Evidence map›Paper›PMID 36140783›Full record

ArticleGenes2022

Multinomial Convolutions for Joint Modeling of Regulatory Motifs and Sequence Activity Readouts.

Minjun Park, Salvi Singh, Samin Rahman Khan, Mohammed Abid Abrar, Francisco Grisanti, M Sohel Rahman, Md Abul Hassan Samee

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Genes, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing papers in PubMed
–field-weighted citation impact
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

2 citing papers in PubMed.

  1. Review
  2. Article
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

7 authors.

Minjun ParkDepartment of Integrative Physiology, Baylor College of Medicine, Houston, TX 77030, USA.
Salvi SinghDepartment of Integrative Physiology, Baylor College of Medicine, Houston, TX 77030, USA.
Samin Rahman KhanComputer Science and Engineering, Bangladesh University of Engineering and Technology, Dhaka 1000, Bangladesh.ORCID 0000-0002-6429-8569
Mohammed Abid AbrarComputer Science and Engineering, Brac University, Dhaka 1212, Bangladesh.
Francisco GrisantiDepartment of Integrative Physiology, Baylor College of Medicine, Houston, TX 77030, USA.
M Sohel RahmanComputer Science and Engineering, Bangladesh University of Engineering and Technology, Dhaka 1000, Bangladesh.ORCID 0000-0001-9419-6478
Md Abul Hassan SameeDepartment of Integrative Physiology, Baylor College of Medicine, Houston, TX 77030, USA.ORCID 0000-0002-7472-1156

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

A common goal in the convolutional neural network (CNN) modeling of genomic data is to discover specific sequence motifs. Post hoc analysis methods aid in this task but are dependent on parameters whose optimal values are unclear and applying the discovered motifs to new genomic data is not straightforward. As an alternative, we propose to learn convolutions as multinomial distributions, thus streamlining interpretable motif discovery with CNN model fitting. We developed MuSeAM (Multinomial CNNs for Sequence Activity Modeling) by implementing multinomial convolutions in a CNN model. Through benchmarking, we demonstrate the efficacy of MuSeAM in accurately modeling genomic data while fitting multinomial convolutions that recapitulate known transcription factor motifs.

Indexed as

GenomicsNeural Networks, ComputerTranscription FactorsTranscription Factorsconvolutional neural networksmotifsMPRAmultinomial convolutional neural networksmultinomial convolutions

Identifiers

PMID36140783
PMCPMC9498894

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.