Evidence map›Paper›PMID 40601262›Full record

ArticleMethods in molecular biology (Clifton, N.J.)2025

Large Context, Deeper Insights: Harnessing Large Language Models for Advancing Protein-Protein Interaction Analysis.

Kaicheng U, Sophia Meixuan Zhang, Suresh Pokharel, Pawel Pratyush, Farah Qaderi, Dongfang Liu, Junhan Zhao, Dukka B Kc, Siwei Chen

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In one paragraph

Article in Methods in molecular biology (Clifton, N.J.), 2025. 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

9 authors.

Kaicheng UTri-Institutional Computational Biology & Medicine, Weill Cornell Medicine, New York, NY, USA.
Sophia Meixuan ZhangCollege of Agriculture and Life Sciences, Cornell University, Ithaca, NY, USA.
Suresh PokharelDepartment of Computer Science, Rochester Institute of Technology, Rochester, NY, USA.
Pawel PratyushDepartment of Computer Science, Rochester Institute of Technology, Rochester, NY, USA.
Farah QaderiDepartment of Surgical Oncology, Massachusetts General Hospital, Boston, MA, USA.
Dongfang LiuDepartment of Computer Engineering, Rochester Institute of Technology, Rochester, NY, USA.
Junhan ZhaoDepartment of Biomedical Informatics, Harvard Medical School, Boston, MA, USA.
Dukka B KcDepartment of Computer Science, Golisano College of Computing and Information Sciences, Rochester Institute of Technology, Rochester, NY, USA.
Siwei ChenStanley Center for Psychiatric Research, Broad Institute of MIT and Harvard, Cambridge, MA, USA. siwei@broadinstitute.org.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Protein-protein interactions (PPIs) are involved in nearly all biological processes. Understanding and analysis of PPI is key to revealing biological networks and identifying new therapeutic targets. Various computational approaches have been proposed as an alternative to the experimental investigation of PPIs. More recently, with the advent of Large Language Models (LLMs), a plethora of approaches using LLMs have been developed, enabling efficient analysis of interaction networks and binding sites directly from protein sequences. These models capture intricate biological patterns, offering scalability and adaptability across diverse datasets. However, challenges remain, including computational costs, data imbalance, and the integration of multimodal information. Advancements in addressing these limitations are set to further enhance the potential of LLMs in protein-protein interaction analysis, driving deeper insights and broader applications in biological research.

Indexed as

Computational BiologyProtein Interaction MappingProtein Interaction MapsProteinsBinding SitesDatabases, ProteinHumansLarge Language ModelsProtein BindingSoftwareProteinsLarge language models (LLMs)PPI predictionProtein language modelProtein–protein interaction (PPI)Sequence-based models

Identifiers

PMID40601262

What Socratic holds

Textmetadata
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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.