ArticleBMC systems biology2016
Identifying the missing proteins in human proteome by biological language model.
Article in BMC systems biology, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 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
3 citing papers in PubMed, 4 citations in OpenAlex.
- Bridging artificial intelligence and biological sciences: a comprehensive review of large language models in bioinformatics.Briefings in bioinformatics · 2025Review
- Grammar of protein domain architectures.Proceedings of the National Academy of Sciences of the United States of America · 2019Article
- Effective computational detection of piRNAs using n-gram models and support vector machine.BMC bioinformatics · 2017Article
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 at 3 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundWith the rapid development of high-throughput sequencing technology, the proteomics research becomes a trendy field in the post genomics era. It is necessary to identify all the native-encoding protein sequences for further function and pathway analysis. Toward that end, the Human Proteome Organization lunched the Human Protein Project in 2011. However many proteins are hard to be detected by experiment methods, which becomes one of the bottleneck in Human Proteome Project. In consideration of the complicatedness of detecting these missing proteins by using wet-experiment approach, here we use bioinformatics method to pre-filter the missing proteins.
resultsSince there are analogy between the biological sequences and natural language, the n-gram models from Natural Language Processing field has been used to filter the missing proteins. The dataset used in this study contains 616 missing proteins from the "uncertain" category of the neXtProt database. There are 102 proteins deduced by the n-gram model, which have high probability to be native human proteins. We perform a detail analysis on the predicted structure and function of these missing proteins and also compare the high probability proteins with other mass spectrum datasets. The evaluation shows that the results reported here are in good agreement with those obtained by other well-established databases.
conclusionThe analysis shows that 102 proteins may be native gene-coding proteins and some of the missing proteins are membrane or natively disordered proteins which are hard to be detected by experiment methods.
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.