Paper Citation Record · LEDGER

Integrating Single-Cell Foundation Models with Graph Neural Networks for Drug Response Prediction

As of 22 August 2026, this Paper Citation Record lists 12 of 12 outbound references and 0 inbound Pith citation observations for arXiv:2504.14361.

A citation records a reference. It does not transfer a finding from one paper to another.

pith.paper-citation-record.v1
2504.14361 v2

Coverage vector

measured 12 of 12 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-16T11:53:45.369266Z

measured 12 of 12 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-22T06:32:14.747728+00:00

measured 0 of 0 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links

measured 0 of 1 external citation measurements

A source-named dated measurement, never combined with another source.

Source: cited_works

Reference resolution

12 of 12 outbound references displayed

verified exact0
verified fuzzy10
unresolved2
parse uncertain0
malformed identifier0
metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 094f8722-9378-4b1b-9828-d4a3700bd295 · outbound

This paper cites Deepcdr: a hybrid graph convolutional network for predicting cancer drug response.

Integrating Single-Cell Foundation Models with Graph Neural Networks for Drug Response Prediction Deepcdr: a hybrid graph convolutional network for predicting cancer drug response

Reference 1

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T11:53:45.581070Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation 5c76b3e2-7bcd-4f7e-86d4-7c140c5e04f3 · outbound

This paper cites Large-scale foundation model on single-cell transcriptomics.

Integrating Single-Cell Foundation Models with Graph Neural Networks for Drug Response Prediction Large-scale foundation model on single-cell transcriptomics

Reference 2

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T11:53:45.564978Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation 549ad2c2-161a-4ba7-9d89-4c148b5f8b4a · outbound

This paper cites scgpt: towards building a foundation model for single-cell multi-omics using generative ai.

Integrating Single-Cell Foundation Models with Graph Neural Networks for Drug Response Prediction scgpt: towards building a foundation model for single-cell multi-omics using generative ai

Reference 3

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T11:53:45.550688Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation 1b760a99-63be-4d1a-845b-c1c9adc3dff8 · outbound

This paper cites scfoundation github repository - https://github.com/biomap-research/scfoundation, 2023.

Integrating Single-Cell Foundation Models with Graph Neural Networks for Drug Response Prediction scfoundation github repository - https://github.com/biomap-research/scfoundation, 2023

Reference 4

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T11:53:45.535585Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation 5f99b46f-58c5-4cbd-b412-11330783284a · outbound

This paper cites The cancer cell line encyclopedia enables predictive modelling of anticancer drug sensitivity.

Integrating Single-Cell Foundation Models with Graph Neural Networks for Drug Response Prediction The cancer cell line encyclopedia enables predictive modelling of anticancer drug sensitivity

Reference 5

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T11:53:45.519881Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation 231b3fb0-4a25-40f8-bf41-974fd9dae445 · outbound

This paper cites A landscape of pharmacogenomic interactions in cancer.

Integrating Single-Cell Foundation Models with Graph Neural Networks for Drug Response Prediction A landscape of pharmacogenomic interactions in cancer

Reference 6

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T11:53:45.502543Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation 7d9e7c8d-022f-4748-97dc-4e99caba547d · outbound

This paper cites scgpt github repository - https://github.com/bowang-lab/scgpt, 2024.

Integrating Single-Cell Foundation Models with Graph Neural Networks for Drug Response Prediction scgpt github repository - https://github.com/bowang-lab/scgpt, 2024

Reference 7

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T11:53:45.487947Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation d7bfdb8d-c843-4769-a754-7d00d1701d90 · outbound

This paper cites ChemBERTa-2: Towards Chemical Foundation Models.

Integrating Single-Cell Foundation Models with Graph Neural Networks for Drug Response Prediction ChemBERTa-2: Towards Chemical Foundation Models

Reference 8

Resolution: unresolved; no resolver link, observed 2026-08-16T11:53:45.351830Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation d5d46dce-0b17-42b9-821e-31ea82155ca3 · outbound

This paper cites Bidirectional generation of structure and properties through a single molecular foundation model.

Integrating Single-Cell Foundation Models with Graph Neural Networks for Drug Response Prediction Bidirectional generation of structure and properties through a single molecular foundation model

Reference 9

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T11:53:45.474457Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation 35b8147f-986c-493c-98a3-e8a810b1bafd · outbound

This paper cites Large-scale chemical language representations capture molecular structure and properties.

Integrating Single-Cell Foundation Models with Graph Neural Networks for Drug Response Prediction Large-scale chemical language representations capture molecular structure and properties

Reference 10

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T11:53:45.459288Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation 35c91201-18ef-4a08-8574-32c7b28787df · outbound

This paper cites Mole: a foundation model for molecular graphs using disentangled attention.

Integrating Single-Cell Foundation Models with Graph Neural Networks for Drug Response Prediction Mole: a foundation model for molecular graphs using disentangled attention

Reference 11

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T11:53:45.444301Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation 0888c87b-bad2-4544-a385-153e74abb48e · outbound

This paper cites Self-Supervised Graph Transformer on Large-Scale Molecular Data.

Integrating Single-Cell Foundation Models with Graph Neural Networks for Drug Response Prediction Self-Supervised Graph Transformer on Large-Scale Molecular Data

Reference 12

Resolution: unresolved; no resolver link, observed 2026-08-16T11:53:45.369266Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Pith citing papers

No inbound Pith citation observations are available.