Open Access
| Issue |
BIO Web Conf.
Volume 163, 2025
2025 15th International Conference on Bioscience, Biochemistry and Bioinformatics (ICBBB 2025)
|
|
|---|---|---|
| Article Number | 01001 | |
| Number of page(s) | 13 | |
| Section | Bioinformatics and Computational Biology | |
| DOI | https://doi.org/10.1051/bioconf/202516301001 | |
| Published online | 06 March 2025 | |
- I. Thiele, B.Ø. Palsson, A protocol for generating a high-quality genome-scale metabolic reconstruction, Nature Protocols 5, 93 (2010). [CrossRef] [PubMed] [Google Scholar]
- A. Elnaggar, M. Heinzinger, C. Dallago, G. Rihawi, Y. Wang, L. Jones, T. Gibbs, T. Feher, C. Angerer, M. Steinegger et al., ProtTrans: Towards cracking the language of life’s code through self-supervised deep learning and high performance computing, IEEE Trans. Pattern Analysis & Machine Intelligence pp. 7112–7127 (2022). [CrossRef] [PubMed] [Google Scholar]
- K. Han, M. Wang, L. Zhang, Y. Wang, M. Guo, M. Zhao, Q. Zhao, Y. Zhang, N. Zeng, C. Wang, Predicting ion channels genes and their types with machine learning techniques, Frontiers in Genetics 10, 399 (2019). [CrossRef] [PubMed] [Google Scholar]
- V.S. Reddy, M.H. Saier Jr, BioV Suite–a collection of programs for the study of transport protein evolution, FEBS Journal 279, 2036 (2012). [CrossRef] [PubMed] [Google Scholar]
- F. Aplop, G. Butler, TransATH: Transporter prediction via annotation transfer by homology, ARPN J. of Engineering & Applied Sciences 12 (2017). [Google Scholar]
- O. Dias, M. Rocha, E.C. Ferreira, I. Rocha, Reconstructing genome-scale metabolic models with merlin, Nucleic Acids Research 43, 3899 (2015). [CrossRef] [PubMed] [Google Scholar]
- J. Capela, D. Lagoa, R. Rodrigues, E. Cunha, F. Cruz, A. Barbosa, J. Bastos, D. Lima, E.C. Ferreira, M. Rocha et al., Merlin, an improved framework for the reconstruction of high-quality genome-scale metabolic models, Nucleic Acids Research 50, 6052 (2022). [CrossRef] [PubMed] [Google Scholar]
- E. Cunha, D. Lagoa, J.P. Faria, F. Liu, C.S. Henry, O. Dias, TranSyT, an innovative framework for identifying transport systems, Bioinformatics 39, btad466 (2023). [CrossRef] [PubMed] [Google Scholar]
- N. Loira, A. Zhukova, D.J. Sherman, Pantograph: A template-based method for genome-scale metabolic model reconstruction, J. of Bioinformatics & Computational Biology 13, 1550006 (2015). [CrossRef] [PubMed] [Google Scholar]
- H. Li, V.A. Benedito, M.K. Udvardi, P.X. Zhao, TransportTP: a two-phase classification approach for membrane transporter prediction and characterization, BMC Bioinformatics 10, 418 (2009). [CrossRef] [PubMed] [Google Scholar]
- M.M. Gromiha, Y. Yabuki, Functional discrimination of membrane proteins using machine learning techniques, BMC Bioinformatics 9, 135 (2008). [CrossRef] [PubMed] [Google Scholar]
- Y.Y. Ou, S.A. Chen, M.M. Gromiha, Classification of transporters using efficient radial basis function networks with position-specific scoring matrices & biochemical properties, Proteins: Structure, Function & Bioinformatics 78, 1789 (2010). [CrossRef] [PubMed] [Google Scholar]
- S.A. Chen, Y.Y. Ou, T.Y. Lee, M.M. Gromiha, Prediction of transporter targets using efficient RBF networks with PSSM profiles & biochemical properties, Bioinformatics 27, 2062 (2011). [CrossRef] [PubMed] [Google Scholar]
- N.S. Schaadt, J. Christoph, V. Helms, Classifying substrate specificities of membrane transporters from Arabidopsis thaliana, J. Chemical Information & Modeling 50, 1899 (2010). [CrossRef] [PubMed] [Google Scholar]
- N. Schaadt, V. Helms, Functional classification of membrane transporters and channels based on filtered TM/non-TM amino acid composition, Biopolymers 97, 558 (2012). [CrossRef] [PubMed] [Google Scholar]
- A. Barghash, V. Helms, Transferring functional annotations of membrane transporters on the basis of sequence similarity and sequence motifs, BMC Bioinformatics 14, 343 (2013). [CrossRef] [PubMed] [Google Scholar]
- N.K. Mishra, J. Chang, P.X. Zhao, Prediction of membrane transport proteins and their substrate specificities using primary sequence information, PLoS ONE 9, e100278 (2014). [CrossRef] [PubMed] [Google Scholar]
- M. Alballa, F. Aplop, G. Butler, TranCEP: Predicting the substrate class of transmembrane transport proteins using compositional, evolutionary, and positional information, PLoS ONE 15, e0227683 (2020). [CrossRef] [PubMed] [Google Scholar]
- M. Alballa, Ph.D. thesis, Concordia University (2020) [Google Scholar]
- M. Alballa, G. Butler, TooT-SC: Predicting eleven substrate classes of transmembrane transport proteins, bioRxiv (2022). 10.1101/2022.01.25.477715 [Google Scholar]
- H. Lin, L. Han, C. Cai, Z. Ji, Y. Chen, Prediction of transporter family from protein sequence by support vector machine approach, Proteins: Structure, Function & Bioinformatics 62, 218 (2006). [CrossRef] [PubMed] [Google Scholar]
- Y.F. Liou, T. Vasylenko, C.L. Yeh, W.C. Lin, S.H. Chiu, P. Charoenkwan, L.S. Shu, S.Y. Ho, H.L. Huang, SCMMTP: identifying and characterizing membrane transport proteins using propensity scores of dipeptides, BMC Genomics 16, S6 (2015). [CrossRef] [Google Scholar]
- L. Li, J. Li, W. Xiao, Y. Li, Y. Qin, S. Zhou, H. Yang, Prediction the substrate specificities of membrane transport proteins based on support vector machine and hybrid features, IEEE Trans. Computational Biology & Bioinformatics 13, 947 (2016). [CrossRef] [PubMed] [Google Scholar]
- Q.T. Ho, D.V. Phan, Y.Y. Ou et al., Using word embedding technique to efficiently represent protein sequences for identifying substrate specificities of transporters, Analytical Biochemistry 577, 73 (2019). [CrossRef] [PubMed] [Google Scholar]
- S. Ataei, G. Butler, Predicting the specific substrate for transmembrane transport proteins using BERT language model, in 2022 IEEE Conf. on Computational Intelligence in Bioinformatics & Computational Biology (IEEE, 2022), pp. 1–8 [Google Scholar]
- J. Devlin, M.W. Chang, K. Lee, K. Toutanova, BERT: Pre-training of deep bidirectional transformers for language understanding, arXiv:1810.04805 (2018). [Google Scholar]
- A. Vaswani, N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, I. Polosukhin, Attention is all you need, Advances in Neural Information Processing Systems 30 (2017). [Google Scholar]
- S.J. Pan, Q. Yang, A survey on transfer learning, IEEE Transactions on Knowledge & Data Engineering 22, 1345 (2009). [Google Scholar]
- M. Steinegger, J. Söding, Clustering huge protein sequence sets in linear time, Nature Communications 9, 1 (2018). [CrossRef] [PubMed] [Google Scholar]
- The UniProt Consortium, UniProt: the universal protein knowledgebase in 2023, Nucleic Acids Research 51, D523 (2022). 10.1093/nar/gkac1052 [Google Scholar]
- Y. Huang, B. Niu, Y. Gao, L. Fu, W. Li, CD-HIT Suite: a web server for clustering & comparing biological sequences, Bioinformatics 26, 680 (2010). [CrossRef] [PubMed] [Google Scholar]
- B. Krawczyk, Learning from imbalanced data: open challenges and future directions, Progress in Artificial Intelligence 5, 221 (2016). [CrossRef] [Google Scholar]
- D.P. Kingma, J. Ba, Adam: A method for stochastic optimization, arXiv:1412.6980 (2014). [Google Scholar]
- A. Paszke, S. Gross, F. Massa, A. Lerer, J. Bradbury, G. Chanan, T. Killeen, Z. Lin, N. Gimelshein, L. Antiga et al., PyTorch: An imperative style, highperformance deep learning library, Advances in Neural Information Processing Systems 32 (2019). [Google Scholar]
- T. Wolf, L. Debut, V. Sanh, J. Chaumond, C. Delangue, A. Moi, P. Cistac, T. Rault, R. Louf, M. Funtowicz et al., Huggingface’s transformers: State-ofthe-art natural language processing, arXiv:1910.03771 (2019). [Google Scholar]
- Z. Ding, Ph.D. thesis, Georgia State University (2011) [Google Scholar]
- G.M. Weiss, F. Provost, Learning when training data are costly: The effect of class distribution on tree induction, Journal of Artificial Intelligence Research 19, 315 (2003). [CrossRef] [Google Scholar]
- M. Bekkar, H.K. Djemaa, T.A. Alitouche, Evaluation measures for models assessment over imbalanced data sets, J. Information Engineering & Applications 3, 27 (2013). [Google Scholar]
- C.D. Manning, P. Raghavan, H. Schütze, Introduction to information retrieval (Cambridge University Press, 2008) [Google Scholar]
- J. Gorodkin, Comparing two K-category assignments by a K-category correlation coefficient, Computational Biology & Chemistry 28, 367 (2004). [CrossRef] [Google Scholar]
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