azra Alshehri, Mohammed Pardasani, Kamal Raj Aziz, Tarique King Abdulaziz Univ Dept Informat Technol Fac Comp & IT Jeddah Saudi Arabia Indian Inst Technol Dept Comp Sci Madras Tamil Nadu India Majmaah Univ Coll Comp & Informat Sci Majmaah Saudi Arabia MANIT Dept Math Bioinformat & Comp Applicat Bhopal India Malaysian Inst Microelect Syst Technol Pk Malaysia Kuala Lumpur Malaysia

protein secondary structure is the local conformation assigned to protein sequences with the help of its three-dimensional structure. Assigning the local conformation to protein sequences requires much computational work. There exists a vast literature on the protein secondary structure prediction approaches (more than 20 techniques), but to date, none of the existing techniques is entirely accurate. Thus, there is an excellent room for developing new models of protein secondary structure prediction to address the issues of prediction accuracy. In the present study, ensemble techniques such as AdaBoost- and Bagging-based deep learning models are proposed to predict the protein secondary structure. The data from standard datasets, namely CB513, RS126, PTOP742, PSA472, and MANESH, have been used for training and testing purposes. These standard datasets possess less than 25% redundancy. The model is evaluated using performance measures: Q8 and Q3 cross-validation accuracy, class precision, class recall, kappa factor, and testing on a dataset that is not used for training purposes, i.e., blind test. The ensembling technique used along with variability in datasets can remove the bias of each dataset by balancing it and making the features more distinguishable, leading to the improvement in accuracy as compared to the conventional and existing techniques. The proposed model shows an average improvement of similar to 2% and similar to 3% accuracy over the existing methods in a blind test for Q8 and Q3 accuracy.

关键词： protein secondary structure prediction Ensemble techniques AdaBoost Bagging Machine learning Deep learning Supervised learning

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Applying hybrid reasoning to mine for associative features in biological data

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JOURNAL OF BIOMEDICAL INFORMATICS 2007年第3期40卷 203-220页

作者： Galitsky, Boris A. Kuznetsov, Sergey O. Vinogradov, Dmitry V. Univ London Birkbeck Coll Sch Comp & Informat Sci London WC1E 7HX England All Russian Inst Sci & Tech Informat Moscow 125190 Russia

We develop the means to mine for associative features in biological data. The hybrid reasoning schema for deterministic machine learning and its implementation via logic programming is presented. The methodology of mining for correlation between features is illustrated by the prediction tasks for protein secondary structure and phylogenetic profiles. The suggested methodology leads to a clearer approach to hierarchical classification of proteins and a novel way to represent evolutionary relationships. Comparative analysis of Jasmine and other statistical and deterministic systems (including Explanation-Based Learning and Inductive Logic Programming) are outlined. Advantages of using deterministic versus statistical data mining approaches for high-level exploration of correlation structure are analyzed. (C) 2006 Elsevier Inc. All rights reserved.

关键词： deterministic machine learning plausible reasoning causal link protein secondary structure prediction phylogenetic profile analysis

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Folding type specific secondary structure propensities of synonymous codons

IEEE TRANSACTIONS ON NANOBIOSCIENCE

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IEEE TRANSACTIONS ON NANOBIOSCIENCE 2003年第3期2卷 150-157页

作者： Gu, WJ Zhou, T Ma, JM Sun, X Lu, ZH SE Univ Key Lab Mol & Biomol Elect Minist Educ Nanjing 210096 Peoples R China

We have proposed new amino acid secondary structure propensities in proteins with different folding types based on synonymous codons. They have been derived from 200 all alpha, all beta, alpha/beta, and alpha + beta proteins of known structures and their coding genes. The secondary structure propensities of the same codon in gene coding for different folding type proteins are not the same. For instance, amino acid Ile coded by AUU is indifferent to form the alpha unit in the alpha + beta protein class, but it is a former and a breaker for the alpha unit in the all alpha protein class and the alpha/beta class, respectively. On the other hand, the secondary structure propensities of different synonymous codons in the coding genes with the same folding type are also not all the same. As an example, CGU, CGG, and AGA, which are synonymous codons of Arg, are preferential to form the alpha unit in all alpha proteins, while CGA is an alpha unit breaker and the other two synonymous codons, CGC and AGG, are indifferent to form or break the alpha unit. As a result, protein secondary structure information contained both in mRNA sequences and in amino acid sequences has been introduced in these codon-based amino acid secondary structure propensities. These codon-based amino acid secondary structure propensities are helpful to in vitro protein design and protein secondary structure prediction.

关键词： protein design protein folding type protein secondary structure prediction secondary structure propensity synonymous codon usage

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Predicting protein secondary structure using stochastic tree grammars

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MACHINE LEARNING 1997年第2-3期29卷 275-301页

作者： Abe, N Mamitsuka, H NEC Corp Ltd Real World Comp Partnership C&C Media Res Labs Theory NEC Lab Miyamae Ku Kawasaki Kanagawa 216 Japan

We propose a new method for predicting protein secondary structure of a given amino acid sequence, based on a training algorithm for the probability parameters of a stochastic tree grammar. In particular, we concentrate on the problem of predicting beta-sheet regions, which has previously been considered difficult because of the unbounded dependencies exhibited by sequences corresponding to beta-sheets. To cope with this difficulty, we use a new family of stochastic tree grammars, which we call Stochastic Ranked Node Rewriting Grammars, which are powerful enough to capture the type of dependencies exhibited by the sequences of P-sheet regions, such as the 'parallel' and 'anti-parallel' dependencies and their combinations. The training algorithm we use is an extension of the 'inside-outside' algorithm for stochastic context-bee grammars, but with a number of significant modifications. We applied our method on real data obtained from the HSSP database !Homology-derived secondary structure of proteins Ver 1.0) and the results were encouraging: Our method was able to predict roughly 75 percent of the beta-strands correctly in a systematic evaluation experiment, in which the test sequences not only have less than 25 percent identity to the training sequences, but are totally unrelated to them. This figure compares favorably to the predictive accuracy of the state-of-the-art prediction methods in the field, even though our experiment was on a restricted type of beta-sheet structures and the test was done on a relatively small data size. We also stress that our method can predict the structure as well as the location of beta-sheet regions, which was not possible by conventional methods for secondary structure prediction. Extended abstracts of parts of the work presented in this paper have appeared in (Abe & Mamitsuka, 1994) and (Mamitsuka & Abe, 1993).

关键词： stochastic tree grammars protein secondary structure prediction beta-sheets maximum likelihood estimation minimum description length principle unsupervised learning

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