Memory-efficient, accelerated protein interaction inference with blocked, multi-GPU D-SCRIPT.

Daniel E Schäffer, Samuel Sledzieski, Lenore Cowen, Bonnie Berger

Abstract read

In one paragraph

Article in Bioinformatics (Oxford, England), 2025. 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

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

4 authors.

Daniel E SchäfferComputer Science and Artificial Intelligence Laboratory, Massachusetts Institute of Technology, Cambridge, MA 02139, United States.ORCID 0000-0003-3608-152X

Samuel SledzieskiCenter for Computational Biology, Flatiron Institute, New York, NY 10010, United States.ORCID 0000-0002-0170-3029

Lenore CowenDepartment of Computer Science, Tufts University, Medford, MA 02155, United States.ORCID 0000-0001-6698-6413

Bonnie BergerComputer Science and Artificial Intelligence Laboratory, Massachusetts Institute of Technology, Cambridge, MA 02139, United States.ORCID 0000-0002-2724-7228

Funding

Manifold representations and active learning for 21 st century biologyR35GM141861 · NIGMS · MASSACHUSETTS INSTITUTE OF TECHNOLOGY · PI BERGER, BONNIE · 2021 to 2025

$1.9M

National Science Foundation Graduate Research Fellowship 2141064NIGMS NIH HHS R35 GM141861NIH HHS R35GM141861

6 · The paper itself

Abstract

summaryD-SCRIPT is a powerful tool for high-throughput inference of protein-protein interactions (PPIs), but it is expensive in time and memory to infer all PPIs for network-/proteome-level analyses. We introduce D-SCRIPT with blocked multi-GPU parallel inference, which substantially reduces memory usage across tasks and computational systems (13.8× for a representative large proteome) and enables multi-GPU parallelism. AVAILABILITY AND IMPLEMENTATION: Blocked multi-GPU parallel inference has been integrated into the main D-SCRIPT package, available at https://github.com/samsledje/D-SCRIPT. An archived version of the code at time of submission can be found at https://doi.org/10.5281/zenodo.16325182.

Indexed as

Computational BiologyProtein Interaction MappingSoftwareAlgorithmsProteomeProteome

Identifiers

PMID41075159

DOI10.1093/bioinformatics/btaf564

PMCPMC12553328

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.