Publication2024
Integrative prognostic modeling for breast cancer: Unveiling optimal multimodal combinations using graph convolutional networks and calibrated random forest
The record
- Type
- Publication
- Date
- 1 March 2024
- Level
- National
- Journal
- Applied Soft Computing
- Volume
- 154
- Published
- 1 March 2024
- DOI
- 10.1016/j.asoc.2024.111379
- Scopus ID
- 85185536721
- ISSN
- 15684946
- Authors
- 4
- Open access
- No
- Citations
- 18
- International co-authorship
- No
- Co-author countries
- IN
Classification
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UN Sustainable Development Goals
3 Ensure healthy lives and promote well-being for all at all ages
Multimodal graph convolutional network with calibrated random forest predicts breast cancer prognosis achieving 77.1% accuracy from mRNA, methylation, CNV and imaging data.
- High confidence
- The subject matter is a strong and direct inference from the record type, the funding or organising body, or the programme context, even where the title alone is thin. Example: an ICMR-funded project against SDG 3, on the strength of the agency.
- Judged
- Judged against the published classification rubric, under the rubric and prompt versions recorded with the run that produced it.
- Produced by
- claude-haiku-4-5-20251001, under kortex-publication-v1
Viksit Bharat @2047
vb-1.2 Health and well-being
AI-driven cancer prognostic modeling enables personalized treatment planning and improved patient survival outcomes through integrated multimodal analysis.
- Produced by
- claude-haiku-4-5-20251001, under kortex-publication-v1
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- 1
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Citing this record
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KIIT Deemed to be University. “Integrative prognostic modeling for breast cancer: Unveiling optimal multimodal combinations using graph convolutional networks and calibrated random forest”. Kortex.