OrgXenomics: an integrated proteomic knowledge base for patient-derived organoid and xenograft

Patient-derived models (PDMs, particularly organoids and xenografts) are irreplaceable tools for precision medicine, from target development to lead identification, then to preclinical evaluation, and finally to clinical decision-making. So far, PDM-based proteomics has emerged to be one of the cutting-edge directions and massive data have been accumulated. However, such PDM-based proteomic data have not been provided by any of the available databases, and proteomics profiles of all proteins in proteomic study are also completely absent from existing databases. Herein, an integrated database named 'OrgXenomics' was thus developed to provide the proteomic data for PDMs, which was unique in (a) explicitly describing the establishment detail for a wide array of models, (b) systematically providing the proteomic profiles (expression/function/interaction) for all proteins in studied proteomic analysis and (c) comprehensively giving the raw data for diverse organoid/xenograft-based proteomic studies of various diseases. Our OrgXenomics was expected to server as one good complement to existing proteomic databases, and had great implication for the practice of precision medicine, which could be accessed at: https://idrblab.org/orgxenomics/.

Authors

Institutions

Publication Details

Journal
Nucleic Acids Research
Published
2024-10-07
DOI
https://doi.org/10.1093/nar/gkae861
Citations
2
Primary Topic
Cancer, Hypoxia, and Metabolism
Type
article
Field-Weighted Citation Impact
0.57

Funders

Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

OrgXenomics: an integrated proteomic knowledge base for patient-derived organoid and xenograft

Shuiping Liu, Lin Tao, Yintao Zhang, Ying Zhou et al.
2 citations
Nucleic Acids Research
Cancer, Hypoxia, and Metabolism
0.57
article

OrgXenomics: an integrated proteomic knowledge base for patient-derived organoid and xenograft

Shuiping Liu, Lin Tao, Yintao Zhang, Ying Zhou, Xichen Lian, Tingting Fu, Hao Zhang, Hangwei Xu, Feng Zhu, Sisi Zhu, Ziheng Ni
article en
2 citations

Abstract

Patient-derived models (PDMs, particularly organoids and xenografts) are irreplaceable tools for precision medicine, from target development to lead identification, then to preclinical evaluation, and finally to clinical decision-making. So far, PDM-based proteomics has emerged to be one of the cutting-edge directions and massive data have been accumulated. However, such PDM-based proteomic data have not been provided by any of the available databases, and proteomics profiles of all proteins in proteomic study are also completely absent from existing databases. Herein, an integrated database named 'OrgXenomics' was thus developed to provide the proteomic data for PDMs, which was unique in (a) explicitly describing the establishment detail for a wide array of models, (b) systematically providing the proteomic profiles (expression/function/interaction) for all proteins in studied proteomic analysis and (c) comprehensively giving the raw data for diverse organoid/xenograft-based proteomic studies of various diseases. Our OrgXenomics was expected to server as one good complement to existing proteomic databases, and had great implication for the practice of precision medicine, which could be accessed at: https://idrblab.org/orgxenomics/.

Nucleic Acids ResearchVol. 53(D1)
Hangzhou Normal University (CN), Second Affiliated Hospital of Zhejiang University (CN), Artificial Intelligence in Medicine (Canada) (CA), Zhejiang University (CN)
National Natural Science Foundation of China, Zhejiang University, National Key Research and Development Program of China, Natural Science Foundation of Zhejiang Province
Peace, Justice and strong institutions
Openalex Percentile: Top 31%
Cancer, Hypoxia, and Metabolism
0.57
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.