Discover Community Create You

Loading publication...

Noodle always counts identifier-free feature events for service operation. With your permission, Google Analytics can also estimate audience and engagement. Search text, publication identifiers, cookies, IP addresses and browser fingerprints are never included in Noodle's first-party event counters.

Privacy details

Article

Biologically Interpretable Deep Learning To Predict Response to Immunotherapy In Advanced Melanoma Using Mutations and Copy Number Variations.

Journal of immunotherapy (Hagerstown, Md. : 1997) - 1 Jan 2000

Zhang Liuchao, Cao Lei, Li Shuang, Wang Liuying, Song Yongzhen, Huang Yue, Xu Zhenyi, He Jia, Wang Meng, Li Kang

Abstract excerpt

Only 30-40% of advanced melanoma patients respond effectively to immunotherapy in clinical practice, so it is necessary to accurately identify the response of patients to immunotherapy pre-clinically. Here, we develop KP-NET, a deep learning model that is sparse on KEGG pathways, and combine it with transfer- learning to accurately predict the response of advanced melanomas to immunotherapy using KEGG...

Read the complete abstract on PubMed

Topics

Share this publication in a Topic to start or enrich a Post.

Share to B7-H1 Antigen
Share to Deep Learning

Methodology Data sources Editorial policy Privacy About Contact Helena Bioinformatics

Share to DNA Copy Number Variations

Share to Immunotherapy

Share to Melanoma

Share to Mutation

Source

Identifiers

PMID: 37220017
DOI: 10.1097/CJI.0000000000000475

Provenance

PubMed metadata and Literature Corpus relationships. Corpus snapshot updated 7 Sept 2026. Manifest publication-graph-cohort-2026-09-07.1.

Source receipt: pubmed, retrieved 7 Sept 2026.

Related research

Semantic proximity does not establish scientific evidence.

Research trail→

Click a neighbor to travelStep 1 · 12 closest

Biologically Interpretable Deep Learning To Predict Response to Immunotherapy In Advanced Melanoma Using Mutations and Copy Number Variations.PMID 37220017Read ↗

Biologically interpretable deep learning to predict response to immunotherapy in advanced melanoma using mutations and copy number variationsSemantic proximity Read ↗

PathHDNN: a pathway hierarchical-informed deep neural network framework for predicting immunotherapy response and mechanism interpretation.Semantic proximity Read ↗

Machine learning-based identification of an immunotherapy-related signature to enhance outcomes and immunotherapy responses in melanomaSemantic proximity Read ↗

Integrated genomic analysis identifies a genetic mutation model predicting response to immune checkpoint inhibitors in melanoma.Semantic proximity Read ↗

Biomarker Discovery for Meta-Classification of Melanoma Metastatic Progression using Transfer LearningSemantic proximity Read ↗

De novo single-cell biological analysis of drug resistance in human melanoma through a novel deep learning-powered approachSemantic proximity Read ↗

Biological Pathway-Derived TMB Robustly Predicts the Outcome of Immune Checkpoint Blockade Therapy.Semantic proximity Read ↗

Multifactorial Deep Learning Reveals Pan-Cancer Genomic Tumor Clusters with Distinct Immunogenomic Landscape and Response to ImmunotherapySemantic proximity Read ↗

A genomic signature for accurate classification and prediction of clinical outcomes in cancer patients treated with immune checkpoint blockade immunotherapy.Semantic proximity Read ↗

Robust prediction of patient outcomes with immune checkpoint blockade therapy for cancer using common clinical, pathologic, and genomic featuresSemantic proximity Read ↗

Identification of tumor mutation burden-related hub genes and the underlying mechanism in melanomaSemantic proximity Read ↗

Prediction of survival and immunotherapy response by the combined classifier of G protein-coupled receptors and tumor microenvironment in melanomaSemantic proximity Read ↗

⌁Select a neighboring publication to make it the new centre.