Paper Citation Record · LEDGER

scReader: Prompting Large Language Models to Interpret scRNA-seq Data

As of 17 August 2026, this Paper Citation Record lists 41 of 41 outbound references and 0 inbound Pith citation observations for arXiv:2412.18156.

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

pith.paper-citation-record.v1
2412.18156 v1

Coverage vector

measured 41 of 41 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-11T05:03:04.887529Z

measured 41 of 41 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

41 of 41 outbound references displayed

verified exact0
verified fuzzy27
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External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 6b47b4b4-eb5c-4c98-b3ef-dd52a414ac8a · outbound

This paper cites GPT-4 Technical Report.

scReader: Prompting Large Language Models to Interpret scRNA-seq Data GPT-4 Technical Report

Reference 1

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Observation 2f41b563-0450-461f-a806-100d9ea083a6 · outbound

This paper cites The Llama 3 Herd of Models.

scReader: Prompting Large Language Models to Interpret scRNA-seq Data The Llama 3 Herd of Models

Reference 2

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This paper cites LlamaFactory: Unified Efficient Fine-Tuning of 100+ Language Models.

scReader: Prompting Large Language Models to Interpret scRNA-seq Data LlamaFactory: Unified Efficient Fine-Tuning of 100+ Language Models

Reference 3

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This paper cites Benefits, limits, and risks of gpt-4 as an ai chatbot for medicine,.

scReader: Prompting Large Language Models to Interpret scRNA-seq Data Benefits, limits, and risks of gpt-4 as an ai chatbot for medicine,

Reference 4

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Observation 8b36ab28-e042-4731-bb6d-9bc18d13f6ea · outbound

This paper cites Needed: Introducing hierarchical transformer to eye diseases diagnosis,.

scReader: Prompting Large Language Models to Interpret scRNA-seq Data Needed: Introducing hierarchical transformer to eye diseases diagnosis,

Reference 5

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Observation 69d652f2-d41f-4330-a58a-c303f10c166a · outbound

This paper cites Hierarchical interdisciplinary topic detection model for research proposal classification,.

scReader: Prompting Large Language Models to Interpret scRNA-seq Data Hierarchical interdisciplinary topic detection model for research proposal classification,

Reference 6

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Source-reported events for the cited work

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Observation 299cc1a7-0486-4381-bbe5-0dbc026c7369 · outbound

This paper cites Revolutionizing radiology with gpt- based models: current applications, future possibilities and limitations of chatgpt,.

scReader: Prompting Large Language Models to Interpret scRNA-seq Data Revolutionizing radiology with gpt- based models: current applications, future possibilities and limitations of chatgpt,

Reference 7

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Observation 028af019-7c7a-4fe2-bbd5-cb4edb249890 · outbound

This paper cites Transfer learning enables predictions in network biology,.

scReader: Prompting Large Language Models to Interpret scRNA-seq Data Transfer learning enables predictions in network biology,

Reference 8

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Observation 93250f2d-473f-4c57-bf26-5c0b0cbd2152 · outbound

This paper cites Do large language models understand chemistry? a conversation with chatgpt,.

scReader: Prompting Large Language Models to Interpret scRNA-seq Data Do large language models understand chemistry? a conversation with chatgpt,

Reference 9

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T05:03:05.445391Z

Source-reported events for the cited work

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Observation 01c8731b-6b58-4d9c-ad9d-cc1bb816a52c · outbound

This paper cites Automated taxonomy alignment via large language models: bridging the gap between knowledge domains,.

scReader: Prompting Large Language Models to Interpret scRNA-seq Data Automated taxonomy alignment via large language models: bridging the gap between knowledge domains,

Reference 10

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T05:03:05.430824Z

Source-reported events for the cited work

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Observation ae212bc1-743d-4119-8d09-c5d2839f9ae3 · outbound

This paper cites Resolving the imbalance issue in hierarchical disciplinary topic inference via llm-based data augmentation,.

scReader: Prompting Large Language Models to Interpret scRNA-seq Data Resolving the imbalance issue in hierarchical disciplinary topic inference via llm-based data augmentation,

Reference 11

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T05:03:05.415192Z

Source-reported events for the cited work

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Observation 6cdbf85a-e78f-407e-a72c-106c1a205692 · outbound

This paper cites A survey on the construction methods and applications of sci-tech big data knowledge graph,.

scReader: Prompting Large Language Models to Interpret scRNA-seq Data A survey on the construction methods and applications of sci-tech big data knowledge graph,

Reference 12

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T05:03:05.398010Z

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Observation c4a7f606-39d0-4e64-a308-3f7e8949f5e4 · outbound

This paper cites Temporal inductive path neural network for temporal knowledge graph reasoning,.

scReader: Prompting Large Language Models to Interpret scRNA-seq Data Temporal inductive path neural network for temporal knowledge graph reasoning,

Reference 13

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T05:03:05.379939Z

Source-reported events for the cited work

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Observation 4b8715ad-d25a-48b7-910c-ca151518f422 · outbound

This paper cites BioMistral: A Collection of Open-Source Pretrained Large Language Models for Medical Domains.

scReader: Prompting Large Language Models to Interpret scRNA-seq Data BioMistral: A Collection of Open-Source Pretrained Large Language Models for Medical Domains

Reference 14

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Observation 6fab3eaa-23e3-4606-9533-3d909904a581 · outbound

This paper cites BioRAG: A RAG-LLM Framework for Biological Question Reasoning.

scReader: Prompting Large Language Models to Interpret scRNA-seq Data BioRAG: A RAG-LLM Framework for Biological Question Reasoning

Reference 15

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Observation e34fb8a6-e6e4-4f28-9569-c4fa46957a5b · outbound

This paper cites Genept: A simple but hard-to-beat foundation model for genes and cells built from chatgpt,.

scReader: Prompting Large Language Models to Interpret scRNA-seq Data Genept: A simple but hard-to-beat foundation model for genes and cells built from chatgpt,

Reference 16

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Source-reported events for the cited work

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Observation d696e546-a6ea-46e9-83a3-1131277c007e · outbound

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

scReader: Prompting Large Language Models to Interpret scRNA-seq Data scgpt: Towards building a foundation model for single-cell multi-omics using generative ai,

Reference 17

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Source-reported events for the cited work

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Observation a0cf8f84-18aa-4a7b-8f39-74ac88489408 · outbound

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

scReader: Prompting Large Language Models to Interpret scRNA-seq Data Large scale foundation model on single-cell transcriptomics,

Reference 18

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Source-reported events for the cited work

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Observation 2ca33ef6-f3af-470d-9683-91772602aacb · outbound

This paper cites Genecompass: Deciphering universal gene regulatory mechanisms with knowledge-informed cross-species foundation model,.

scReader: Prompting Large Language Models to Interpret scRNA-seq Data Genecompass: Deciphering universal gene regulatory mechanisms with knowledge-informed cross-species foundation model,

Reference 19

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Observation 9ebc46c1-7ede-491c-b214-abc53eab6406 · outbound

This paper cites The ncbi taxonomy database,.

scReader: Prompting Large Language Models to Interpret scRNA-seq Data The ncbi taxonomy database,

Reference 20

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Observation d66a7b72-7eb7-47ad-866c-ab29a11f2210 · outbound

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scReader: Prompting Large Language Models to Interpret scRNA-seq Data Ncbi taxonomy: a comprehensive update on curation, resources and tools,

Reference 21

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Observation 7f37c0ed-4f5a-4bcb-8050-f0bad1987315 · outbound

This paper cites Single-cell epigenomics: techniques and emerging applications,.

scReader: Prompting Large Language Models to Interpret scRNA-seq Data Single-cell epigenomics: techniques and emerging applications,

Reference 22

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Observation 651fdc66-5ea5-4b4a-adfa-8112f987a8c5 · outbound

This paper cites Single-cell genome sequencing: current state of the science,.

scReader: Prompting Large Language Models to Interpret scRNA-seq Data Single-cell genome sequencing: current state of the science,

Reference 23

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Observation 00e2d4fa-6f73-4a42-8dc1-2d34d585346e · outbound

This paper cites Building a lineage from single cells: genetic techniques for cell lineage tracking,.

scReader: Prompting Large Language Models to Interpret scRNA-seq Data Building a lineage from single cells: genetic techniques for cell lineage tracking,

Reference 24

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Observation 9b1a5502-67f2-4a63-8c11-b7ed39b9fca9 · outbound

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scReader: Prompting Large Language Models to Interpret scRNA-seq Data Enhanced Gene Selection in Single-Cell Genomics: Pre-Filtering Synergy and Reinforced Optimization

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This paper cites Gpt-3: What’s it good for?.

scReader: Prompting Large Language Models to Interpret scRNA-seq Data Gpt-3: What’s it good for?

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scReader: Prompting Large Language Models to Interpret scRNA-seq Data Gpt-3: Its nature, scope, limits, and consequences,

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scReader: Prompting Large Language Models to Interpret scRNA-seq Data Next-generation transcriptome assembly,

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scReader: Prompting Large Language Models to Interpret scRNA-seq Data Transcriptome analysis using next-generation sequencing,

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scReader: Prompting Large Language Models to Interpret scRNA-seq Data Single-cell rna sequencing technologies and applications: A brief overview,

Reference 30

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scReader: Prompting Large Language Models to Interpret scRNA-seq Data Initial sequencing and analysis of the human genome,

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scReader: Prompting Large Language Models to Interpret scRNA-seq Data Tracing the temporal-spatial transcriptome landscapes of the human fetal digestive tract using single-cell rna-sequencing,

Reference 32

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Observation d8a604ef-a8e0-44ef-9621-575aa6ce9fa5 · outbound

This paper cites Challenges in unsupervised clustering of single-cell rna-seq data,.

scReader: Prompting Large Language Models to Interpret scRNA-seq Data Challenges in unsupervised clustering of single-cell rna-seq data,

Reference 33

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scReader: Prompting Large Language Models to Interpret scRNA-seq Data Transformers in single-cell omics: a review and new perspectives,

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scReader: Prompting Large Language Models to Interpret scRNA-seq Data Bert: Pre-training of deep bidirectional transformers for language understanding,

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scReader: Prompting Large Language Models to Interpret scRNA-seq Data A survey of gpt-3 family large language models including chatgpt and gpt-4,

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scReader: Prompting Large Language Models to Interpret scRNA-seq Data LLaMA: Open and Efficient Foundation Language Models

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scReader: Prompting Large Language Models to Interpret scRNA-seq Data Comprehensive integration of single-cell data,

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scReader: Prompting Large Language Models to Interpret scRNA-seq Data R language definition,

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Observation 099cff19-3434-491c-bdb6-8db96e8b8419 · outbound

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scReader: Prompting Large Language Models to Interpret scRNA-seq Data Panglaodb: a web server for exploration of mouse and human single-cell rna sequencing data,

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Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T05:03:05.047337Z

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Observation ffcb7366-3eae-4781-9236-4cabdf84ab55 · outbound

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scReader: Prompting Large Language Models to Interpret scRNA-seq Data Cellmarker: a manually curated resource of cell markers in human and mouse,

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Pith citing papers

No inbound Pith citation observations are available.