16 papers · ranked by Valyu relevance
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…
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…
Yaqing Liu, Qingwang Chen, Qiaochu Chen, Leqing Sang + 5 more
Advances in precision medicine rely on the accurate identification and analysis of molecular alterations for personalized diagnostic, prognostic, and therapeutic decision-making. A critical obstacle is the integration of heterogeneous interpretations of clinically actionable alterations from various knowledgebases.…
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…
Raghunandan Wable, Achuth Suresh Nair, Anirudh Pappu, Widnie Pierre-Louis + 6 more
Timely understanding of biological secrets of complex diseases will ultimately benefit millions of individuals by reducing the high risks for mortality and improving the quality of life with personalized diagnoses and treatments. Due to the advancements in sequencing technologies and reduced cost, genomics data is…
Shuangxia Ren, Yifeng Tao, Ke Yu, Yifan Xue + 2 more
Application of artificial intelligence (AI) in precision oncology typically involves predicting whether the cancer cells of a patient (previously unseen by AI models) will respond to any of a set of existing anticancer drugs, based on responses of previous training cell samples to those drugs. To expand the repertoire…
Gal Dinstag, Eldad D. Shulman, Efrat Elis, Doreen S. Ben-Zvi + 27 more
Precision oncology is gradually advancing into mainstream clinical practice, demonstrating significant survival benefits. However, eligibility and response rates remain limited in many cases, calling for better predictive biomarkers. We present ENLIGHT, a transcriptomics-based computational approach that identifies…
Payal Chandak, Kexin Huang, Marinka Zitnik
Developing personalized diagnostic strategies and targeted treatments requires a deep understanding of disease biology and the ability to dissect the relationship between molecular and genetic factors and their phenotypic consequences. However, such knowledge is fragmented across publications, non-standardized research…
Kristi Lin-Rahardja, Jessica Scarborough, Jacob G Scott
Gene expression signatures predictive of chemotherapeutic response have the potential to greatly extend the reach of precision medicine by allowing medical providers to plan treatment regimens on an individual basis for patients with and without actionable mutations. Most published gene signatures are only capable of…
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…
Rida Nasir Butt, Bibi Amina, Muhammad Umer Sultan, Zain Bin Tanveer + 8 more
Computational modeling and analysis of biomolecular network models annotated with cancer patient-specific multi-omics data can enable the development of personalized therapies. Current endeavors aimed at employing in silico models towards personalized cancer therapeutics remain to be fully translated. In this work, we…
Muhammad Dawood, Quoc Dang Vu, Lawrence S. Young, Kim Branson + 3 more
Drug sensitivity prediction models can aid in personalising cancer therapy, biomarker discovery, and drug design. Such models require survival data from randomized controlled trials which can be time consuming and expensive. In this proof-of-concept study, we demonstrate for the first time that deep learning can link…
Wei He, Matthew D. McCoy, Chen-Hsiang Yeang, Rebecca B. Riggins + 1 more
Cancers exhibit genetic diversity between individual cancer cells. Previous work shows greater diversity than heretofore expected (1) and that also increases more quickly during a patient’s clinical course than previously thought (1, 2). Rare subclones will harbor pre-existing resistance to any single agent and may…
Petr Smirnov, Sisira Kadambat Nair, Farnoosh Abbas-Aghababazadeh, Nikta Feizi + 3 more
Preclinical pharmacogenomic studies provide an opportunity to discover novel biomarkers for drug response. However, pharamcogenomic studies linking gene expression profiles to drug response do not always agree on the significance or strength of biomarkers. We apply a statistical meta-analysis approach to 7 large…
Xiaoxi Li, Ling Liu, Yuanyuan Yan, Mengfang Yang + 3 more
Tumor heterogeneity plays a critical role in tumor relapse and the development of drug resistance. Current personalized strategies, based on genetic profiling or bulk tumor drug sensitivity testing, offer limited clinical value as they overlook clonal heterogeneity. Here, through establishing primary tumor cell…
Banabithi Bose, Barbara Stranger, Serdar Bozdag
The widespread availability of multi-omics tumor profiling has enabled detailed molecular characterization of individual tumors, paving the way for more effective, less toxic, and patient-specific therapies. However, widespread compound screens in human patients are constrained by ethical and logistical challenges…