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- 2019
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Tidskriftsartikel (refereegranskat)
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| 2. |
- You, Liwen
(författare)
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Computational Prediction Models for Proteolytic Cleavage and Epitope Identification
- 2007
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Doktorsavhandling (övrigt vetenskapligt/konstnärligt)abstract
- The biological functions of proteins depend on their physical interactions with other molecules, such as proteins and peptides. Therefore, modeling the protein-ligand interactions is important for understanding protein functions in different biological processes. We have focused on the cleavage specificities of HIV-1 protease, HCV NS3 protease and caspases on short oligopeptides or in native proteins; the binding affinity of MHC molecules with short oligopeptides and identification of T cell epitopes. We expect that our findings on HIV-1 protease, HCV NS3 protease and caspases generalize to other proteases. In this thesis, we have performed analysis on these interactions from different perspectives --- we have extended and collected new substrate data sets; used and compared different prediction methods (e.g. linear support vector machines, neural networks, OSRE method, rough set theory and Gaussian processes) to understand the underlying interaction problems; suggested new methods (i.e. a hierarchical method and Gaussian processes with test reject method) to improve predictions; and extracted cleavage rules for protease cleavage specificities. From our studies, we have extended oligopeptide substrate data sets and collected native protein substrates for HIV-1 protease, and a new oligopeptide substrate data set for HCV protease. We have shown that all current HIV-1 protease oligopeptide substrate data sets and our HCV data set are linearly separable; for HIV-1 protease, size and hydrophobicity are two important physicochemical properties in the recognition of short oligopeptide substrates to the protease; and linear support vector machine is the state-of-the-art for this protease cleavage prediction problem. Our hierarchical method combining protein secondary structure information and experimental short oligopeptide cleavage information can improve the prediction of HIV-1 protease cleavage sites in native proteins. Our rule extraction method provides simple and accurate cleavage rules with high fidelity for HIV-1 and HCV proteases. For MHC molecules, we showed that high binding affinities are not necessarily correlated to immunogenicity on HLA-restricted peptides. Our test reject method combined with Gaussian processes can simplify experimental design by reducing false positives for detecting potential epitopes in large pathogen genomes.
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| 3. |
- Cameron, J., et al.
(författare)
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A biometric approach to laboratory rodent identification
- 2007
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Ingår i: Lab animal. - New York : Nature Publishing Group. - 0093-7355 .- 1548-4475. ; 36:3, s. 36-40
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Tidskriftsartikel (refereegranskat)abstract
- Individual identification of laboratory rodents typically involves invasive methods, such as tattoos, ear clips, and implanted transponders. Beyond the ethical dilemmas they may present, these methods may cause pain or distress that confounds research results. The authors describe a prototype device for biometric identification of laboratory rodents that would allow researchers to identify rodents without the complications of other methods. The device, which uses the rodent's ear blood vessel pattern as the identifier, is fast, automatic, noninvasive, and painless.
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| 4. |
- Parsapoor, Mahboobeh, 1978-, et al.
(författare)
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A new computational intelligence model for long-term prediction of solar and geomagnetic activity
- 2015
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Ingår i: Proceedings of the Twenty-Ninth AAAI Conference on Artificial Intelligence. - 0262511290 ; , s. 4192-4193
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Konferensbidrag (refereegranskat)abstract
- This paper briefly describes how the neural structure of fear conditioning has inspired to develop a computational intelligence model that is referred to as the brain emotional learning-inspired model (BELIM). The model is applied to predict long step ahead of solar activity and geomagnetic storms. © Copyright 2015, Association for the Advancement of Artificial Intelligence (www.aaai.org). All rights reserved.
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| 5. |
- Rögnvaldsson, Thorsteinn, 1963-, et al.
(författare)
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How to find simple and accurate rules for viral protease cleavage specificities
- 2009
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Ingår i: BMC Bioinformatics. - London : BioMed Central Ltd.. - 1471-2105. ; 10, s. 149-156
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Tidskriftsartikel (refereegranskat)abstract
- BACKGROUND:Proteases of human pathogens are becoming increasingly important drug targets, hence it is necessary to understand their substrate specificity and to interpret this knowledge in practically useful ways. New methods are being developed that produce large amounts of cleavage information for individual proteases and some have been applied to extract cleavage rules from data. However, the hitherto proposed methods for extracting rules have been neither easy to understand nor very accurate. To be practically useful, cleavage rules should be accurate, compact, and expressed in an easily understandable way.RESULTS:A new method is presented for producing cleavage rules for viral proteases with seemingly complex cleavage profiles. The method is based on orthogonal search-based rule extraction (OSRE) combined with spectral clustering. It is demonstrated on substrate data sets for human immunodeficiency virus type 1 (HIV-1) protease and hepatitis C (HCV) NS3/4A protease, showing excellent prediction performance for both HIV-1 cleavage and HCV NS3/4A cleavage, agreeing with observed HCV genotype differences. New cleavage rules (consensus sequences) are suggested for HIV-1 and HCV NS3/4A cleavages. The practical usability of the method is also demonstrated by using it to predict the location of an internal cleavage site in the HCV NS3 protease and to correct the location of a previously reported internal cleavage site in the HCV NS3 protease. The method is fast to converge and yields accurate rules, on par with previous results for HIV-1 protease and better than previous state-of-the-art for HCV NS3/4A protease. Moreover, the rules are fewer and simpler than previously obtained with rule extraction methods.CONCLUSION: A rule extraction methodology by searching for multivariate low-order predicates yields results that significantly outperform existing rule bases on out-of-sample data, but are more transparent to expert users. The approach yields rules that are easy to use and useful for interpreting experimental data.
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| 6. |
- Rögnvaldsson, Thorsteinn, 1963-, et al.
(författare)
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Improved protein identification in mammalian cells using a new automatic peak detection algorithm
- 2002
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Ingår i: Proceedings - 50th ASMS Conference on Mass Spectrometry and Allied Topics. - Santa Fe : ASMS. ; , s. 785-786
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Konferensbidrag (refereegranskat)abstract
- A comparison of two automatic peak detection algorithms is presented. One algorithm comes with the Voyager 5 Data ExplorerTM program, the other is a new algorithm called Pepex® (short for PEptide Peak Extractor) from BioBridge Computing. The peak sets selected with both tools have been compared, against each other and against manual peak selections, on a large set of mass spectra obtained after tryptic in gel digestion of 2D-gel samples from human fetal fibroblasts. It is shown how much variation there is in peak sets, both when selected by human operators and when selected by automatic peak detection algorithms. This variation has been used as an advantage to gain significantly better protein identification results, using the Pepex tool, than what an experienced mass spectroscopist has achieved on the same data. The strongest improvement has been observed in weak spectra, where the signal peak intensities are low.
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| 7. |
- Samuelsson, Jim, et al.
(författare)
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Modular, scriptable and automated analysis tools for high-throughput peptide mass fingerprinting
- 2004
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Ingår i: Bioinformatics. - Oxford : Oxford University Press. - 1367-4803 .- 1367-4811. ; 20:18, s. 3628-3635
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Tidskriftsartikel (refereegranskat)abstract
- A set of new algorithms and software tools for automatic protein identification using peptide mass fingerprinting is presented. The software is automatic, fast and modular to suit different laboratory needs, and it can be operated either via a Java user interface or called from within scripts. The software modules do peak extraction, peak filtering and protein database matching, and communicate via XML. Individual modules can therefore easily be replaced with other software if desired, and all intermediate results are available to the user. The algorithms are designed to operate without human intervention and contain several novel approaches. The performance and capabilities of the software is illustrated on spectra from different mass spectrometer manufacturers, and the factors influencing successful identification are discussed and quantified.
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| 8. |
- Verikas, Antanas, 1951-, et al.
(författare)
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Selecting neural networks for making a committee decision
- 2002
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Ingår i: ARTIFICIAL NEURAL NETWORKS - ICANN 2002. - Berlin : Springer Berlin/Heidelberg. - 9783540440741 - 9783540460848 ; , s. 420-425
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Konferensbidrag (refereegranskat)abstract
- To improve recognition results, decisions of multiple neural networks can be aggregated into a committee decision. In contrast to the ordinary approach of utilizing all neural networks available to make a committee decision, we propose creating adaptive committees, which are specific for each input data point. A prediction network is used to identify classification neural networks to be fused for making a committee decision about a given input data point. The jth output value of the prediction network expresses the expectation level that the jth classification neural network will make a correct decision about the class label of a given input data point. The effectiveness of the approach is demonstrated on two artificial and three real data sets.
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| 9. |
- Verikas, Antanas, 1951-, et al.
(författare)
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Selecting salient features for classification committees
- 2003
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Ingår i: Artificial Neural Networks and Neural Information Processing — ICANN/ICONIP 2003. - Heidelberg : Springer Berlin/Heidelberg. - 9783540404088 - 9783540449898 ; , s. 35-42
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Konferensbidrag (refereegranskat)abstract
- We present a neural network based approach for identifying salient features for classification in neural network committees. Our approach involves neural network training with an augmented cross-entropy error function. The augmented error function forces the neural network to keep low derivatives of the transfer functions of neurons of the network when learning a classification task. Feature selection is based on two criteria, namely the reaction of the cross-validation data set classification error due to the removal of the individual features and the diversity of neural networks comprising the committee. The algorithm developed removed a large number of features from the original data sets without reducing the classification accuracy of the committees. By contrast, the accuracy of the committees utilizing the reduced feature sets was higher than those exploiting all the original features. © Springer-Verlag Berlin Heidelberg 2003.
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