ArticleMethods in molecular biology (Clifton, N.J.)2011
eFIP: a tool for mining functional impact of phosphorylation from literature.
Article in Methods in molecular biology (Clifton, N.J.), 2011. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.
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.
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.
Who cites it
8 citing papers in PubMed, 11 citations in OpenAlex.
- Integrating text mining and knowledge graph to enhance biopharmaceutical process optimization.PloS one · 2026Article
- Identification of Novel Kinases of Tau Using Fluorescence Complementation Mass Spectrometry (FCMS).Molecular & cellular proteomics : MCP · 2022Article
- Using phosphoproteomics data to understand cellular signaling: a comprehensive guide to bioinformatics resources.Clinical proteomics · 2020Review
- Biomolecular Relationships Discovered from Biological Labyrinth and Lost in Ocean of Literature: Community Efforts Can Rescue Until Automated Artificial Intelligence Takes Over.Frontiers in genetics · 2016Article
- RLIMS-P: an online text-mining tool for literature-based extraction of protein phosphorylation information.Database : the journal of biological databases and curation · 2014Article
- topPTM: a new module of dbPTM for identifying functional post-translational modifications in transmembrane proteins.Nucleic acids research · 2014Article
- Application of text-mining for updating protein post-translational modification annotation in UniProtKB.BMC bioinformatics · 2013Article
- The eFIP system for text mining of protein interaction networks of phosphorylated proteins.Database : the journal of biological databases and curation · 2012Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
6 authors at 2 institutions in 1 country.
Funding
Abstract
Technologies and experimental strategies have improved dramatically in the field of genomics and proteomics facilitating analysis of cellular and biochemical processes, as well as of proteins networks. Based on numerous such analyses, there has been a significant increase of publications in life sciences and biomedicine. In this respect, knowledge bases are struggling to cope with the literature volume and they may not be able to capture in detail certain aspects of proteins and genes. One important aspect of proteins is their phosphorylated states and their implication in protein function and protein interacting networks. For this reason, we developed eFIP, a web-based tool, which aids scientists to find quickly abstracts mentioning phosphorylation of a given protein (including site and kinase), coupled with mentions of interactions and functional aspects of the protein. eFIP combines information provided by applications such as eGRAB, RLIMS-P, eGIFT and AIIAGMT, to rank abstracts mentioning phosphorylation, and to display the results in a highlighted and tabular format for a quick inspection. In this chapter, we present a case study of results returned by eFIP for the protein BAD, which is a key regulator of apoptosis that is posttranslationally modified by phosphorylation.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
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 OpenQuestion graph.