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44th IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)

作者： Moreno, Guillermo Romero Niranjan, Mahesan Prugel-Bennett, Adam Univ Southampton Sch Elect & Comp Sci Southampton Hants England

ISBN: (纸本)9781479981311

Deep learning, a powerful methodology for data-driven modelling, has been shown to be useful in tackling several problems in the biomedical domain. However, deep neural architectures lack interpretability of how predictions from them are made on any test input. While several approaches to "opening the black box" are being developed, their application to biological and medical data is very much as its infancy. Here, we consider the specific problem of protein secondary structure prediction using the techniques of saliency maps to explain decisions of a deep neural network. The analysis leads to two important observations: (a) one-hot-encoded amino-acids are irrelevant in the presence of PSSM values as extra features;and (b) in predicting alpha-helices at any position, amino-acids to the right are far more important than those to the left. The latter observation may have a biological basis relating to the synthesis of proteins by ribosome movement from left to right, sequentially adding amino-acids.

关键词： Interpretability Saliency Maps protein secondary structure prediction Convolutional Neural Networks

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Application of the Multi-modal Relevance Vector Machine to the Problem of protein secondary structure prediction

Application of the Multi-modal Relevance Vector Machine to t...

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7th International-Association-for-Pattern-Recognition (IAPR) International Conference on Pattern Recognition in Bioinformatics (PRIB)

作者： Razin, Nikolay Sungurov, Dmitry Mottl, Vadim Torshin, Ivan Sulimova, Valentina Seredin, Oleg Windridge, David Moscow Inst Phys & Technol Moscow Russia Russian Acad Sci Ctr Comp Moscow Russia Tula State Univ Tula Russia Univ Surrey Guildford Surrey England

ISBN: (纸本)9783642341236

The aim of the paper is to experimentally examine the plausibility of Relevance Vector Machines (RVM) for protein secondary structure prediction. We restrict our attention to detecting strands which represent an especially problematic element of the secondary structure. The commonly adopted local principle of secondary structure prediction is applied, which implies comparison of a sliding window in the given polypeptide chain with a number of reference amino-acid sequences cut out of the training proteins as benchmarks representing the classes of secondary structure. As distinct from the classical RVM, the novel version applied in this paper allows for selective combination of several tentative window comparison modalities. Experiments on the RS126 data set have shown its ability to essentially decrease the number of reference fragments in the resulting decision rule and to select a subset of the most appropriate comparison modalities within the given set of the tentative ones.

关键词： protein secondary structure prediction machine learning multi-modal relational pattern recognition Relevance Vector Machine controlled selectivity of reference objects and object-comparison modalities

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A Novel Group Template Pattern Classifiers (GTPCs) Method in protein secondary structure prediction 3

A Novel Group Template Pattern Classifiers (GTPCs) Method in...

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3rd IEEE International Conference on Computer and Communications (ICCC)

作者： Liu, Yihui Ma, Yuming Cheng, Jinyong Qilu Univ Technol Inst Intelligent Informat Proc Jinan Shandong Peoples R China

ISBN: (纸本)9781509063529

protein secondary structure prediction is a fundamental problem and is much more challenging. In this study we propose a novel method called Group Template Pattern Classifiers (GTPCs), which captures the long-range interactions of the whole protein at a great degree. Firstly the protein dataset is divided into several groups based on the length of proteins. The proteins with "similar" length in each group are used to build the prediction model. Then Wavelets are used to extract the features of PSSM profile, and characterize the changing information of PSSM profile. A new protein can be predicted by the corresponding GTPCs model based on the new protein length. Experiments are performed on 25PDB, CB513, CASP9, CASP10, CASP11, and CASP12 datasets, and the good performance of 86.38% 84.11%, 83.92%, 83.07%, 81.98%, and 82.35% is achieved respectively using 6 GTPCs model. Our experimental results are better than other state of the art methods.( http://*** /protein_GTPC)

关键词： protein secondary structure prediction Group Template Pattern Classifiers (GTPCs) wavelet feature extraction

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MMEC: Multi-Modal Ensemble Classifier for protein secondary structure prediction 19th

MMEC: Multi-Modal Ensemble Classifier for Protein Secondary ...

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19th International Conference on Computer Analysis of Images and Patterns (CAIP)

作者： de Oliveira, Gabriel Bianchin Pedrini, Helio Dias, Zanoni Univ Estadual Campinas Inst Comp Campinas SP Brazil

ISBN: (纸本)9783030891275;9783030891282

The protein secondary structure prediction is an important task with many applications, such as local folding analysis, tertiary structure prediction, and function classification. Driven by the recent success of multi-modal classifiers, new studies have been conducted using this type of method in other domains, for instance, biology and health care. In this work, we investigate the ensemble of three different classifiers for protein secondary structure prediction. Each classifier of our method deals with a transformation of the original data into a specific domain, such as image classification, natural language processing, and time series tasks. As a result, each classifier achieved competitive results compared to the literature, and the ensemble of the three different classifiers obtained 77.9% and 73.3% of Q8 accuracy on the CB6133 and CB513 datasets, surpassing state-of-the-art approaches in both scenarios.

关键词： Deep learning protein secondary structure prediction Multi-modal classification

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Hybrid SVM kernels for protein secondary structure prediction

Hybrid SVM kernels for protein secondary structure predictio...

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IEEE International Conference on Granular Computing

作者： Altun, Gulsah Hu, Hae-Jin Brinza, Dumitru Harrison, Robert W. Zelikovsky, Alex Pan, Yi

ISBN: (纸本)142440133X

The Support Vector Machine is a powerful methodology for solving problems in nonlinear classification, function estimation and density estimation. When data are not linearly separable, data are mapped to a high dimensional future space using a nonlinear function which can be computed through a positive definite kernel in the input space. Using a suitable kernel function for a particular problem and input data can change the prediction results remarkably and improve the accuracy. The goal of this work is to find the best kernel functions that can be applied to different types of data and problems. In this paper, we propose two hybrid kernels SVMSM+RBF and SVMEDIT+RBF. SVMSM+RBF is designed by combining the best performed RBF kernel with substitution matrix (SM) based kernel developed by Vanschoenwinkel and Manderick. SVMEDIT+RBF kernel combines the RBF kernel and the edit kernel devised by Li and Jiang. We tested these two hybrid kernels on one of the widely studied problems in bioinformatics which is the protein secondary structure prediction problem. For the protein secondary structure problem, our results were 91% accuracy on HIE binary classifier.

关键词： kernel method protein secondary structure prediction support vector machine

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Ensemble of Bidirectional Recurrent Networks and Random Forests for protein secondary structure prediction 27

Ensemble of Bidirectional Recurrent Networks and Random Fore...

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27th International Conference on Systems, Signals and Image Processing (IWSSIP)

作者： de Oliveira, Gabriel Bianchin Pedrini, Helio Dias, Zanoni Univ Estadual Campinas Inst Comp BR-13083852 Campinas SP Brazil

ISBN: (数字)9781728175393

ISBN: (纸本)9781728175393

The prediction of secondary protein structures is one of the classic problems of bioinformatics and has several practical applications. In this work, we present an ensemble of bidirectional recurrent networks (capable of doing long-term analyses of amino acid sequences) with random forests (capable of doing local analyses). The fusion was performed using weights for each of the classifiers and classes, found by searching through genetic algorithms. To evaluate our method, we used the PDB, CB6133 and CB513 datasets. We achieved 73.1%, 59.1% and 55.8% Q8 accuracy on PDB, CB6133 and CB513 proteins, respectively, whereas 81.5% Q3 accuracy on PDB proteins using only amino acid sequence information. In order to compare our results against the literature, we also evaluated our method using amino acid sequence and sequence profile features, achieving 73.4% and 68.9% Q8 accuracy on CB6133 and CB513. Our method yielded good results when using only the amino acid sequence and presents competitive results compared to the literature when using amino acid sequence information and protein sequence similarity.

关键词： protein secondary structure prediction Bidirectional Recurrent Network Random Forest Ensemble

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The Relative Importance of Input Encoding and Learning Methodology on protein secondary structure prediction

The Relative Importance of Input Encoding and Learning Metho...

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作者： Arnshea Clayton Georgia State University

学位级别：硕士

In this thesis the relative importance of input encoding and learning algorithm on protein secondary structure prediction is explored. A novel input encoding, based on multidimensional scaling applied to a recently published amino acid substitution matrix, is developed and shown to be superior to an arbitrary input encoding. Both decimal valued and binary input encodings are compared. Two neural network learning algorithms, Resilient Propagation and Learning Vector Quantization, which have not previously been applied to the problem of protein secondary structure prediction, are examined. Input encoding is shown to have a greater impact on prediction accuracy than learning methodology with a binary input encoding providing the highest training and test set prediction accuracy.

关键词： Neural Networks protein secondary structure prediction Input Encoding Resilient Propagation Learning Vector Quantization

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Lightweight proteinUnet2 network for protein secondary structure prediction: a step towards proper evaluation

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BMC BIOINFORMATICS 2022年第1期23卷 100-100页

作者： Stapor, Katarzyna Kotowski, Krzysztof Smolarczyk, Tomasz Roterman, Irena Silesian Tech Univ Dept Appl Informat Akad 16 PL-44100 Gliwice Poland Jagiellonian Univ Med Coll Dept Bioinformat & Telemed Med 7 PL-30688 Krakow Poland

Background The prediction of protein secondary structures is a crucial and significant step for ab initio tertiary structure prediction which delivers the information about proteins activity and functions. As the experimental methods are expensive and sometimes impossible, many SS predictors, mainly based on different machine learning methods have been proposed for many years. Currently, most of the top methods use evolutionary-based input features produced by PSSM and HHblits software, although quite recently the embeddings-the new description of protein sequences generated by language models (LM) have appeared that could be leveraged as input features. Apart from input features calculation, the top models usually need extensive computational resources for training and prediction and are barely possible to run on a regular PC. SS prediction as the imbalanced classification problem should not be judged by the commonly used Q3/Q8 metrics. Moreover, as the benchmark datasets are not random samples, the classical statistical null hypothesis testing based on the Neyman-Pearson approach is not appropriate. Results We present a lightweight deep network proteinUnet2 for SS prediction which is based on U-Net convolutional architecture and evolutionary-based input features (from PSSM and HHblits) as well as SPOT-Contact features. Through an extensive evaluation study, we report the performance of proteinUnet2 in comparison with top SS prediction methods based on evolutionary information (SAINT and SPOT-1D). We also propose a new statistical methodology for prediction performance assessment based on the significance from Fisher-Pitman permutation tests accompanied by practical significance measured by Cohen's effect size. Conclusions Our results suggest that proteinUnet2 architecture has much shorter training and inference times while maintaining results similar to SAINT and SPOT-1D predictors. Taking into account the relatively long times of calculating evolutionary-based fe

关键词： protein secondary structure prediction U-Net Deep learning PSSM HHblits

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Self-attention and asymmetric multi-layer perceptron-gated recurrent unit blocks for protein secondary structure prediction

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APPLIED SOFT COMPUTING 2024年 159卷

作者： Ismi, Dewi Pramudi Pulungan, Reza Afiahayati Univ Gadjah Mada Fac Math & Nat Sci Dept Comp Sci & Elect Yogyakarta Indonesia Univ Ahmad Dahlan Fac Ind Technol Dept Informat Yogyakarta Indonesia

protein secondary structure prediction (PSSP) is one of the most prominent and widely -conducted tasks in Bioinformatics. Deep neural networks have become the primary methods for building PSSP models in the last decade due to their potential to enhance PSSP performances. However, there is room for improvement in PSSP as previous studies have yet to reach the theoretical limit of PSSP model performance. In this work, we propose a PSSP model called SADGRU-SS, which is built with a novel and unique deep learning architecture that utilizes self -attention, asymmetric multi -layer perceptron (MLP)-gated recurrent unit (GRU) blocks, and a dense block for solving the PSSP problem. Our experiment results show that using self -attention in the SADGRUSS architecture has successfully increased SADGRU-SS performance. Moreover, installing self -attention in the frontmost position of the networks produces better performance than locating it in other positions. Using the asymmetric configuration in the MLP-GRU blocks results in more excellent performance than the symmetric ones. Our model is trained using the standard CB6133-filtered dataset. We evaluate the performance of our model using the standard CB513 test dataset. Our experiment shows that the performance of our model on 8 -state PSSP outstands other PSSP models. The model achieves 70.74% and 82.78% prediction accuracy in the 8 -state and 3 -state PSSP, respectively.

关键词： protein secondary structure prediction Deep learning Self-attention Asymmetric MLP-GRU block prediction accuracy

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Ensemble deep learning models for protein secondary structure prediction using bidirectional temporal convolution and bidirectional long short-term memory

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FRONTIERS IN BIOENGINEERING AND BIOTECHNOLOGY 2023年 11卷 1051268页

作者： Yuan, Lu Ma, Yuming Liu, Yihui Qilu Univ Technol Shandong Acad Sci Sch Comp Sci & Technol Jinan Peoples R China

protein secondary structure prediction (PSSP) is a challenging task in computational biology. However, existing models with deep architectures are not sufficient and comprehensive for deep long-range feature extraction of long sequences. This paper proposes a novel deep learning model to improve protein secondary structure prediction. In the model, our proposed bidirectional temporal convolutional network (BTCN) can extract the bidirectional deep local dependencies in protein sequences segmented by the sliding window technique, the bidirectional long short-term memory (BLSTM) network can extract the global interactions between residues, and our proposed multi-scale bidirectional temporal convolutional network (MSBTCN) can further capture the bidirectional multi-scale long-range features of residues while preserving the hidden layer information more comprehensively. In particular, we also propose that fusing the features of 3-state and 8-state protein secondary structure prediction can further improve the prediction accuracy. Moreover, we also propose and compare multiple novel deep models by combining bidirectional long short-term memory with temporal convolutional network (TCN), reverse temporal convolutional network (RTCN), multi-scale temporal convolutional network (multi-scale bidirectional temporal convolutional network), bidirectional temporal convolutional network and multi-scale bidirectional temporal convolutional network, respectively. Furthermore, we demonstrate that the reverse prediction of secondary structure outperforms the forward prediction, suggesting that amino acids at later positions have a greater impact on secondary structure recognition. Experimental results on benchmark datasets including CASP10, CASP11, CASP12, CASP13, CASP14, and CB513 show that our methods achieve better prediction performance compared to five state-of-the-art methods.

关键词： protein secondary structure prediction bidirectional temporal convolutional network bidirectional long short-term memory multi-scale BTCN reverse prediction fusing the features

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