Paper Citation Record · LEDGER

Global Context-aware Representation Learning for Spatially Resolved Transcriptomics

As of 17 August 2026, this Paper Citation Record lists 17 of 17 outbound references and 0 inbound Pith citation observations for arXiv:2506.15698.

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

pith.paper-citation-record.v1
2506.15698 v2

Coverage vector

measured 17 of 17 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-07T13:17:31.374351Z

measured 17 of 17 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-17T06:30:58.91139+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

17 of 17 outbound references displayed

verified exact0
verified fuzzy8
unresolved7
parse uncertain0
malformed identifier1
metadata mismatch1

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 50825fc7-9061-4b3b-96df-5585776618d4 · outbound

This paper cites Semi-Supervised Classification with Graph Convolutional Networks.

Global Context-aware Representation Learning for Spatially Resolved Transcriptomics Semi-Supervised Classification with Graph Convolutional Networks

Reference 3

Resolution: unresolved; no resolver link, observed 2026-08-07T13:17:29.992315Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 953596a9-878b-48be-ae65-7420f516ffc7 · outbound

This paper cites an unresolved cited work.

Global Context-aware Representation Learning for Spatially Resolved Transcriptomics Unresolved cited work

Reference 5

Resolution: unresolved; raw_fallback, observed 2026-08-07T13:17:32.072554Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation 02f58d42-3f17-4f44-96e5-734eb1124ebc · outbound

This paper cites Large-Scale Representation Learning on Graphs via Bootstrapping.

Global Context-aware Representation Learning for Spatially Resolved Transcriptomics Large-Scale Representation Learning on Graphs via Bootstrapping

Reference 7

Resolution: unresolved; no resolver link, observed 2026-08-07T13:17:30.321492Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 006a320e-7372-4372-aa9e-ab7162eaf57c · outbound

This paper cites Graph Attention Networks.

Global Context-aware Representation Learning for Spatially Resolved Transcriptomics Graph Attention Networks

Reference 8

Resolution: unresolved; no resolver link, observed 2026-08-07T13:17:30.432498Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation fd258649-da0d-444c-982a-f264bb99508d · outbound

This paper cites URL https:// www.jneurosci.org/content/27/32/8496.

Global Context-aware Representation Learning for Spatially Resolved Transcriptomics URL https:// www.jneurosci.org/content/27/32/8496

Reference 10

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:17:33.548858Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation 29ebae98-0344-4381-88ab-dd14793c529b · outbound

This paper cites Deep Graph Contrastive Representation Learning.

Global Context-aware Representation Learning for Spatially Resolved Transcriptomics Deep Graph Contrastive Representation Learning

Reference 11

Resolution: unresolved; no resolver link, observed 2026-08-07T13:17:30.614825Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 3149eb28-4c5b-4623-8e74-3ca4518d8843 · outbound

This paper cites It was profiled by Stereo-seq technology, which allows spatial transcriptomics at the cellular level by integrating DNA nanoball-patterned arrays with in situ RNA capture.

Global Context-aware Representation Learning for Spatially Resolved Transcriptomics It was profiled by Stereo-seq technology, which allows spatial transcriptomics at the cellular level by integrating DNA nanoball-patterned arrays with in situ RNA capture

Reference 12

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:17:33.148899Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation 74c6165d-5c34-441e-a16f-38139b815fc8 · outbound

This paper cites BGRL (Thakoor et al., 2021; Grill et al.,.

Global Context-aware Representation Learning for Spatially Resolved Transcriptomics BGRL (Thakoor et al., 2021; Grill et al.,

Reference 13

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:17:32.910848Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation 8c9dcf33-003a-4bf8-b22b-536af10b9bc3 · outbound

This paper cites (a) DLPFC Patient 1 (b) DLPFC Patient 2 (c) DLPFC Patient 3 Figure 25.Trajectory conservation between pseudotimes and Layers in DLPFC data.

Global Context-aware Representation Learning for Spatially Resolved Transcriptomics (a) DLPFC Patient 1 (b) DLPFC Patient 2 (c) DLPFC Patient 3 Figure 25.Trajectory conservation between pseudotimes and Layers in DLPFC data

Reference 26

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:17:32.484534Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation ead46a5c-e6f6-40c8-98cc-e99e4942d117 · outbound

This paper cites The results show that after imputation usingSpotscape, marker genes are more distinctly expressed, demonstrating the practical applicability ofSpotscape.

Global Context-aware Representation Learning for Spatially Resolved Transcriptomics The results show that after imputation usingSpotscape, marker genes are more distinctly expressed, demonstrating the practical applicability ofSpotscape

Reference 28

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:17:32.236707Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation 51c930a0-1e80-4275-b1cd-0a35f4be4d64 · outbound

This paper cites The encoder’s hidden dimensions are set to [Ng,256,64] , while the decoder’s dimensions are configured as [64,256, Ng].

Global Context-aware Representation Learning for Spatially Resolved Transcriptomics The encoder’s hidden dimensions are set to [Ng,256,64] , while the decoder’s dimensions are configured as [64,256, Ng]

Reference 2016

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:17:32.718161Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation c596b532-56b0-4f96-b45b-d5262add32c5 · outbound

This paper cites Deep Graph Infomax.

Global Context-aware Representation Learning for Spatially Resolved Transcriptomics Deep Graph Infomax

Reference 2017

Resolution: unresolved; no resolver link, observed 2026-08-07T13:17:30.523091Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation f00eeb2e-6a69-41c8-a4c8-8ad9ebb47ca9 · outbound

This paper cites Datasets Table 5.Statistics for datasets used for experiments.

Global Context-aware Representation Learning for Spatially Resolved Transcriptomics Datasets Table 5.Statistics for datasets used for experiments

Reference 2020

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:17:33.322771Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation 80e705d2-e38f-4b9f-afc7-562bc9d7e98b · outbound

This paper cites W., Schulmann, A., Hara, E., Winnubst, J., Liu, C., Valakh, V ., Wang, L., Shields, B.

Global Context-aware Representation Learning for Spatially Resolved Transcriptomics W., Schulmann, A., Hara, E., Winnubst, J., Liu, C., Valakh, V ., Wang, L., Shields, B

Reference 2021

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:17:33.763563Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation 04ce1b88-ea0a-4db5-b6e5-794e53f96c53 · outbound

This paper cites doi: 10.1038/ s41392-023-01588-0.

Global Context-aware Representation Learning for Spatially Resolved Transcriptomics doi: 10.1038/ s41392-023-01588-0

Reference 2023

Resolution: malformed identifier; raw_fallback, observed 2026-08-07T13:17:34.086206Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation 3cf57a09-95cb-46bb-8470-c6c5b14c79fa · outbound

This paper cites Prototypical Contrastive Learning of Unsupervised Representations.

Global Context-aware Representation Learning for Spatially Resolved Transcriptomics Prototypical Contrastive Learning of Unsupervised Representations

Reference 2024

Resolution: unresolved; no resolver link, observed 2026-08-07T13:17:30.075307Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation d23c8410-be06-48f3-882e-d3c6524f28f6 · outbound

This paper cites URL https://www.frontiersin.org/journals/ molecular-neuroscience/articles/10.

Global Context-aware Representation Learning for Spatially Resolved Transcriptomics URL https://www.frontiersin.org/journals/ molecular-neuroscience/articles/10

Reference 5099

Resolution: metadata mismatch; raw_fallback, observed 2026-08-07T13:17:31.892196Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Pith citing papers

No inbound Pith citation observations are available.