27 papers · ranked by Valyu relevance
Richard C. Wang, Zhixiang Wang, David Wong
Simple Summary The genomics-based concept of precision medicine began to emerge following the completion of the Human Genome Project. In contrast to evidence-based medicine, precision medicine will allow doctors and scientists to tailor the treatment of different subpopulations of patients who differ in their…
Mikael Dolsten, Morten Søgaard
Pharmaceutical R&D productivity has declined over the last decade despite increasing investments. Recent trends, however, indicate a potential reversal of this trend fueled by a wave of new biologics, vaccines and highly selective NCEs directed against targets validated by human genetics, focus on new disease areas…
Marcio Nakanishi
Precision Medicine has been a growing topic of discussion in the last decade. More than 17,000 articles have been published on this subject only in the last 5 years. In 2015, the US National Institutes of Health launched the Precision Medicine Initiative, which defines it as “an emerging approach to disease prevention…
Xiangdong Wang
Precision medicine has been initiated and gains more and more attention from preclinical and clinical scientists. A number of key elements or critical parts in precision medicine have been described and emphasized to establish a systems understanding of precision medicine. The principle of precision medicine is to…
Habiba Abdelhalim, Asude Berber, Mudassir Lodi, Rihi Jain + 22 more
Precision medicine has greatly aided in improving health outcomes using earlier diagnosis and better prognosis for chronic diseases. It makes use of clinical data associated with the patient as well as their multi-omics/genomic data to reach a conclusion regarding how a physician should proceed with a specific…
Muhammad Afzal, S. M. Riazul Islam, Maqbool Hussain, Sungyoung Lee
This research was supported by the Ministry of Science and ICT (MSIT), Korea, under the Information Technology Research Center (ITRC) support program (IITP-2017-0-01629) supervised by the Institute of Information & Communications Technology Planning & Evaluation (IITP). This work was supported by an IITP grant funded…
Wanxin Duan, Mingjie Wang, Yifei Liu, Celine Desoyer + 2 more
Precision medicine has evolved through distinct phases, from the origins of the Human Genome Project to mutation-based targeted therapies. This editorial posits that “stereological cell biomedicine” could be a new approach promoting the development of the next generation of precision medicine. This emerging discipline…
Hsin-Chou Yang, Pui-Yan Kwok, Ling-Hui Li, Yi-Min Liu + 139 more
The Taiwan Precision Medicine Initiative (TPMI), a project initiated by the Academia Sinica in collaboration with 16 major medical centers around Taiwan, has recruited 565,390 participants who consented to provide DNA samples for genetic profiling and grant access to their electronic medical records (EMR) for studies…
Claire Bellis, Gabriel Kolle, Jacklyn Yong, Maxime Hebrard + 28 more
Precision medicine (PM) research in recent years has witnessed a remarkable surge in large-scale population genomics programs. In 2017, Singapore initiated the National Precision Medicine (NPM) program, a three-phase national strategy driving PM research, innovation, and enterprise capitalizing on Singapore’s unique…
Roy C. Ziegelstein
Clinical practice guidelines have been developed for many common conditions based on data from randomized controlled trials. When medicine is informed solely by clinical practice guidelines, however, the patient is not treated as an individual, but rather as member of a group. Precision medicine, as defined herein…
Nasim Sadat Mosavi, Manuel Filipe Santos
Research is ongoing all over the world for identifying the barriers and finding effective solutions to accelerate the projection of Precision Medicine (PM) in the healthcare industry. Yet there has not been a valid and practical model to tackle the several challenges that have slowed down the widespread of this…
Chloé-Aga, Azencott
Machine learning can have major societal impact in computational biology applications. In particular, it plays a central role in the development of precision medicine, whereby treatment is tailored to the clinical or genetic features of the patient. However, these advances require collecting and sharing among…
Brett K. Beaulieu‐Jones
Research is a tertiary priority in the EHR, where the priorities are patient care and billing. Because of this, the data is not standardized or formatted in a manner easily adapted to machine learning approaches. Data may be missing for a large variety of reasons ranging from individual input styles to differences in…
Jayaram Kancherla, Shruti Rao, Krithika Bhuvaneshwar, Rebecca B. Riggins + 4 more
In this work, we introduce CDGnet, an evidence-based network approach for recommending targeted cancer therapies, available as a user-friendly informatics tool. Our approach can be used to expand the range of options of targeted therapies for cancer patients who undergo molecular profiling. It considers biological…
Manish Kumar
Cancer is a complex disease having a number of composite problems to be considered including cancer immune evasion, therapy resistance, and recurrence for a cure. Fundamentally, it remains a genetic disease as diverse aspects of the complexity of tumor growth and cancer development relate to its genetic machinery and…
Saba Ahmadi, Pattara Sukprasert, Natalie Artzi, Samir Khuller + 2 more
The availability of single-cell transcriptomics data opens up new opportunities for designing combination cancer treatments. Mining such data, we employed combinatorial optimization to explore the landscape of optimal combination therapies in solid tumors (including brain, head and neck, melanoma, lung, breast and…
Sanju Sinha, Rahulsimham Vegesna, Saugato Rahman Dhruba, Wei Wu + 10 more
Tailoring the best treatments to cancer patients is an important open challenge. Here, we build a precision oncology data science and software framework for PERsonalized single-Cell Expression-based Planning for Treatments In Oncology (PERCEPTION). Our approach capitalizes on recently published matched bulk and…
Lingli Wang, Rumeng Qu, Qialing Huang, Min Hu + 1 more
Tumor heterogeneity highlights the necessity of precision cancer medicine, making the evaluation and screening of anticancer drugs a core challenge in cancer therapy. However, current cell-based efficacy assessment methods struggle to quantify the holistic impact of drugs on cellular behavior through specific target…
Prabhjot S. Mundi, Filemon S. Dela Cruz, Adina Grunn, Daniel Diolaiti + 47 more
Predicting tumor sensitivity to antineoplastics remains an elusive challenge, with no methods demonstrating predictive power. Joint analysis of tumors—from patients with distinct malignancies who had progressed on multiple lines of therapy—and drug perturbation transcriptional profiles predicted sensitivity to 28 of…
JunBo Wu, Nathaniel Comfort
Public health is the most recent of the biomedical sciences to be seduced by the trendy moniker "precision." Advocates for "precision public health" (PPH) call for a data-driven, computational approach to public health, leveraging swaths of genomic "big data" to inform public health decision-making. Yet, like precision…
Authors not listed
This conceptual framework proposes an out-of-the-box approach to innovate non-biological drugs that surpass biologics by 200-fold in efficacy and safety for cancer treatment. Integrating advanced paradigms from physics, chemistry, medicine, biology, engineering, and materials science, we delineate a multi-dimensional…
Authors not listed
Machine learning holds significant promise for accelerating biomarker discovery in clinical proteomics, yet its real-world impact remains limited by widespread methodological pitfalls and unrealistic expectations. In this perspective, we critically examine the integration of machine learning into clinical proteomics…
Authors not listed
Integrating machine learning (ML) into drug discovery has ushered in a new era of innovation, dramatically enhancing the efficiency and precision of identifying and developing new therapeutics. This review provides a comprehensive analysis of the current applications of machine learning in drug discovery, focusing on…
Janet Sasso, Barbara Ambrose, Rumiana Tenchov, Ruchira Datta + 3 more
In the last decade, there has been a shift in research, clinical development, and commercial activity to exploit the many roles of RNA in physiology for use in medicine. With the rapid success in the development of lipid-RNA nanoparticles for mRNA vaccines against COVID-19 and with several approved RNA-based drugs, RNA…
Juerg Straubhaar, Alexandria D’Souza, Zachary Niziolek, Bogdan Budnik
Single-cell analysis has clearly established itself in biology and biomedical fields as an invaluable tool that allows one to comprehensively understand the relationship between cells, including their types, states, transitions, trajectories, and spatial position. Scientific methods such as fluorescence labeling…
Norberto Sánchez-Cruz, Jose L. Medina-Franco
Epigenetic targets are a significant focus for drug discovery research, as demonstrated by the eight approved epigenetic drugs for treatment of cancer and the increasing availability of chemogenomic data related to epigenetics. This data represents a large amount of structure-activity relationships that has not been…
Norberto Sánchez-Cruz, Jose L. Medina-Franco
Motivation: The identification of protein targets of small molecules is essential for drug discovery. With the increasing amount of chemogenomic data in the public domain, multiple ligand-based models for target prediction have emerged. However, these models are generally biased by the number of known ligands for…