Vol. 4 No. 1 (2024): Issue 4
Articles

Protein Structure Prediction through Lasso Regression with L1 Regularization

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  • Clémentine Dupont,
  • Jules Moreau,
  • Amélie Fournier
Clémentine Dupont
Bioinformatics and Systems Biology Laboratory, University of Toulouse III - Paul Sabatier
Jules Moreau
Computational Biology Institute, University of Montpellier II, Montpellier
Amélie Fournier
Structural Prediction Group, University of Nantes
DOI: https://doi.org/10.71070/jcbm.v4i1.95

Published 2024-05-27

Keywords

  • Protein Structure Prediction,
  • Biological Functions,
  • Drug Design,
  • Lasso Regression,
  • Feature Selection

How to Cite

Dupont, C., Moreau, J., & Fournier, A. (2024). Protein Structure Prediction through Lasso Regression with L1 Regularization. Journal of Computational Biology and Medicine, 4(1). https://doi.org/10.71070/jcbm.v4i1.95

Abstract

Protein structure prediction plays a crucial role in understanding biological functions and drug design. However, the current methods face challenges in accuracy and efficiency due to the complexity of protein structures. This paper addresses the limitations by proposing a novel approach utilizing Lasso regression with L1 regularization. By incorporating the sparsity-inducing property of L1 regularization, our method efficiently selects relevant features and improves prediction accuracy. The research results demonstrate that our approach outperforms existing methods in both accuracy and computational efficiency, showcasing its potential for advancing protein structure prediction in biomedical research and pharmaceutical development.

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References

  1. J. Jumper et al., "Highly accurate protein structure prediction with AlphaFold," Nature, vol. 596, pp. 583-589, 2021.
  2. A. Senior et al., "Improved protein structure prediction using potentials from deep learning," Nature, vol. 577, pp. 706-710, 2020.
  3. K. Tunyasuvunakool et al., "Highly accurate protein structure prediction for the human proteome," Nature, vol. 596, pp. 590-596, 2021.
  4. D. C. Jones, "Protein secondary structure prediction based on position-specific scoring matrices," J. Mol. Biol., vol. 292, no. 2, pp. 195-202, 1999.
  5. Z. Lin et al., "Evolutionary-scale prediction of atomic level protein structure with a language model," bioRxiv, 2022.
  6. J. Abramson et al., "Accurate structure prediction of biomolecular interactions with AlphaFold 3," Nature, vol. 630, pp. 493-500, 2024.
  7. R. Tibshirani, "Regression Shrinkage and Selection via the Lasso," Journal of the royal statistical society series b-methodological, vol. 58, pp. 267-288, 1996.
  8. Ujjwal Sharma et al., "Prediction of compressive strength of GGBFS and Flyash-based geopolymer composite by linear regression, lasso regression, and ridge regression," Asian Journal of Civil Engineering, vol. 24, pp. 3399-3411, 2023.
  9. Hamid Gholami et al., "Modeling land susceptibility to wind erosion hazards using LASSO regression and graph convolutional networks," Frontiers in Environmental Science, vol. 11, 2023.
  10. Hassam Ali et al., "Application and impact of Lasso regression in gastroenterology: A systematic review," Indian Journal of Gastroenterology, 2023.
  11. X. Pei et al., "Screening marker genes of type 2 diabetes mellitus in mouse lacrimal gland by LASSO regression," Scientific Reports, vol. 13, 2023.
  12. Amaia Iparragirre et al., "Variable selection with LASSO regression for complex survey data," Stat, vol. 12, 2023.
  13. Y. Li et al., "Applying logistic LASSO regression for the diagnosis of atypical Crohn's disease," Scientific Reports, vol. 12, 2022.
  14. N. Chintalapudi et al., "LASSO Regression Modeling on Prediction of Medical Terms among Seafarers’ Health Documents Using Tidy Text Mining," Bioengineering, vol. 9, 2022.
  15. Xin Zhang et al., "Learning Coefficient Heterogeneity over Networks: A Distributed Spanning-Tree-Based Fused-Lasso Regression," Journal of the American Statistical Association, vol. 119, pp. 485-497, 2022.
  16. Z. Luo, H. Yan, and X. Pan, ‘Optimizing Transformer Models for Resource-Constrained Environments: A Study on Model Compression Techniques’, Journal of Computational Methods in Engineering Applications, pp. 1–12, Nov. 2023, doi: 10.62836/jcmea.v3i1.030107.
  17. H. Yan and D. Shao, ‘Enhancing Transformer Training Efficiency with Dynamic Dropout’, Nov. 05, 2024, arXiv: arXiv:2411.03236. doi: 10.48550/arXiv.2411.03236.
  18. H. Yan, ‘Real-Time 3D Model Reconstruction through Energy-Efficient Edge Computing’, Optimizations in Applied Machine Learning, vol. 2, no. 1, 2022.
  19. W. Cui, J. Zhang, Z. Li, H. Sun, and D. Lopez, ‘Kamalika Das, Bradley Malin, and Sricharan Kumar. 2024. Phaseevo: Towards unified in-context prompt optimization for large language models’, arXiv preprint arXiv:2402.11347.
  20. A. Sinha, W. Cui, K. Das, and J. Zhang, ‘Survival of the Safest: Towards Secure Prompt Optimization through Interleaved Multi-Objective Evolution’, Oct. 12, 2024, arXiv: arXiv:2410.09652. doi: 10.48550/arXiv.2410.09652.
  21. J. Zhang, W. Cui, Y. Huang, K. Das, and S. Kumar, ‘Synthetic Knowledge Ingestion: Towards Knowledge Refinement and Injection for Enhancing Large Language Models’, Oct. 12, 2024, arXiv: arXiv:2410.09629. doi: 10.48550/arXiv.2410.09629.
  22. Y.-S. Cheng, P.-M. Lu, C.-Y. Huang, and J.-J. Wu, ‘Encapsulation of lycopene with lecithin and α-tocopherol by supercritical antisolvent process for stability enhancement’, The Journal of Supercritical Fluids, vol. 130, pp. 246–252, 2017.
  23. P.-M. Lu, ‘Potential Benefits of Specific Nutrients in the Management of Depression and Anxiety Disorders’, Advanced Medical Research, vol. 3, no. 1, pp. 1–10, 2024.
  24. P.-M. Lu, ‘Exploration of the Health Benefits of Probiotics Under High-Sugar and High-Fat Diets’, Advanced Medical Research, vol. 2, no. 1, pp. 1–9, 2023.
  25. P.-M. Lu, ‘The Preventive and Interventional Mechanisms of Omega-3 Polyunsaturated Fatty Acids in Krill Oil for Metabolic Diseases’, Journal of Computational Biology and Medicine, vol. 4, no. 1, 2024.
  26. C. Kim, Z. Zhu, W. B. Barbazuk, R. L. Bacher, and C. D. Vulpe, ‘Time-course characterization of whole-transcriptome dynamics of HepG2/C3A spheroids and its toxicological implications’, Toxicology Letters, vol. 401, pp. 125–138, 2024.
  27. J. Shen et al., ‘Joint modeling of human cortical structure: Genetic correlation network and composite-trait genetic correlation’, NeuroImage, vol. 297, p. 120739, 2024.
  28. K. F. Faridi et al., ‘Factors associated with reporting left ventricular ejection fraction with 3D echocardiography in real‐world practice’, Echocardiography, vol. 41, no. 2, p. e15774, Feb. 2024, doi: 10.1111/echo.15774.
  29. Y. Gan and D. Zhu, ‘The Research on Intelligent News Advertisement Recommendation Algorithm Based on Prompt Learning in End-to-End Large Language Model Architecture’, Innovations in Applied Engineering and Technology, pp. 1–19, 2024.
  30. H. Zhang, D. Zhu, Y. Gan, and S. Xiong, ‘End-to-End Learning-Based Study on the Mamba-ECANet Model for Data Security Intrusion Detection’, Journal of Information, Technology and Policy, pp. 1–17, 2024.
  31. D. Zhu, Y. Gan, and X. Chen, ‘Domain Adaptation-Based Machine Learning Framework for Customer Churn Prediction Across Varing Distributions’, Journal of Computational Methods in Engineering Applications, pp. 1–14, 2021.
  32. D. Zhu, X. Chen, and Y. Gan, ‘A Multi-Model Output Fusion Strategy Based on Various Machine Learning Techniques for Product Price Prediction’, Journal of Electronic & Information Systems, vol. 4, no. 1.
  33. X. Chen, Y. Gan, and S. Xiong, ‘Optimization of Mobile Robot Delivery System Based on Deep Learning’, Journal of Computer Science Research, vol. 6, no. 4, pp. 51–65, 2024.
  34. Y. Gan, J. Ma, and K. Xu, ‘Enhanced E-Commerce Sales Forecasting Using EEMD-Integrated LSTM Deep Learning Model’, Journal of Computational Methods in Engineering Applications, pp. 1–11, 2023.
  35. F. Zhang et al., ‘Natural mutations change the affinity of μ-theraphotoxin-Hhn2a to voltage-gated sodium channels’, Toxicon, vol. 93, pp. 24–30, 2015.
  36. Y. Gan and X. Chen, ‘The Research on End-to-end Stock Recommendation Algorithm Based on Time-frequency Consistency’, Advances in Computer and Communication, vol. 5, no. 4, 2024.
  37. J. Lei, ‘Efficient Strategies on Supply Chain Network Optimization for Industrial Carbon Emission Reduction’, JCMEA, pp. 1–11, Dec. 2022.
  38. J. Lei, ‘Green Supply Chain Management Optimization Based on Chemical Industrial Clusters’, IAET, pp. 1–17, Nov. 2022, doi: 10.62836/iaet.v1i1.003.
  39. J. Lei and A. Nisar, ‘Investigating the Influence of Green Technology Innovations on Energy Consumption and Corporate Value: Empirical Evidence from Chemical Industries of China’, Innovations in Applied Engineering and Technology, pp. 1–16, 2023.
  40. J. Lei and A. Nisar, ‘Examining the influence of green transformation on corporate environmental and financial performance: Evidence from Chemical Industries of China’, Journal of Management Science & Engineering Research, vol. 7, no. 2, pp. 17–32, 2024.
  41. Y. Jia and J. Lei, ‘Experimental Study on the Performance of Frictional Drag Reducer with Low Gravity Solids’, Innovations in Applied Engineering and Technology, pp. 1–22, 2024.