Computational Molecular Biology 2024, Vol.14, No.4, 173-181 http://bioscipublisher.com/index.php/cmb 175 Figure 1 Examples Demonstrating the “Advanced Circos” and “eFP Browser” Functions in TBtools (Adopted from Chen et al., 2020) 3.1.2 Predictive models for protein structure and function Predicting protein structure and function is a major challenge in bioinformatics, which has seen significant advancements through the application of machine learning. Deep learning methods, such as convolutional neural networks, have been used to predict residue-residue contacts and reconstruct protein tertiary structures from sequence data, achieving top rankings in critical assessments of protein structure prediction. Bioinformatics tools continue to evolve, improving the accuracy of predictions regarding protein functionality, homology, mutations, and evolutionary processes (Hernández-Domínguez et al., 2019). 3.1.3 Integration of multi-omics data The integration of multi-omics data is crucial for a comprehensive understanding of biological systems. High-performance computing (HPC) infrastructure has empowered machine learning and optimization algorithms to analyze and integrate large-scale omics data. For example, large-scale data-driven optimization algorithms have been developed to reconstruct high-resolution 3D genome structures from Hi-C data, which can be used to study
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