Evidence map›Paper›PMID 32784445›Full record

ReviewInternational journal of molecular sciences2020

Computational Methods for Predicting Functions at the mRNA Isoform Level.

Sambit K Mishra, Viraj Muthye, Gaurav Kandoi

Open access · goldAbstract readReview
In one paragraph

Review in International journal of molecular sciences, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.

0numbers the graph read from it
0cells of the map it votes in
3citing papers in PubMed
0.4field-weighted citation impact, top 40% of its field
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

3 citing papers in PubMed, 7 citations in OpenAlex.

  1. Article
  2. Article
  3. Review
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

3 authors at 1 institution in 1 country.

Sambit K MishraBioinformatics and Computational Biology Program, Iowa State University, Ames, IA 50011, USA.
Viraj MuthyeBioinformatics and Computational Biology Program, Iowa State University, Ames, IA 50011, USA.
Gaurav KandoiBioinformatics and Computational Biology Program, Iowa State University, Ames, IA 50011, USA.ORCID 0000-0003-0559-9481
Iowa State University · US

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Multiple mRNA isoforms of the same gene are produced via alternative splicing, a biological mechanism that regulates protein diversity while maintaining genome size. Alternatively spliced mRNA isoforms of the same gene may sometimes have very similar sequence, but they can have significantly diverse effects on cellular function and regulation. The products of alternative splicing have important and diverse functional roles, such as response to environmental stress, regulation of gene expression, human heritable, and plant diseases. The mRNA isoforms of the same gene can have dramatically different functions. Despite the functional importance of mRNA isoforms, very little has been done to annotate their functions. The recent years have however seen the development of several computational methods aimed at predicting mRNA isoform level biological functions. These methods use a wide array of proteo-genomic data to develop machine learning-based mRNA isoform function prediction tools. In this review, we discuss the computational methods developed for predicting the biological function at the individual mRNA isoform level.

Indexed as

Alternative SplicingAnimalsComputational BiologyGene Regulatory NetworksHumansMachine LearningRNA IsoformsRNA, MessengerRNA IsoformsRNA, Messengeralternative splicingdeep learninggene ontologymachine learningmRNA isoformsmultiple instance learningrecommender systemsRNA-seq

Identifiers

PMID32784445
PMCPMC7460821
OpenAlexW3043243129

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

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 OpenQuestion graph.