Computational Molecular Biology 2024, Vol.14, No.4, 173-181 http://bioscipublisher.com/index.php/cmb 176 gene function, gene expression, and genome methylation (Kanehisa, 2019). Deep learning architectures have been applied across various bioinformatics domains, including omics, biomedical imaging, and signal processing, to transform biomedical big data into valuable knowledge (Min et al, 2016). 3.2 Data mining and pattern recognition Data mining and pattern recognition are essential for extracting meaningful insights from large biological datasets. Formal concept analysis (FCA) is one such method that allows the examination of structural properties of data, facilitating applications such as gene data analysis, biomarker discovery, and protein-protein interaction analysis (Roscoe et al., 2022). Graph neural networks (GNNs) have been employed to analyze biological networks, predicting protein functions, protein-protein interactions, and aiding in drug discovery and development. 3.3 High-performance computing (HPC) for genomic data High-performance computing (HPC) plays a pivotal role in managing and analyzing the vast amounts of genomic data generated by next-generation sequencing technologies. HPC infrastructure, such as GPUs and HPC clusters, supports the execution of large-scale machine learning and optimization algorithms, enabling the fast analysis of massive DNA, RNA, and protein sequence data (Kashyap et al., 2016; Cheng, 2020). These computational resources are critical for addressing bioinformatics problems, such as the construction of co-expression and regulatory networks, detection of protein complexes, and querying heterogeneous disease networks. 4 Challenges in Bioinformatics and Big Data Management 4.1 Data storage and accessibility issues The exponential growth of biological data, driven by advancements in high-throughput sequencing and other technologies, has created significant challenges in data storage and accessibility. For instance, the European Bioinformatics Institute (EMBL-EBI) stored over 390 petabytes of raw data by the end of 2020, and this volume is expected to reach the exascale within the next few years (Shahid, 2023). Platforms like Sherlock have been developed to address these challenges by providing cloud-based solutions for storing, converting, querying, and sharing large datasets, thereby streamlining bioinformatics data management. However, the sheer volume and complexity of the data necessitate continuous improvements in storage technologies and data management practices to ensure that researchers can efficiently access and utilize these vast resources (Gauthier et al., 2018). 4.2 Managing data complexity and integration The complexity of biological data, which often includes diverse data types such as genomic sequences, protein structures, and interaction networks, poses significant challenges for integration and analysis. Tools like TBtools have been developed to facilitate the handling of such complex datasets by providing a user-friendly interface and a wide range of functions for data processing and visualization (Chen et al., 2020). The integration of deep learning techniques has shown promise in transforming biomedical big data into valuable knowledge, although it also introduces new challenges related to data heterogeneity and the need for specialized computational resources. Platforms like Sherlock further aid in managing data complexity by converting various structured data into optimized formats, enabling efficient distributed analytical queries (Bohár et al., 2022). 4.3 Ethical concerns and data privacy The management of large-scale biological data also raises significant ethical concerns and data privacy issues. The sensitive nature of personal health and genomic data necessitates robust privacy protections to prevent unauthorized access and data breaches. Advances in cryptography, such as homomorphic encryption, offer potential solutions by allowing data to be stored and computed on in encrypted form, without the need for decryption keys (Dowlin et al., 2017). This approach enables researchers to outsource data storage to untrusted clouds while maintaining data privacy. Additionally, the integration of ethical guidelines and best practices is crucial to ensure the responsible use of bioinformatics tools and data, particularly as the field continues to evolve with new technologies and methodologies (Shahid, 2023).
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