Article

Integrating knowledge, omics and AI to develop patient-specific virtual avatars

2024-11-12

Abstract excerpt

We propose a method for creating personalized regulatory networks, enabling the development of virtual avatars for cancer patients, with patient-derived xenograft (PDX) models as a test case. Starting from a Prior Knowledge Network (PKN) based on the hallmarks of cancer, we constructed gene networks that are contextualized to each sample by integrating sample-specific gene expression data. These networks were opti...

Topics

Open a Topic to create a Post that cites this publication.

Identifiers and source

Literature Corpus work: ef1c70d8-b390-51f2-be6b-ee391f7d1a3c
DOI: 10.1101/2024.11.07.622508

Open publication

Related research

Semantic proximity does not establish scientific evidence.

Click a neighbor to travelStep 1 · 12 closest

Integrating knowledge, omics and AI to develop patient-specific virtual avatarsDOI 10.1101/2024.11.07.622508Read

Expression graph network framework for biomarker discoverySemantic proximity Read

Multiview representation learning for identification of novel cancer genes and their causative biological mechanisms.Semantic proximity Read

DeepMOCCA: A pan-cancer prognostic model identifies personalized prognostic markers through graph attention and multi-omics data integrationSemantic proximity Read

Artificial Molecular Network (AMoNet): graph learning for cancer survival prediction from targeted sequencingSemantic proximity Read

DeePaN: A deep patient graph convolutional network integratingclinico-genomic evidence to stratify lung cancers benefiting from immunotherapySemantic proximity Read

Data-driven learning how oncogenic gene expression locally alters heterocellular networksSemantic proximity Read

A Generative Virtual Tissue Model Enables Computational Design of Therapeutic Perturbation StrategiesSemantic proximity Read

Network-guided supervised learning on gene expression using a graph convolutional neural networkSemantic proximity Read

CanSeer: A Method for Development and Clinical Translation of Personalized Cancer TherapeuticsSemantic proximity Read

Single-cell gene regulatory network reconstruction and key regulator identification using a dual-channel fusion graph convolutional networkSemantic proximity Read

The Personalized Cancer Network Explorer (PeCaX) as a visual analytics tool to support molecular tumor boardsSemantic proximity Read

Integrated next-generation sequencing and avatar mouse models for personalized cancer treatment.Semantic proximity Read