Anu Amallraja

dblp:425/3057 · DBLP profile ↗
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1ranked-venue papers
0as first author
1since 2021 · last 2025
—ORCID · unresolved

Domains — the database's venue-derived domains; a paper can count in several

Applied, interdisciplinary, general and emerging computing · 1 · 1 since 2021

Expertise — from the expertise taxonomy: the topics of the expert's papers under the CCF categories. A weight counts papers with recency: 1 for a paper about the topic, 0.3 when the topic is its context, halved every five years.

Interdisciplinary, comprehensive, and emerging computing
1 paper
Medical and health informatics · 100%

Topics — the 2 heaviest of 3, each with the papers that count most for it

TopicWeightPapersLastEvidence papers
Medical and health informatics › drug development › clinical trial › clinical trial informatics
patient-trial matching
0.912025
CancerTrialMatch: a computational resource for the management of biomarker-based clinical trials at a community cancer center · Bioinform. 2025
Medical and health informatics › precision medicine
precision oncology
0.912025
CancerTrialMatch: a computational resource for the management of biomarker-based clinical trials at a community cancer center · Bioinform. 2025

Methods — techniques the papers use, named apart from their topics

oncotree classification · 0.9clinicaltrials.gov API · 0.9
YearPublicationVenuePosition
2025 CancerTrialMatch: a computational resource for the management of biomarker-based clinical trials at a community cancer center
abstract
MOTIVATION: The widespread implementation of next-generation sequencing in cancer care has enabled routine use of molecular and biomarker profiling. At our cancer center, as with many others, biomarker-based clinical trials are increasingly available to oncologists as potential treatment options via molecular tumor boards. To better support this effort, we developed CancerTrialMatch, a systematic approach to capture structured clinical trial data and match patients to trials based on their disease characteristics and sequencing profiles. RESULTS: CancerTrialMatch is an open-source application designed to streamline clinical trial curation and patient trial matching, while also enabling an institution's curated trial portfolio to be distributed across the institution for easy access to providers, care teams and researchers. It facilitates curating, updating, and searching for trials through a semi-automated interface built using R Shiny, MongoDB, and Docker. While much of the trial data is retrieved via the clinicaltrials.gov Application Programming Interface, certain items like biomarkers and disease subtypes are entered manually. The user inputs disease type using the OncoTree classification, and provides relevant biomarker details, such as mutations, copy numbers, fusions, and other disease-specific markers. This resource reduces the time required for institutional trial management and helps to identify potential clinical trials for patients, ultimately supporting larger clinical trial enrollment and enhancing the clinical application of precision oncology. AVAILABILITY AND IMPLEMENTATION: CancerTrialMatch was implemented and tested on Windows 11 (64-bit, 32 GB RAM) using WSL2 with Ubuntu 22.04. Docker 27.0.3 and Docker Compose 2.28.1 were used to build images and containers. Users can build it by cloning the repo and following the README instructions and supplemental file (cancertrialmatchsupplemental.pdf) . The source code and example data are available in GitHub and Figshare at https://github.com/AveraSD/CancerTrialMatch and 10.6084/m9.figshare.28447367 respectively.
Padmapriya Swaminathan, Anu Amallraja, Shivani Kapadia, Casey B. Williams, Tobias Meißner
Bioinform.2