Paper Citation Record · LEDGER

Predicting Therapeutic Outcome via Aligning Patient-Specific Knowledge Graph and Gene-Level Perturbation Representations

As of 12 August 2026, this Paper Citation Record lists 29 of 29 outbound references and 0 inbound Pith citation observations for arXiv:2607.04557.

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

pith.paper-citation-record.v1
2607.04557 v1

Coverage vector

measured 29 of 29 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-07-11T17:22:40.642396Z

measured 29 of 29 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-11T06:34:44.6726+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

29 of 29 outbound references displayed

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unresolved29
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Outbound references

Observation 21c2c0b3-602d-446b-b737-c2e299321fba · outbound

This paper cites Precision medicine: changing the way we think about healthcare.Clinics, 73:e723, 2018.

Predicting Therapeutic Outcome via Aligning Patient-Specific Knowledge Graph and Gene-Level Perturbation Representations Precision medicine: changing the way we think about healthcare.Clinics, 73:e723, 2018

Reference 1

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Observation 90ceefa1-7b0e-4b45-9935-d16b262c3e34 · outbound

This paper cites Pharmacogenomics: translating functional genomics into rational therapeutics.science, 286(5439):487–491, 1999.

Predicting Therapeutic Outcome via Aligning Patient-Specific Knowledge Graph and Gene-Level Perturbation Representations Pharmacogenomics: translating functional genomics into rational therapeutics.science, 286(5439):487–491, 1999

Reference 2

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Observation 1443159d-546e-4a5a-80dd-7c07347ca2a0 · outbound

This paper cites Machine learning approaches to drug response prediction: challenges and recent progress.NPJ precision oncology, 4(1):19, 2020.

Predicting Therapeutic Outcome via Aligning Patient-Specific Knowledge Graph and Gene-Level Perturbation Representations Machine learning approaches to drug response prediction: challenges and recent progress.NPJ precision oncology, 4(1):19, 2020

Reference 3

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Observation 1a2c7a12-a15c-4195-b3be-be3d3b1ec160 · outbound

This paper cites The cancer genome atlas pan-cancer analysis project.Nature genetics, 45(10):1113– 1120, 2013.

Predicting Therapeutic Outcome via Aligning Patient-Specific Knowledge Graph and Gene-Level Perturbation Representations The cancer genome atlas pan-cancer analysis project.Nature genetics, 45(10):1113– 1120, 2013

Reference 4

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Observation c36f6490-9381-4cfc-ae32-dec3bcaf0127 · outbound

This paper cites A landscape of pharmacogenomic interactions in cancer.

Predicting Therapeutic Outcome via Aligning Patient-Specific Knowledge Graph and Gene-Level Perturbation Representations A landscape of pharmacogenomic interactions in cancer

Reference 5

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Observation 8e6fccf2-fea7-4f83-8f95-138197a74f47 · outbound

This paper cites The cancer cell line encyclopedia enables predictive modelling of anticancer drug sensitivity.Nature, 483(7391):603–607, 2012.

Predicting Therapeutic Outcome via Aligning Patient-Specific Knowledge Graph and Gene-Level Perturbation Representations The cancer cell line encyclopedia enables predictive modelling of anticancer drug sensitivity.Nature, 483(7391):603–607, 2012

Reference 6

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Observation ccb810c7-47e9-4be7-97ba-df8a9eeca435 · outbound

This paper cites Opportunities and challenges in interpretable deep learning for drug sensitivity prediction of cancer cells.Frontiers in Bioinformatics, 2:1036963, 2022.

Predicting Therapeutic Outcome via Aligning Patient-Specific Knowledge Graph and Gene-Level Perturbation Representations Opportunities and challenges in interpretable deep learning for drug sensitivity prediction of cancer cells.Frontiers in Bioinformatics, 2:1036963, 2022

Reference 7

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Observation 70d993ee-47b2-4cd1-80f5-5bdd7a5517cf · outbound

This paper cites Deepcdr: a hybrid graph convolutional network for predicting cancer drug response.Bioinformatics, 36(Supplement_2):i911–i918, 2020.

Predicting Therapeutic Outcome via Aligning Patient-Specific Knowledge Graph and Gene-Level Perturbation Representations Deepcdr: a hybrid graph convolutional network for predicting cancer drug response.Bioinformatics, 36(Supplement_2):i911–i918, 2020

Reference 8

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Observation c015a4ff-43b3-44b6-8a42-f7edd38c7e57 · outbound

This paper cites Deeptta: a transformer-based model for predicting cancer drug response.

Predicting Therapeutic Outcome via Aligning Patient-Specific Knowledge Graph and Gene-Level Perturbation Representations Deeptta: a transformer-based model for predicting cancer drug response

Reference 9

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Observation 0ee6dcf8-bad0-4bca-b79e-8f517ce3114c · outbound

This paper cites Drug sensitivity prediction from cell line-based pharmacogenomics data: guidelines for developing machine learning models.

Predicting Therapeutic Outcome via Aligning Patient-Specific Knowledge Graph and Gene-Level Perturbation Representations Drug sensitivity prediction from cell line-based pharmacogenomics data: guidelines for developing machine learning models

Reference 10

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Observation 7f6aec7a-eff4-4b8e-984a-8d8cae5a7ca3 · outbound

This paper cites an unresolved cited work.

Predicting Therapeutic Outcome via Aligning Patient-Specific Knowledge Graph and Gene-Level Perturbation Representations Unresolved cited work

Reference 11

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Observation 96aaa35a-6ac6-4003-8aec-ba82fa2c71bf · outbound

This paper cites Gene expression based inference of cancer drug sensitiv- ity.Nature communications, 13(1):5680, 2022.

Predicting Therapeutic Outcome via Aligning Patient-Specific Knowledge Graph and Gene-Level Perturbation Representations Gene expression based inference of cancer drug sensitiv- ity.Nature communications, 13(1):5680, 2022

Reference 12

Resolution: unresolved; no resolver link, observed 2026-07-11T17:22:40.642396Z

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Observation 04f187c3-a71a-4b5a-b909-c671749889b1 · outbound

This paper cites L1000cds2: Lincs l1000 characteristic direction signatures search engine.

Predicting Therapeutic Outcome via Aligning Patient-Specific Knowledge Graph and Gene-Level Perturbation Representations L1000cds2: Lincs l1000 characteristic direction signatures search engine

Reference 13

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Observation 7172beaf-5cd4-4354-93f0-83b195bf3eb9 · outbound

This paper cites Transfer learning of condition-specific perturbation in gene interactions improves drug response prediction.Bioinfor- matics, 40(Supplement_1):i130–i139, 2024.

Predicting Therapeutic Outcome via Aligning Patient-Specific Knowledge Graph and Gene-Level Perturbation Representations Transfer learning of condition-specific perturbation in gene interactions improves drug response prediction.Bioinfor- matics, 40(Supplement_1):i130–i139, 2024

Reference 14

Resolution: unresolved; no resolver link, observed 2026-07-11T17:22:40.642396Z

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Observation 85ec6745-d394-464a-b2a3-f8d4b1e20138 · outbound

This paper cites Learning transferable visual models from natural language supervision.

Predicting Therapeutic Outcome via Aligning Patient-Specific Knowledge Graph and Gene-Level Perturbation Representations Learning transferable visual models from natural language supervision

Reference 15

Resolution: unresolved; no resolver link, observed 2026-07-11T17:22:40.642396Z

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Observation d96ff1f2-dcbb-4378-8b10-e5fb02af130d · outbound

This paper cites I-spy 2: an adaptive breast cancer trial design in the setting of neoadjuvant chemotherapy.Clinical Pharmacology & Therapeutics, 86(1):97–100, 2009.

Predicting Therapeutic Outcome via Aligning Patient-Specific Knowledge Graph and Gene-Level Perturbation Representations I-spy 2: an adaptive breast cancer trial design in the setting of neoadjuvant chemotherapy.Clinical Pharmacology & Therapeutics, 86(1):97–100, 2009

Reference 16

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Observation e17e5c8b-0453-4dea-9584-1b0ca2205897 · outbound

This paper cites Dysregnet: Patient-specific and confounder-aware dysregulated network inference towards precision therapeutics.British journal of pharmacology, 2024.

Predicting Therapeutic Outcome via Aligning Patient-Specific Knowledge Graph and Gene-Level Perturbation Representations Dysregnet: Patient-specific and confounder-aware dysregulated network inference towards precision therapeutics.British journal of pharmacology, 2024

Reference 17

Resolution: unresolved; no resolver link, observed 2026-07-11T17:22:40.642396Z

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Observation d96d009b-dd2d-4e21-81a5-6cbbfcffdaab · outbound

This paper cites Htridb: an open-access database for experimentally verified human transcriptional regulation interactions.Nature Precedings, pages 1–1, 2012.

Predicting Therapeutic Outcome via Aligning Patient-Specific Knowledge Graph and Gene-Level Perturbation Representations Htridb: an open-access database for experimentally verified human transcriptional regulation interactions.Nature Precedings, pages 1–1, 2012

Reference 18

Resolution: unresolved; no resolver link, observed 2026-07-11T17:22:40.642396Z

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Observation 6cb23e02-beac-412d-863a-b868b9e0f218 · outbound

This paper cites Drugbank 6.0: the drugbank knowledgebase for 2024.Nucleic acids research, 52(D1):D1265–D1275, 2024.

Predicting Therapeutic Outcome via Aligning Patient-Specific Knowledge Graph and Gene-Level Perturbation Representations Drugbank 6.0: the drugbank knowledgebase for 2024.Nucleic acids research, 52(D1):D1265–D1275, 2024

Reference 19

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Observation a109c54b-1bc9-4f74-90ae-592ba557b0c7 · outbound

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

Predicting Therapeutic Outcome via Aligning Patient-Specific Knowledge Graph and Gene-Level Perturbation Representations Semi-Supervised Classification with Graph Convolutional Networks

Reference 20

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Observation ad6e8d28-656c-4f8a-baad-d160d6dc0764 · outbound

This paper cites Biomedical knowledge graph learning for drug repurposing by extending guilt-by-association to multiple layers.Nature Communications, 14(1):3570, 2023.

Predicting Therapeutic Outcome via Aligning Patient-Specific Knowledge Graph and Gene-Level Perturbation Representations Biomedical knowledge graph learning for drug repurposing by extending guilt-by-association to multiple layers.Nature Communications, 14(1):3570, 2023

Reference 21

Resolution: unresolved; no resolver link, observed 2026-07-11T17:22:40.642396Z

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Observation 082b2549-47d5-451a-a53d-43a789129404 · outbound

This paper cites an unresolved cited work.

Predicting Therapeutic Outcome via Aligning Patient-Specific Knowledge Graph and Gene-Level Perturbation Representations Unresolved cited work

Reference 22

Resolution: unresolved; no resolver link, observed 2026-07-11T17:22:40.642396Z

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Observation 89dab832-0987-457b-a3ce-845a7253b30f · outbound

This paper cites Molecule Attention Transformer.

Predicting Therapeutic Outcome via Aligning Patient-Specific Knowledge Graph and Gene-Level Perturbation Representations Molecule Attention Transformer

Reference 23

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Observation fb13668c-a0b2-4c42-8119-3dde73b9c641 · outbound

This paper cites Transfer learning enables predictions in network biology.

Predicting Therapeutic Outcome via Aligning Patient-Specific Knowledge Graph and Gene-Level Perturbation Representations Transfer learning enables predictions in network biology

Reference 24

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Observation 09221c79-d1bb-48f0-96b6-684a628777a5 · outbound

This paper cites From local explanations to global understanding with explainable ai for trees.Nature machine intelligence, 2(1):56–67, 2020.

Predicting Therapeutic Outcome via Aligning Patient-Specific Knowledge Graph and Gene-Level Perturbation Representations From local explanations to global understanding with explainable ai for trees.Nature machine intelligence, 2(1):56–67, 2020

Reference 25

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Observation 2be0c273-39db-4eab-a735-78b6b1bd5c92 · outbound

This paper cites A unified approach to interpreting model predictions.Advances in neural information processing systems, 30, 2017.

Predicting Therapeutic Outcome via Aligning Patient-Specific Knowledge Graph and Gene-Level Perturbation Representations A unified approach to interpreting model predictions.Advances in neural information processing systems, 30, 2017

Reference 26

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Observation c81c3f5c-6f2e-4cf8-9721-2eab251ef8e7 · outbound

This paper cites Mechanisms of cancer cell death induction by paclitaxel: an updated review.Apoptosis, 27(9):647– 667, 2022.

Predicting Therapeutic Outcome via Aligning Patient-Specific Knowledge Graph and Gene-Level Perturbation Representations Mechanisms of cancer cell death induction by paclitaxel: an updated review.Apoptosis, 27(9):647– 667, 2022

Reference 27

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Observation 211c209f-7cd1-4df1-a7be-f59ee7599272 · outbound

This paper cites Cancer hallmarks, biomarkers and breast cancer molecular subtypes.Journal of cancer, 7(10):1281, 2016.

Predicting Therapeutic Outcome via Aligning Patient-Specific Knowledge Graph and Gene-Level Perturbation Representations Cancer hallmarks, biomarkers and breast cancer molecular subtypes.Journal of cancer, 7(10):1281, 2016

Reference 28

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Observation c43cc64f-7b4b-4fee-affa-6cc37ef1b215 · outbound

This paper cites Gene set knowledge discovery with enrichr.Current protocols, 1(3):e90, 2021.

Predicting Therapeutic Outcome via Aligning Patient-Specific Knowledge Graph and Gene-Level Perturbation Representations Gene set knowledge discovery with enrichr.Current protocols, 1(3):e90, 2021

Reference 29

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Pith citing papers

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