Article in Cancer research, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
0numbers the graph read from it
0cells of the map it votes in
3citing papers in PubMed
0.7field-weighted citation impact, top 25% of its field
1 · What the graph read from it
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.
2 · The registry
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.
3 · Its place in the literature
Who cites it
3 citing papers in PubMed, 5 citations in OpenAlex.
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
5 · Who and what money
Authors and funding
7 authors at 3 institutions in 2 countries.
Alexandra M Blee *Department of Biochemistry and Center for Structural Biology, Vanderbilt University, Nashville, Tennessee.ORCID 0000-0003-4959-5466
Bian Li *Department of Medicine, Vanderbilt University Medical Center, Nashville, Tennessee.ORCID 0000-0002-8280-5018
Turner PecenJohn B. Little Center of Radiation Sciences, Department of Environmental Health, Harvard T. H. Chan School of Public Health, Boston, Massachusetts.
Jens MeilerDepartment of Chemistry and Center for Structural Biology, Vanderbilt University, Nashville, Tennessee.
Zachary D NagelJohn B. Little Center of Radiation Sciences, Department of Environmental Health, Harvard T. H. Chan School of Public Health, Boston, Massachusetts.
John A CapraBakar Computational Health Sciences Institute and Department of Epidemiology and Biostatistics, University of California, San Francisco, California.ORCID 0000-0001-9743-1795
Walter J ChazinDepartment of Biochemistry and Center for Structural Biology, Vanderbilt University, Nashville, Tennessee.ORCID 0000-0002-2180-0790
Vanderbilt University · USHarvard University · USVanderbilt University Medical Center · US
Funding
Transcription-Coupled & Replication-Associated Excision RepairP01CA092584 · NCI · UNIVERSITY OF CALIF-LAWRENC BERKELEY LAB · PI John A. Tainer · 2001 to 2026
$89.6M
Training Program in Molecular and Integrative Physiological SciencesT32HL007118 · NHLBI · HARVARD UNIVERSITY (SCH OF PUBLIC HLTH) · PI LU, QUAN · 1985 to 2025
$14.3M
Vanderbilt Center for Undiagnosed Diseases (VCUD) - BiorepositoryU01HG007674 · NHGRI · VANDERBILT UNIVERSITY MEDICAL CENTER · PI COGAN, JOY D, HAMID, RIZWAN · 2014 to 2022
$13.7M
BIOCHEMICAL &CHEMICAL TRAINING FOR CANCER RESEARCHT32CA009582 · NCI · VANDERBILT UNIVERSITY · PI David K Cortez · 1987 to 2026
$8.8M
The XPA scaffold protein in Nucleotide Excision RepairR01CA218315 · NCI · VANDERBILT UNIVERSITY · PI WALTER J. CHAZIN · 2018 to 2026
$3.4M
WASHINGTON UNIVERSITY SCHOOL OF MEDICINE UNDIAGNOSED DISEASES NETWORK CLINICAL SITEU01HG010215 · NHGRI · WASHINGTON UNIVERSITY · PI DICKSON, PATRICIA I · 2018 to 2022
$3.4M
Personalized Structural Biology: Enabling Exome Interpretation in Undiagnosed DiseasesR01LM013434 · NLM · VANDERBILT UNIVERSITY · PI CAPRA, JOHN ANTHONY, MEILER, JENS · 2021 to 2024
$1.4M
Network-Attached Biomedical Research Data Management SystemS10RR031634 · NCRR · VANDERBILT UNIVERSITY · PI SMITH, JARROD ANSON · 2011 to 2011
$600k
The effect of cancer-associated missense mutations in XPA on nucleotide excision repair activity and platinum-based chemotherapy responseF32CA250258 · NCI · VANDERBILT UNIVERSITY · PI BLEE, ALEXANDRA M · 2020 to 2021
significanceA novel machine learning approach predicts the impact of tumor mutations on cellular phenotypes, overcomes limited training data, minimizes costly functional validation, and advances efforts to implement cancer precision medicine.
Indexed as
Machine LearningNeoplasmsHumansMutationPrecision Medicine
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.
An Active Learning Framework Improves Tumor Variant Interpretation. · full record | OpenQuestion