HARNESSING THE POWER OF AI AND MACHINE LEARNING FOR NEXT-GENERATION SEQUENCING DATA ANALYSIS: A COMPREHENSIVE REVIEW OF APPLICATIONS, CHALLENGES, AND FUTURE DIRECTIONS IN PRECISION ONCOLOGY

: This comprehensive review discusses the transformative impact of artificial intelligence (AI) and machine learning (ML) on next-generation sequencing (NGS) data analysis in precision oncology. AI and ML are pivotal in enhancing the precision of variant calling, annotating complex genomic data, discovering and validating new biomarkers, and integrating multi-omics data to elucidate cancer complexities. These technologies facilitate advanced clinical decision support systems that improve diagnostic accuracy and treatment efficacy. Despite these advancements, challenges persist, including the need for model interpretability, validation across diverse datasets, and integration into clinical practice. Future directions involve federated learning for data privacy, integration of multi-modal data, and expansion of explainable AI applications to enhance understanding and trust in AI-driven methodologies.


INTRODUCTION
The advent of next-generation sequencing (NGS) technologies has revolutionized precision oncology by enabling the generation of vast amounts of high-dimensional genomic data (1).However, the analysis and interpretation of these complex datasets pose significant challenges, necessitating the development of advanced computational tools (2).Artificial 1 UNIFOA. 2 UNIFIP. 3Faculdade Santa Marcelina, 4 Santa Casa de Misericórdia de Vitoria Higher School of Sciences -EMESCAMB.Molecular Dynamics and Modeling Laboratory (DynMolLab). 5Molecular Dynamics and Modeling Laboratory (DynMolLab) -Santa Luíza -Vitória -ES -Brazil.intelligence (AI) and machine learning (ML) have emerged as powerful approaches to tackle these challenges, offering novel insights into the molecular underpinnings of cancer (3).This narrative review aims to provide a comprehensive overview of AI and ML's applications, challenges, and future directions in NGS data analysis for precision oncology.

METHODOLOGY
A systematic literature search was conducted using multiple databases, including Scopus, Web of Science, PubMed, IEEE Xplore, ScienceDirect, Directory of Open Access Journals (DOAJ), and JSTOR.The search terms included combinations of "artificial intelligence," "machine learning," "next-generation sequencing," "precision oncology," "cancer genomics," and related keywords.Articles published between 2015 and 2023 were considered for inclusion.The retrieved articles were screened for relevance, and the most pertinent studies were selected for in-depth analysis and synthesis.

Variant Calling and Annotation
AI and ML algorithms have significantly improved the accuracy and efficiency of variant calling and annotation in NGS data (4).Deep learning-based approaches, such as DeepVariant (5) and Clairvoyant (6), have demonstrated superior performance to traditional methods, particularly in detecting low-frequency variants and complex genomic regions.These advancements have not only enabled the identification of clinically relevant mutations but also facilitated the development of targeted therapies, which are tailored to the specific genetic makeup of a patient's cancer, thereby improving treatment outcomes (7).
Biomarker Discovery and Validation ML algorithms have been extensively applied to identify and validate novel biomarkers from NGS data (8).Unsupervised learning techniques, such as clustering and dimensionality reduction, have been employed to uncover distinct molecular subtypes and prognostic signatures (9).Supervised learning methods, including support vector machines and random forests, have been used to develop predictive models for treatment response and patient stratification (10).These AI-driven approaches have not just accelerated the discovery of clinically actionable biomarkers, but also facilitated the implementation of personalized treatment strategies, inspiring a new wave of progress in the field (11).

Integration of Multi-Omics Data
The integration of multi-omics data, encompassing genomics, transcriptomics, proteomics, and metabolomics, provides a comprehensive view of the molecular landscape of cancer (12).AI and ML techniques have been instrumental in integrating and analyzing these diverse datasets to unravel the complex interplay between different molecular layers (13).
Graph-based methods, such as network analysis and pathway enrichment, have been employed to identify essential driver genes and dysregulated pathways (14).Deep learning architectures, including autoencoders and generative adversarial networks, have been used to learn joint representations and predict the functional impact of genetic alterations (15).

AI-powered clinical decision support systems (CDSSs) have emerged as valuable tools
for integrating NGS data with clinical information to guide treatment decisions (16).These systems leverage ML algorithms to provide personalized treatment recommendations based on a patient's genomic profile, clinical characteristics, and treatment history (17).CDSSs have been shown to improve the accuracy and efficiency of treatment selection, reduce adverse events, and enhance overall patient outcomes (18).The integration of AI and ML with electronic health records and clinical trial databases has further expanded the capabilities of CDSSs in precision oncology (19).

DISCUSSION
The application of AI and ML in NGS data analysis has significantly advanced the field of precision oncology, enabling the identification of clinically relevant mutations, biomarkers, and therapeutic targets (20).However, several challenges remain to be addressed.
The interpretability and explainability of AI and ML models are crucial for building trust and facilitating clinical adoption (21).Rigorous validation and benchmarking of these models across diverse patient populations and cancer types are necessary to ensure their generalizability and robustness (22).Furthermore, integrating AI and ML into clinical workflows requires close collaboration between computational scientists, clinicians, and regulatory bodies to establish standardized guidelines and best practices (23).
Future directions in this field include the development of federated learning approaches to enable the analysis of distributed datasets while preserving patient privacy (24).The incorporation of multi-modal data, such as imaging and electronic health records, into AI and ML frameworks will provide a more comprehensive understanding of cancer biology and patient outcomes (25).The application of explainable AI techniques, such as attention mechanisms and knowledge graphs, will enhance the interpretability and trustworthiness of AI-driven insights (26).Additionally, the integration of AI and ML with emerging technologies, such as single cell sequencing and spatial transcriptomics, will not just enable the exploration of intratumor heterogeneity and the tumor microenvironment at unprecedented resolution, but also open up exciting new possibilities in precision oncology.(27).CONCLUSION AI and ML have not just revolutionized the analysis of NGS data in precision oncology, but also opened new possibilities.They have enabled the identification of clinically relevant mutations, biomarkers, and therapeutic targets and facilitated the integration of multi-omics data, the development of clinical decision support systems, and the personalization of treatment strategies.As we address the challenges related to interpretability, validation, and clinical integration, we are on the brink of fully realizing the potential of AI and ML in precision oncology.Future directions, including federated learning, multi-modal data integration, explainable AI, and the incorporation of emerging technologies, will further advance the field and improve patient outcomes.As AI and ML evolve, their impact on precision oncology will undoubtedly grow, paving the way for a new era of data-driven, personalized cancer care.