A Transferable Machine Learning Framework for Predicting Transcriptional Responses of Genes Across Species.

Zhikai Liang, Xiaoxi Meng, James C Schnable

Abstract read

In one paragraph

Article in Methods in molecular biology (Clifton, N.J.), 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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0cells of the map it votes in

0citing papers in PubMed

–field-weighted citation impact

1 · What the graph read from it

What it found

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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

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3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

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5 · Who and what money

Authors and funding

3 authors.

Zhikai LiangDepartment of Plant and Microbial Biology, University of Minnesota, Saint Paul, MN, USA.

Xiaoxi MengDepartment of Horticultural Science, University of Minnesota, Saint Paul, MN, USA.

James C SchnableCenter for Plant Science Innovation, University of Nebraska-Lincoln, Lincoln, NE, USA. schnable@unl.edu.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Leveraging existing resources in studied species to predict gene functions has the potential to rapidly expand understanding of annotated genes in other, less well-studied, species with assembled genomes. However, orthology is not a reliable predictor for the transcriptional responses of genes to stress. Machine learning methods can quantitatively estimate expression patterns and gene functions using known annotations and collections of features describing each gene. In this chapter, we describe a supervised machine learning framework to predict stress-responsive genes across species using only features derived from nucleotide sequences, using the example of cold stress-responsive genes in different Panicoid grass species.

Indexed as

Machine LearningSupervised Machine LearningCold-Shock ResponsePoaceaeDinucleotide frequencyGene annotationGrassesMachine learningRandom forestTransfer learning

Identifiers

PMID37682485

DOI10.1186/s12864-020-06824-3

What OpenQuestion holds

Textmetadata

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Registered trials

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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.