ArticlePersonalized medicine2015
Integrative network modeling approaches to personalized cancer medicine.
Article in Personalized medicine, 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
7 citing papers in PubMed.
- Adenosine Metabolism: Emerging Concepts for Cancer Therapy.Cancer cell · 2019Review
- AI in Health: State of the Art, Challenges, and Future Directions.Yearbook of medical informatics · 2019Review
- Integrative Approaches to Cancer Immunotherapy.Trends in cancer · 2019Review
- Systematic analyses of drugs and disease indications in RepurposeDB reveal pharmacological, biological and epidemiological factors influencing drug repositioning.Briefings in bioinformatics · 2018Article
- Overcoming multiple myeloma drug resistance in the era of cancer 'omics'.Leukemia & lymphoma · 2018Review
- Cancer Immunotherapy and Personalized Medicine: Emerging Technologies and Biomarker-Based Approaches.Journal of molecular biomarkers & diagnosis · 2017Article
- Network-based machine learning and graph theory algorithms for precision oncology.NPJ precision oncology · 2017Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
5 authors.
Funding
Abstract
The ability to collect millions of molecular measurements from patients is a now a reality for clinical medicine. This reality has created the challenge of how to integrate these vast amounts of data into models that accurately predict complex pathophysiology and can translate this complexity into clinically actionable outputs. Integrative informatics and data-driven approaches provide a framework for analyzing large-scale datasets and combining them into multiscale models that can be used to determine the key drivers of disease and identify optimal therapies for treating tumors. In this perspective we discuss how an integrative modeling approach is being used to inform individual treatment decisions, highlighting a recent case report that illustrates the challenges and opportunities for personalized oncology.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.