multiVIB: A unified probabilistic contrastive learning framework for atlas-scale integration of single-cell multi-omics data.

Yang Xu, Stephen J Fleming, Brice Wang, Erin G Schoenbeck, Mehrtash Babadi, Bing-Xing Huo

Abstract readPreprint

In one paragraph

Article in bioRxiv : the preprint server for biology, 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

6 authors.

Yang XuData Sciences Platform, Broad Institute of MIT and Harvard, Cambridge, MA.ORCID 0000-0003-4173-4337

Stephen J FlemingData Sciences Platform, Broad Institute of MIT and Harvard, Cambridge, MA.ORCID 0000-0002-1885-4405

Brice WangData Sciences Platform, Broad Institute of MIT and Harvard, Cambridge, MA.

Erin G SchoenbeckData Sciences Platform, Broad Institute of MIT and Harvard, Cambridge, MA.

Mehrtash BabadiData Sciences Platform, Broad Institute of MIT and Harvard, Cambridge, MA.

Bing-Xing HuoData Sciences Platform, Broad Institute of MIT and Harvard, Cambridge, MA.ORCID 0000-0002-9389-2591

Funding

A scalable cloud-based framework for multi-modal mapping across single neuron omics, morphology and electrophysiologyRF1MH133777 · NIMH · BROAD INSTITUTE, INC. · PI HUO, BING-XING · 2023 to 2023

$2.4M

NIMH NIH HHS RF1 MH133777

6 · The paper itself

Abstract

Comprehensive brain cell atlases are essential for understanding neural functions and enabling translational insights. As single-cell technologies proliferate across experimental platforms, species, and modalities, these atlases must scale accordingly, calling for data integration framework that aligns heterogeneous datasets without erasing biologically meaningful variations. Existing tools typically address narrow integration settings, forcing researchers to assemble

Identifiers

PMID41383767

DOI10.1101/2025.11.29.691308

PMCPMC12694596

What Socratic 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 Socratic graph.