ArticleThe Journal of membrane biology2015
iMem-Seq: A Multi-label Learning Classifier for Predicting Membrane Proteins Types.
Article in The Journal of membrane biology, 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 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
5 citing papers in PubMed.
- Deep-ProBind: binding protein prediction with transformer-based deep learning model.BMC bioinformatics · 2025Article
- iTTCA-MFF: identifying tumor T cell antigens based on multiple feature fusion.Immunogenetics · 2022Article
- Employing a novel 2-gram subgroup intra pattern (2GSIP) with stacked auto encoder for membrane protein classification.Molecular biology reports · 2019Article
- Benchmark data for identifying multi-functional types of membrane proteins.Data in brief · 2016Article
- Prediction of Protein-Protein Interaction Sites with Machine-Learning-Based Data-Cleaning and Post-Filtering Procedures.The Journal of membrane biology · 2016Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Predicting membrane protein type is a challenging problem, particularly when the query proteins may simultaneously have two or more different types. Most of the existing methods can only be used to deal with the single-label proteins. Actually, multiple-label proteins should not be ignored because they usually bear some special functions worthy of in-depth studies. By introducing the "multi-labeled learning" and hybridizing evolution information through Grey-PSSM, a novel predictor called iMem-Seq is developed that can be used to deal with the systems containing both single and multiple types of membrane proteins. As a demonstration, the jackknife cross-validation was performed with iMem-Seq on a benchmark dataset of membrane proteins classified into the eight types, where some proteins belong to two or there types, but none has ≥25% pairwise sequence identity to any other in a same subset. It was demonstrated via the rigorous cross-validations that the new predictor remarkably outperformed all its counterparts. As a user-friendly web-server, iMem-Seq is freely accessible to the public at the website http://www.jci-bioinfo.cn/iMem-Seq .
Indexed as
Identifiers
25796484What 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.