Precision medicine and oncology: an overview of the opportunities presented by next-generation sequencing and big data and the challenges posed to conventional drug development and regulatory approval pathways

Doherty, Margaret M.; Metcalfe, Thomas; Guardino, Ellie; Peters, Eric; Ramage, L.

doi:10.1093/annonc/mdw165

Cited by 16 publications

(5 citation statements)

References 8 publications

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“…Finally, the ethical use of ML in cancer diagnosis and treatment requires stringent measures to safeguard patient privacy and ensure data security. These should include anonymization, encryption, and transparency to guarantee that the identities and health information of patients remain protected 153 . This is also important for the regulatory approval of tools integrating ML algorithms using microbiome data.…”

Section: Translation To Clinical Practice and Ethical Concernsmentioning

confidence: 99%

A review of machine learning methods for cancer characterization from microbiome data

Teixeira,

Silva,

Ferreira

et al. 2024

npj Precis. Onc.

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Recent studies have shown that the microbiome can impact cancer development, progression, and response to therapies suggesting microbiome-based approaches for cancer characterization. As cancer-related signatures are complex and implicate many taxa, their discovery often requires Machine Learning approaches. This review discusses Machine Learning methods for cancer characterization from microbiome data. It focuses on the implications of choices undertaken during sample collection, feature selection and pre-processing. It also discusses ML model selection, guiding how to choose an ML model, and model validation. Finally, it enumerates current limitations and how these may be surpassed. Proposed methods, often based on Random Forests, show promising results, however insufficient for widespread clinical usage. Studies often report conflicting results mainly due to ML models with poor generalizability. We expect that evaluating models with expanded, hold-out datasets, removing technical artifacts, exploring representations of the microbiome other than taxonomical profiles, leveraging advances in deep learning, and developing ML models better adapted to the characteristics of microbiome data will improve the performance and generalizability of models and enable their usage in the clinic.

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Section: Translation To Clinical Practice and Ethical Concernsmentioning

confidence: 99%

A review of machine learning methods for cancer characterization from microbiome data

Teixeira,

Silva,

Ferreira

et al. 2024

npj Precis. Onc.

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“…At present, a large number of surgical videos can be quantitatively analyzed through machine learning, using computer vision principles to achieve automatic, real-time analysis and segmentation of surgical procedures. Sharing these videos with other surgeons can also help to improve their surgical skills or for educational purposes [15][16][17][18].…”

Section: Medical Big Data Optimize the Diagnosis And Treatment Stratementioning

confidence: 99%

Precise hepatectomy in the intelligent digital era

Chen¹,

He²,

Jia³

2020

Int. J. Biol. Sci.

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In the past 20 years, the concept of surgery has undergone profound changes. Surgical practice has shifted from emphasizing the complete elimination of lesions to achieving optimal rehabilitation in patients. Collaborative optimization of surgery consists of three core elements, removal of lesions, organ protection and injury close monitoring, and controlled surgical intervention. As a result, the traditional surgical paradigm has quietly transformed into a modern precision surgical paradigm. In this review, we summarized the latest breakthroughs and applications of precision medicine in liver surgery. In addition, we also outlined the progresses that have been made in precision liver surgery, the opportunities and challenges that may encountered in the future.

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“…Precision medicine needs to have novel methods to take use of the multiple dimensional big data which is often heterogeneous and unstructured. The synergic factors for CCA pathogenesis are needed to be precisely interconnected for the biomarker and risk factor screening 73 - 75 . Nowadays, with the accumulation of biomedical big data, many bioinformatics methods were developed for personalized and precision medicine of cancer, such as the precision classification and the immune treatment of lung cancer based on their gene expression profiles 76 - 78 .…”

Section: Present Status Of Bioinformatics Applications In Cholanogiocmentioning

confidence: 99%

Translational Bioinformatics for Cholangiocarcinoma: Opportunities and Challenges

et al. 2018

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Translational bioinformatics is becoming a driven force and a new scientific paradigm for cancer research in the era of big data. To promote the cross-disciplinary communication and research, we take cholangiocarcinoma as an example to review the present status and the future perspectives of the bioinformatics models applied in cancer study. We first summarize the present application of computational methods to the study of cholangiocarcinoma ranged from pattern recognition of biological data, knowledge based data annotation to systems biological level modeling and clinical translation. Then the future opportunities and challenges about database or knowledge base building, novel model developing and molecular mechanism exploring as well as the intelligent decision supporting system construction for the precision diagnosis, prognosis and treatment of cholangiocarcinoma are discussed.

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Precision medicine and oncology: an overview of the opportunities presented by next-generation sequencing and big data and the challenges posed to conventional drug development and regulatory approval pathways

Cited by 16 publications

References 8 publications

A review of machine learning methods for cancer characterization from microbiome data

A review of machine learning methods for cancer characterization from microbiome data

Precise hepatectomy in the intelligent digital era

Translational Bioinformatics for Cholangiocarcinoma: Opportunities and Challenges

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