Evidence map›Paper›PMID 40027721›Full record

ArticlebioRxiv : the preprint server for biology2025

ralphi: a deep reinforcement learning framework for haplotype assembly.

Enzo Battistella, Anant Maheshwari, Barış Ekim, Bonnie Berger, Victoria Popic

Abstract readPreprint
PubMed Full text at PMC Publisher
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

5 · Who and what money

Authors and funding

5 authors.

Enzo BattistellaBroad Clinical Labs, Broad Institute of MIT and Harvard, Cambridge, MA.
Anant MaheshwariBroad Institute of MIT and Harvard, Cambridge, MA.
Barış EkimBroad Clinical Labs, Broad Institute of MIT and Harvard, Cambridge, MA.
Bonnie BergerBroad Institute of MIT and Harvard, Cambridge, MA.
Victoria PopicBroad Clinical Labs, Broad Institute of MIT and Harvard, Cambridge, MA.

Funding

Manifold representations and active learning for 21 st century biologyR35GM141861 · NIGMS · MASSACHUSETTS INSTITUTE OF TECHNOLOGY · PI BERGER, BONNIE · 2021 to 2025
$1.9M
A deep reinforcement learning framework for haplotype assemblyR21HG013567 · NHGRI · BROAD INSTITUTE, INC. · PI POPIC, VICTORIA · 2024 to 2024
$458k
NHGRI NIH HHS R21 HG013567NIGMS NIH HHS R35 GM141861
6 · The paper itself

Abstract

Haplotype assembly is the problem of reconstructing the combination of alleles on the maternally and paternally inherited chromosome copies. Individual haplotypes are essential to our understanding of how combinations of different variants impact phenotype. In this work, we focus on read-based haplotype assembly of individual diploid genomes, which reconstructs the two haplotypes directly from read alignments at variant loci. We introduce ralphi, a novel deep reinforcement learning framework for haplotype assembly, which integrates the representational power of deep learning with reinforcement learning to accurately partition read fragments into their respective haplotype sets. To set the reward objective for reinforcement learning, our approach uses the classic reduction of the problem to the

Identifiers

PMID40027721
PMCPMC11870604

What Socratic holds

Textmetadata
LicenceCC BY-NC
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.