ArticleInternational journal of data mining and bioinformatics2010
Prediction of alternatively spliced exons using support vector machines.
Article in International journal of data mining and bioinformatics, 2010. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 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
2 citing papers in PubMed.
- An empirical study of ensemble-based semi-supervised learning approaches for imbalanced splice site datasets.BMC systems biology · 2015Article
- Kassiopeia: a database and web application for the analysis of mutually exclusive exomes of eukaryotes.BMC genomics · 2014Article
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
Abstract
Alternative splicing is a mechanism for generating different gene transcripts (called isoforms) from the same genomic sequence. In this paper, we explore the predictive power of a large set of diverse gene features that have been experimentally shown to have effect on alternative splicing. We use such features to build support vector machine classifiers for predicting alternatively spliced exons. Experimental results show that classifiers built from the diverse set of features give better results than those that consider only basic sequence features. Furthermore, we use feature selection methods to identify the most informative features for the prediction problem at hand.
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.