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Papers on “personalized precision medicine genomics”

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1. The Penn Medicine BioBank: Towards a Genomics-Enabled Learning Healthcare System to Accelerate Precision Medicine in a Diverse Population

   A. Verma, S. Damrauer, Nawar Naseer, et al. · 2022 · Journal of Personalized Medicine · 134 cites

   The Penn Medicine BioBank (PMBB) is an electronic health record (EHR)-linked biobank at the University of Pennsylvania (Penn Medicine). A large variety of health-related information, ranging from diagnosis codes to laboratory measurements, imaging data and lifestyle information, is integrated with genomic and biomarker data in the PMBB to facilitate discoveries and translational science. To date, 174,712 participants have been enrolled into the PMBB, including approximately 30% of participants of non-European ancestry, making it one of the most diverse medical biobanks. There is a median of seven years of longitudinal data in the EHR available on participants, who also consent to permission

2. Precision medicine, genomics and drug discovery.

   L. Cardon, Tim Harris · 2016 · Human molecular genetics · 56 cites

3. Unsupervised Learning in Precision Medicine: Unlocking Personalized Healthcare through AI

   A. Trezza, Anna Visibelli, B. Roncaglia, et al. · 2024 · Applied Sciences · 40 cites

   Integrating Artificial Intelligence (AI) into Precision Medicine (PM) is redefining healthcare, enabling personalized treatments tailored to individual patients based on their genetic code, environment, and lifestyle. AI’s ability to analyze vast and complex datasets, including genomics and medical records, facilitates the identification of hidden patterns and correlations, which are critical for developing personalized treatment plans. Unsupervised Learning (UL) is particularly valuable in PM as it can analyze unstructured and unlabeled data to uncover novel disease subtypes, biomarkers, and patient stratifications. By revealing patterns that are not explicitly labeled, unsupervised algorit

4. Personalized anesthesia and precision medicine: a comprehensive review of genetic factors, artificial intelligence, and patient-specific factors

   Shiyue Zeng, Qi Qing, Wei Xu, et al. · 2024 · Frontiers in Medicine · 40 cites

   Precision medicine, characterized by the personalized integration of a patient’s genetic blueprint and clinical history, represents a dynamic paradigm in healthcare evolution. The emerging field of personalized anesthesia is at the intersection of genetics and anesthesiology, where anesthetic care will be tailored to an individual’s genetic make-up, comorbidities and patient-specific factors. Genomics and biomarkers can provide more accurate anesthetic protocols, while artificial intelligence can simplify anesthetic procedures and reduce anesthetic risks, and real-time monitoring tools can improve perioperative safety and efficacy. The aim of this paper is to present and summarize the applic

5. PRECISION MEDICINE AND GENOMICS: A COMPREHENSIVE REVIEW OF IT-ENABLED APPROACHES

   Francisca Chibugo Udegbe, Ogochukwu Roseline Ebulue, Charles Chukwudalu Ebulue, et al. · 2024 · International Medical Science Research Journal · 29 cites

    This review delves into Information Technology's (IT) transformative impact on precision medicine and genomics, spotlighting the pivotal role of bioinformatics, data mining, machine learning, and blockchain technologies in advancing personalized healthcare. A comprehensive analysis outlines how these IT-enabled approaches facilitate the analysis, interpretation, and application of vast genomic data sets, thereby enhancing disease prediction, diagnosis, and treatment on an individual level. Despite the promising advancements, the review also addresses significant challenges, including data complexity, interoperability, ethical considerations, and the digital divide, underscoring the necessit

6. Multi-omics in Allergic Rhinitis: Mechanism Dissection and Precision Medicine

   Yan Hao, Yu-Juan Yang, Hong-Fei Zhao, et al. · 2025 · Clinical Reviews in Allergy & Immunology · 29 cites

   Allergic rhinitis (AR) is a common chronic inflammatory airway disease caused by inhaled allergens, and its prevalence has increased in recent decades. AR not only causes nasal leakage, itchy nose, nasal congestion, sneezing, and allergic conjunctivitis but also induces asthma, as well as sleep disorders, anxiety, depression, memory loss, and other phenomena that seriously affect the patient’s ability to study and work, lower their quality of life, and burden society. The current methods used to diagnose and treat AR are still far from ideal. Multi-omics technology can be used to comprehensively and systematically analyze the differentially expressed DNA, RNA, proteins, and metabolites and t

7. Large Language Models in Genomics—A Perspective on Personalized Medicine

   Shahid Ali, Yazdan Ahmad Qadri, Khursheed Ahmad, et al. · 2025 · Bioengineering · 21 cites

   Integrating artificial intelligence (AI), particularly large language models (LLMs), into the healthcare industry is revolutionizing the field of medicine. LLMs possess the capability to analyze the scientific literature and genomic data by comprehending and producing human-like text. This enhances the accuracy, precision, and efficiency of extensive genomic analyses through contextualization. LLMs have made significant advancements in their ability to understand complex genetic terminology and accurately predict medical outcomes. These capabilities allow for a more thorough understanding of genetic influences on health issues and the creation of more effective therapies. This review emphasi

8. Incorporating Novel Technologies in Precision Oncology for Colorectal Cancer: Advancing Personalized Medicine

   P. Ahluwalia, Kalyani Ballur, Tiffanie Leeman, et al. · 2024 · Cancers · 21 cites

   Simple Summary Cancer affects millions of individuals every year, with colorectal cancer being among the most common. There is an increased need to identify new biomarkers that can not only diagnose patients early, but also stratify them so the best treatment can be initiated for each patient. Every human has a unique genetic makeup that causes them to respond differently to cancer. In recent years, new technologies have provided unprecedented access to tumor samples from patients. Through these analyses, we can not only diagnose and classify patients based on their comparative risk, but also monitor their response to emerging therapies. Continued progress using these methods will transform