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Protein Sequences Classification Using Modular RBF Neural Networks
[chapter]
2002
Lecture Notes in Computer Science
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The architecture of the proposed model is a modular RBF neural network with a compensational combination at the transition output layer. ...
This paper presents a modular neural classifier for protein sequences with improved classification criteria. ...
The architecture of the proposed model is a modular RBF neural network with a compensational combination at the transition output layer. ...
doi:10.1007/3-540-36187-1_42
fatcat:p3zj6ruglzezxcnevfbkjdshce
RMSD Protein Tertiary Structure Prediction with Soft Computing
2016
International Journal of Mathematical Sciences and Computing
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Protein structure and RMSD prediction is very essential. In 2013, the estimated RMSD proteins based on nine features were obtained best results using D2N (Distance to the native). ...
We presented in This paper proposed approach to reduce predicted RMSD Error Than the actual amount for RMSD and calculate mean absolute error (MAE), through feed forward neural network, adaptive neuro ...
These RBF neural networks were trained to predict the protein-protein interaction sites. ...
doi:10.5815/ijmsc.2016.02.03
fatcat:doxzgszojffolduicexnkfcngu
A Performance Appraisal Of Neural Networks Developed For Response Prediction Across Heterogeneous Domains
2009
Zenodo
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Deciding the numerous parameters involved in designing a competent artificial neural network is a complicated task. ...
Several neural network architectures with different parameters were developed for each application and the results were compared. ...
Among several other networks tried for modeling the oncogenicity level, generalized feed forward (GF), modular neural network (MNN), and RBF/GRNN/PNN network yielded acceptable results which are presented ...
doi:10.5281/zenodo.1073039
fatcat:fok3pmgzfrekrobqkvjydx22xy
Two-Stage Approach for Protein Superfamily Classification
2013
Computational Biology Journal
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We deal with the problem of protein superfamily classification in which the family membership of newly discovered amino acid sequence is predicted. ...
In the second stage, recursive orthogonal least square algorithm (ROLSA) is used for training radial basis function network (RBFN) to select optimal number of hidden centres as well as update the output ...
Zhao et al. in [15] developed a hybrid GA/RBFNN technique which selects features from protein sequences and trains the RBF neural network simultaneously. ...
doi:10.1155/2013/898090
fatcat:37lasfpuifflvhtndekjhg36fe
Prediction of Protein Subcellular Multi-localization by Using a Min-Max Modular Support Vector Machine
[chapter]
2009
Advances in Soft Computing
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, and learn all of the subproblems by using the min-max modular support vector machine (M 3 -SVM). ...
Regarding the protein multi-location problem as a multi-label pattern classification problem, we propose a new predicting method for dealing with the protein subcellular localization problem in this paper ...
Prediction of Protein Subcellular Multi-locations ...
doi:10.1007/978-3-642-03156-4_14
fatcat:o7kpzi3k2nhkxl2u5skqn67m2y
Prediction of Protein Subcellular Multi-locations with a Min-Max Modular Support Vector Machine
[chapter]
2006
Lecture Notes in Computer Science
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, and learn all of the subproblems by using the min-max modular support vector machine (M 3 -SVM). ...
Regarding the protein multi-location problem as a multi-label pattern classification problem, we propose a new predicting method for dealing with the protein subcellular localization problem in this paper ...
Prediction of Protein Subcellular Multi-locations ...
doi:10.1007/11760191_98
fatcat:4j35kvia6nhkxen3rerwz5kkl4
DeepProteomics: Protein family classification using Shallow and Deep Networks
[article]
2018
bioRxiv
pre-print
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We pass it to recurrent neural network (RNN), long short term memory (LSTM) and gated recurrent unit (GRU) model and compare it by applying trigram with deep neural network and shallow neural network on ...
The laboratory experiments take a considerable amount of time for annotation of the sequences. This arises the need to use computational techniques to classify proteins based on their functions. ...
it performance by applying trigram with deep and shallow neural networks for protein family classification. ...
doi:10.1101/414631
fatcat:oo4ecaydprdufb4pztcpyogvoe
Utilizing Domain Knowledge in Neural Network Models for Peptide-Allele Binding Prediction
2005
2005 IEEE Symposium on Computational Intelligence in Bioinformatics and Computational Biology
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We developed Radial Basis Function Neural Networks (RBFNN) for allele-peptide binding prediction. We explored utilizing prior domain knowledge in order to optimize the prediction. ...
An overview of Radial Basis Function Neural Networks Radial Basis Function Neural Networks (RBFNNs) are considered a subclass of Modular Neural Networks (MNNs). ...
We employ the modular neural network (MNN) model based on Radial Basis Functions (RBFNN) which provides the ability to apply different criteria in the modules and easily adapt the network to the domain ...
doi:10.1109/cibcb.2005.1594941
fatcat:rzgyn6bzezfrvc42yipzufvhgi
Bioinformatics with soft computing
2006
IEEE Transactions on Systems Man and Cybernetics Part C (Applications and Reviews)
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Genomic sequence, protein structure, gene expression microarrays, and gene regulatory networks are some of the application areas described. ...
In this paper, we survey the role of different soft computing paradigms, like fuzzy sets (FSs), artificial neural networks (ANNs), evolutionary computation, rough sets (RSes), and support vector machines ...
An RBF neural network is employed to optimize the classification by tuning the membership functions.
D. ...
doi:10.1109/tsmcc.2006.879384
fatcat:owim7m6genf6xc7s2bbhjuz7gu
Classifying gene expression data of cancer using classifier ensemble with mutually exclusive features
2002
Proceedings of the IEEE
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Gene expression profiles are just sequences of numbers, and the necessity of tools analyzing them to get useful information has risen significantly. ...
The explosion of DNA and protein sequence data in public and private databases has been encouraging interdisciplinary research on biology and information technology. ...
Related Works Many researchers have been working on the ensemble of modular neural networks. ...
doi:10.1109/jproc.2002.804682
fatcat:spzlt33aeza6flphxfz2qelx6u
A deep neural network approach to predicting clinical outcomes of neuroblastoma patients
[article]
2019
bioRxiv
pre-print
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Neural networks usually tackle this problem through feature selection or by including additional constraints during the learning process. ...
Our results indicate that the deep neural networks capture complex features in the data that help predicting patient clinical outcomes. ...
Maris and Dr Alvin Farrel for helping us with the clinical data of their cohort. We thank Liyanaarachchi Lekamalage Chamara Kasun for helpful discussion about the DNN models. ...
doi:10.1101/750364
fatcat:etwctzgvmfazlnb36js65b3loa
Machine Learning Approaches for the Prioritization of Genomic Variants Impacting Pre-mRNA Splicing
2019
Cells
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A critical comparative approach is then used to describe seven recent machine learning-based splice prediction tools, revealing highly diverse approaches and common caveats. ...
Despite the advent of next-generation sequencing, allowing a deeper insight into a patient's variant landscape, the ability to characterize variants causing splicing defects has not progressed with the ...
network
GENCODE v24 pre-mRNA transcript
sequence for human protein-coding
genes
Direct encoding of the sequence
PR-AUC = 0.98 in correct
prediction of splice site location
from raw sequence ...
doi:10.3390/cells8121513
pmid:31779139
pmcid:PMC6953098
fatcat:4wxj7hv5d5fpndh37yjtx7jcji
A deep neural network approach to predicting clinical outcomes of neuroblastoma patients
2019
BMC Medical Genomics
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Neural networks usually tackle this problem through feature selection or by including additional constraints during the learning process. ...
Maris and Dr Alvin Farrel for helping us with the clinical data of their cohort. We thank Dr Liyanaarachchi Lekamalage Chamara Kasun for helpful discussion about the DNN models. ...
Deep Neural Networks (DNN) are feed forward neural networks with hidden layers, which can be trained to solve classification and regression problems. ...
doi:10.1186/s12920-019-0628-y
pmid:31856829
pmcid:PMC6923884
fatcat:f5kw4zait5h3fj5qlmsp4mcyza
BRNN-SVM: Increasing the Strength of Domain Signal to Improve Protein Domain Prediction Accuracy
[chapter]
2012
Recurrent Neural Networks and Soft Computing
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Bidirectional Recurrent Neural Network (BRNN) is used to generate secondary structure from alignment of protein sequence in order to highlight the signal of protein domain boundaries. ...
Previously, Neural Network (NN) is used as a classifier to detect protein domain such as in the work of Armadillo, Biozon, Dompred-DPS, and DOMpro. ...
doi:10.5772/36188
fatcat:da6m6wz3bzaujfchcyqv5fwgpm
A review of modularization techniques in artificial neural networks
2019
Artificial Intelligence Review
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Note that this fulltext copy is not of the "primary" version of this work. The version it corresponds to is:
2019 pre-print arXiv:1904.12770v1 fatcat:uk7ck3ferrgplfyirjiku3srey
Modular neural networks (MNNs) are neural networks that embody the concepts and principles of modularity. MNNs adopt a large number of different techniques for achieving modularization. ...
to emphasise the strengths and weaknesses of different modularization approaches in order to highlight good practices for neural network practitioners. ...
Neural Network (CNN), in order to enhance classification performance. ...
doi:10.1007/s10462-019-09706-7
fatcat:g4xp6dktvncu5dao53dcvoexoa
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