Evidence map›Paper›PMID 42745237›Full record

ArticleThe Plant journal : for cell and molecular biology2026

FatPlants 2.0: an AI-powered platform integrating plant lipid genes, pathways, and literature.

Yongfang Qin, Congyu Guo, Minseong Kim, Yichuan Zhang, Muhammad Azam, Tingyuan Xiao, Chunhui Xu, Basil Shorrosh, Timothy P Durrett, Doug K Allen and 4 more

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in The Plant journal : for cell and molecular biology, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

14 authors.

Yongfang QinDepartment of Electrical Engineering and Computer Science, University of Missouri, Columbia, Missouri, 65201, USA.ORCID https://orcid.org/0009-0000-3478-709X
Congyu GuoDepartment of Electrical Engineering and Computer Science, University of Missouri, Columbia, Missouri, 65201, USA.ORCID https://orcid.org/0009-0003-9939-6788
Minseong KimDepartment of Life Science, University of Seoul, Seoul, 02504, Republic of Korea.ORCID https://orcid.org/0009-0004-5512-557X
Yichuan ZhangHealth Informatics Institute, University of South Florida, Tampa, Florida, 33612, USA.
Muhammad AzamDepartment of Electrical Engineering and Computer Science, University of Missouri, Columbia, Missouri, 65201, USA.
Tingyuan XiaoDepartment of Biochemistry and Molecular Biophysics, Kansas State University, Manhattan, Kansas, 66502, USA.ORCID https://orcid.org/0009-0009-3683-0480
Chunhui XuChristopher S. Bond Life Sciences Center, University of Missouri, Columbia, Missouri, 65211, USA.ORCID https://orcid.org/0000-0001-5797-2546
Basil ShorroshAI & Data Knowledge Engineering, Accenture, Denver, Colorado, 80202, USA.
Timothy P DurrettDepartment of Biochemistry and Molecular Biophysics, Kansas State University, Manhattan, Kansas, 66502, USA.ORCID https://orcid.org/0000-0001-5415-9111
Doug K AllenDonald Danforth Plant Science Center, St. Louis, Missouri, 63132, USA.ORCID https://orcid.org/0000-0001-8599-8946
Trupti JoshiDepartment of Electrical Engineering and Computer Science, University of Missouri, Columbia, Missouri, 65201, USA.ORCID https://orcid.org/0000-0001-8944-4924
Edgar B CahoonDepartment of Biochemistry and Center for Plant Science Innovation, University of Nebraska Lincoln, Lincoln, Nebraska, 68588, USA.ORCID https://orcid.org/0000-0002-7277-1176
Jay J ThelenChristopher S. Bond Life Sciences Center, University of Missouri, Columbia, Missouri, 65211, USA.ORCID https://orcid.org/0000-0001-5995-1562
Dong XuDepartment of Electrical Engineering and Computer Science, University of Missouri, Columbia, Missouri, 65201, USA.ORCID https://orcid.org/0000-0002-4809-0514

Funding

National Science Foundation grant IOS-1829365U.S. Department of Energy grant DE-SC0023142
6 · The paper itself

Abstract

Plant lipid research depends on accessible databases that connect genes, pathways, and prior literature. However, currently available resources are fragmented, species-limited, and lack AI-powered interfaces for integrated querying. FatPlants 2.0 addresses these issues by integrating ARALIP, PlantFADB, and new Cuphea/Pennycress experimental data into a unified platform with 14 000 genes/proteins, 110 pathways across 5 species, and 57 000+ curated publications. The platform features LipidBot, an AI agent enabling natural language queries via graph-based pathway search and retrieval-augmented generation-powered literature retrieval. Users query complex relationships conversationally and receive answers with traceable citations. The graph database models 100+ biological pathways as queryable networks with large language model-guided Cypher generation. By evaluating more than 1000 curated questions, LipidBot achieved 95% accuracy on pathway queries and 92% recall in literature retrieval using optimized embeddings. The tool demonstrated robust performance across factual, numerical, and multi-hop queries on curated benchmarks. FatPlants 2.0 accelerates research by reducing the time spent on literature reviews. Database and AI agent freely available at https://fatplants.net with bulk downloads and quarterly updates.

Indexed as

Artificial IntelligenceGenes, PlantLipid MetabolismLipidsPlantsBiocurationDatabases, GeneticInformation Storage and RetrievalSoftwareLipidsAI chatbotdatabaseFatPlantslarge language modelpathway analysisplant lipidsretrieval‐augmented generation (RAG)

Identifiers

PMID42745237
PMCPMC13578592

What Socratic holds

Textmetadata
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the Socratic graph.