Computational Molecular Biology 2024, Vol.14, No.4, 163-172 http://bioscipublisher.com/index.php/cmb 167 Figure 2 Schematic representation of the LSTM-AE, used for biomedical text clustering, where individual drug review texts are embedded using word2vec before feeding as a sequence (Adopted from Karim et al., 2020) 5.2 Advanced visualization techniques Advanced visualization techniques are essential for interpreting high-dimensional data. Techniques such as tensor-based representations and hierarchical clustering have been used to visualize and organize large-scale molecular biophysics data, providing insights into the intrinsic multiscale structure of the data (Ramanathan et al., 2015). These visualization methods help in understanding complex datasets and identifying patterns that may not be apparent through traditional analysis methods. 5.3 Integration of multi-omics data The integration of multi-omics data is crucial for a comprehensive understanding of complex biological systems. Knowledge-guided statistical learning methods that incorporate biological knowledge, such as functional genomics and proteomics, have been developed to improve prediction and classification accuracy in precision oncology (Zhao et al., 2019). These methods enable the analysis of multifactorial diseases like cancer by aggregating weak signals from individual genes into stronger pathway-level signals, making it easier to detect significant changes. 6 Case Studies of Biostatistical Applications 6.1 Genomics and transcriptomics data analysis The analysis of high-dimensional genomics and transcriptomics data presents unique challenges and opportunities in the field of biostatistics. High-throughput techniques have enabled the rapid generation of vast amounts of data, necessitating advanced methods for effective analysis and integration. One significant approach is the quantitative analysis and integration of multi-omics data, which includes genomics, transcriptomics, proteomics, and metabolomics (Ding et al., 2024). This multi-omics approach provides a comprehensive perspective on biological systems, but it also introduces challenges related to data heterogeneity and batch effects. Methods such as network analysis and biological contextualization are employed to address these challenges and enhance the understanding of complex biological relationships (Misra et al., 2019; Wörheide et al., 2021). In precision oncology, knowledge-guided statistical learning methods have been developed to improve the analysis of high-dimensional -omics data. These methods incorporate biological knowledge, such as functional genomics and proteomics, to enhance prediction and classification accuracy. This approach is particularly useful in identifying weak signals in important pathways, which can be aggregated to detect stronger signals and yield biologically interpretable results (Kaur et al., 2021). Another innovative method is the multi-objective chaotic emperor penguin optimization (MOCEPO) algorithm, which is designed for feature selection and cancer classification in high-dimensional genomics data. This algorithm aims to minimize the number of selected genes while maximizing classification accuracy, demonstrating superior performance compared to existing methods (Zhao et al., 2019).
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