Machine learning in onco-pharmacogenomics: a path to precision medicine with many challenges

Frontiers in Pharmacology, Journal Year: 2024, Volume and Issue: 14

Published: Jan. 9, 2024

Over the past two decades, Next-Generation Sequencing (NGS) has revolutionized approach to cancer research. Applications of NGS include identification tumor specific alterations that can influence pathobiology and also impact diagnosis, prognosis therapeutic options. Pharmacogenomics (PGx) studies role inheritance individual genetic patterns in drug response taken advantage technology as it provides access high-throughput data can, however, be difficult manage. Machine learning (ML) recently been used life sciences discover hidden from complex solve various PGx problems. In this review, we provide a comprehensive overview approaches employed different implicating use data. We an excursus ML algorithms exert fundamental strategies field improve personalized medicine cancer.

Language: Английский

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Role of Exosomes in Cancer and Aptamer-Modified Exosomes as a Promising Platform for Cancer Targeted Therapy

Biological Procedures Online, Journal Year: 2024, Volume and Issue: 26(1)

Published: May 27, 2024

Abstract Exosomes are increasingly recognized as important mediators of intercellular communication in cancer biology. can be derived from cells well cellular components tumor microenvironment. After secretion, the exosomes carrying a wide range bioactive cargos ingested by local or distant recipient cells. The released act through variety mechanisms to elicit multiple biological effects and impact most if not all hallmarks cancer. Moreover, owing their excellent biocompatibility capability being easily engineered modified, currently exploited promising platform for targeted therapy. In this review, we first summarize current knowledge roles risk etiology, initiation progression cancer, underlying molecular mechanisms. aptamer-modified exosome therapy is then briefly introduced. We also discuss future directions emerging biology perspective

Language: Английский

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Role of Artificial Intelligence in Drug Discovery and Target Identification in Cancer

Current Drug Delivery, Journal Year: 2023, Volume and Issue: 21(6), P. 870 - 886

Published: Sept. 6, 2023

Abstract: Drug discovery and development (DDD) is a highly complex process that necessitates precise monitoring extensive data analysis at each stage. Furthermore, the DDD both timeconsuming costly. To tackle these concerns, artificial intelligence (AI) technology can be used, which facilitates rapid of datasets within limited timeframe. The pathophysiology cancer disease complicated requires research for novel drug development. first stage in involves identifying targets. Cell structure molecular functioning are due to vast number molecules function constantly, performing various roles. scientists continually discovering cellular mechanisms molecules, expanding range potential Accurately correct target crucial step preparation treatment strategy. Various forms AI, such as machine learning, neural-based deep network-based currently being utilised applications, online services, databases. These technologies facilitate identification validation targets, ultimately contributing success projects. This review focuses on different types subcategories AI databases field cancer.

Language: Английский

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Early detection of hepatocellular carcinoma via no end-repair enzymatic methylation sequencing of cell-free DNA and pre-trained neural network

Genome Medicine, Journal Year: 2023, Volume and Issue: 15(1)

Published: Nov. 8, 2023

Abstract Background Early detection of hepatocellular carcinoma (HCC) is important in order to improve patient prognosis and survival rate. Methylation sequencing combined with neural networks identify cell-free DNA (cfDNA) carrying aberrant methylation offers an appealing non-invasive approach for HCC detection. However, some limitations exist traditional technologies models, which may impede their performance the read-level HCC. Methods We developed a low damage high-fidelity method called No End-repair Enzymatic Methyl-seq (NEEM-seq). further model DeepTrace that can better HCC-derived reads through pre-trained fine-tuned network. After pre-training on 11 million from NEEM-seq, was using 1.2 tumor tissue after noise reduction, 2.7 non-tumor cfDNA. validated data 130 individuals cfDNA whole-genome NEEM-seq at around 1.6X depth. Results overcomes drawbacks enzymatic methods by avoiding introduction unmethylation errors outperformed other models identifying detecting individuals. Based cfDNA, our showed high accuracy 96.2%, sensitivity 93.6%, specificity 98.5% validation cohort consisting 62 patients, 48 liver disease 20 healthy In early stage (BCLC 0/A TNM I), 89.6 89.5% respectively, outperforming Alpha Fetoprotein (AFP) much lower both BCLC (50.5%) I (44.7%). Conclusions By combining model, has great potential specificity, making it potentially suitable clinical applications. DeepTrace: https://github.com/Bamrock/DeepTrace

Language: Английский

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Onco-Breastomics: An Eco-Evo-Devo Holistic Approach

International Journal of Molecular Sciences, Journal Year: 2024, Volume and Issue: 25(3), P. 1628 - 1628

Published: Jan. 28, 2024

Known as a diverse collection of neoplastic diseases, breast cancer (BC) can be hyperbolically characterized dynamic pseudo-organ, living organism able to build complex, open, hierarchically organized, self-sustainable, and self-renewable tumor system, population, species, local community, biocenosis, or an evolving dynamical ecosystem (i.e., immune metabolic ecosystem) that emphasizes both developmental continuity spatio-temporal change. Moreover, cell also known oncobiota, has been described non-sexually reproducing well migratory invasive species expresses intelligent behavior, endangered parasite fights survive, optimize its features inside the host’s ecosystem, is exploit disrupt host circadian cycle for improving own proliferation spreading. BC tumorigenesis compared with early embryo placenta development may suggest new strategies research therapy. Furthermore, environmental disease ecological disorder. Many mechanisms progression have explained by principles ecology, biology, evolutionary paradigms. authors discussed ecological, developmental, more successful anti-cancer therapies, understanding bases exploitable vulnerabilities. Herein, we used integrated framework three theories: Bronfenbrenner’s theory human development, Vannote’s River Continuum Concept (RCC), Ecological Evolutionary Developmental Biology (Eco-Evo-Devo) theory, explain understand several eco-evo-devo-based govern progression. Multi-omics fields, taken together onco-breastomics, offer better opportunities integrate, analyze, interpret large amounts complex heterogeneous data, such various big-omics data obtained multiple investigative modalities, drive treatment. These integrative eco-evo-devo theories help clinicians diagnose treat BC, example, using non-invasive biomarkers in liquid-biopsies emerged from omics-based accurately reflect biomolecular landscape primary order avoid mutilating preventive surgery, like bilateral mastectomy. From perspective preventive, personalized, participatory medicine, these hypotheses patients think about this process governed natural rules, possible causes disease, gain control on their health.

Language: Английский

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