Evidence map›Paper›PMID 42116171›Full record

ArticleGenome biology2026

Data-driven AI system for learning how to run transcript assemblers.

Yihang Shen, Zhiwen Yan, Carl Kingsford

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Genome biology, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
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

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

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

3 authors.

Yihang ShenRay and Stephanie Lane Computational Biology Department, School of Computer Science, Carnegie Mellon University, 5000 Forbes Avenue, Pittsburgh, PA, USA.
Zhiwen YanRay and Stephanie Lane Computational Biology Department, School of Computer Science, Carnegie Mellon University, 5000 Forbes Avenue, Pittsburgh, PA, USA.
Carl KingsfordRay and Stephanie Lane Computational Biology Department, School of Computer Science, Carnegie Mellon University, 5000 Forbes Avenue, Pittsburgh, PA, USA. carlk@cs.cmu.edu.

Funding

Improved genomic sketching for MUMmer and metagenomicsR01HG012470 · NHGRI · CARNEGIE-MELLON UNIVERSITY · PI KINGSFORD, CARLETON LEE, MARÇAIS, GUILLAUME · 2022 to 2025
$1.7M
National Science Foundation DBI-1937540, III-2232121NIH HHS R01HG012470
6 · The paper itself

Abstract

We introduce AutoTuneX, a data-driven, AI system designed to automatically predict optimal parameters for transcript assemblers - tools for reconstructing transcripts from the reads in a given RNA-seq sample. AutoTuneX is built by learning parameter knowledge from existing RNA-seq samples and transferring this knowledge to unseen samples. On 1588 human RNA-seq samples tested with two transcript assemblers, AutoTuneX predicts parameters that resulted in 98% of samples achieving more accurate transcript assembly compared to using default parameters, with some samples experiencing up to a 600% improvement in AUC. AutoTuneX offers a new strategy for automatically optimizing use of sequence analysis tools.

Indexed as

Artificial IntelligenceRNA-SeqSequence Analysis, RNASoftwareAlgorithmsHumansBayesian optimizationContrastive learningParameter advisingTranscript assembly

Identifiers

PMID42116171
PMCPMC13335200

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC-ND
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.