Evidence map›Paper›PMID 36619977›Full record

ArticleiScience2023

Inferring direction of associations between histone modifications using a neural processes-based framework.

Ananthakrishnan Ganesan, Denis Dermadi, Laurynas Kalesinskas, Michele Donato, Rosalie Sowers, Paul J Utz, Purvesh Khatri

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in iScience, 2023. 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. Article
  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.

Ananthakrishnan GanesanInstitute for Immunity, Transplantation and Infection, School of Medicine, Stanford University, Stanford, CA 94305, USA.
Denis DermadiInstitute for Immunity, Transplantation and Infection, School of Medicine, Stanford University, Stanford, CA 94305, USA.
Laurynas KalesinskasInstitute for Immunity, Transplantation and Infection, School of Medicine, Stanford University, Stanford, CA 94305, USA.
Michele DonatoInstitute for Immunity, Transplantation and Infection, School of Medicine, Stanford University, Stanford, CA 94305, USA.
Rosalie SowersInstitute for Immunity, Transplantation and Infection, School of Medicine, Stanford University, Stanford, CA 94305, USA.
Paul J UtzInstitute for Immunity, Transplantation and Infection, School of Medicine, Stanford University, Stanford, CA 94305, USA.
Purvesh KhatriInstitute for Immunity, Transplantation and Infection, School of Medicine, Stanford University, Stanford, CA 94305, USA.

Funding

Using a tonsil organoid system to probe conditions for the induction of protective antibody and T cell responses to influenza.U19AI057229 · NIAID · STANFORD UNIVERSITY · PI Mark Morris Davis · 2003 to 2026
$88.5M
Translational Incubator CoreU19AI109662 · NIAID · STANFORD UNIVERSITY · PI GLENN, JEFFREY S · 2014 to 2019
$28.6M
Adjuvant Comparison and Characterization (HIV)75N93022C00052 · NIAID · STANFORD UNIVERSITY · PI PULENDRAN, BALI · 2022 to 2025
$19.2M
Systems biological assessment of T cell responses to vaccinationU19AI167903 · NIAID · STANFORD UNIVERSITY · PI BALI PULENDRAN · 2022 to 2026
$13.2M
Giant MagnetoResistive (GMR) Sensors for Measuring Influenza VaccineR01AI125197 · NIAID · STANFORD UNIVERSITY · PI UTZ, PAUL JOSEPH, WANG, SHAN X. · 2016 to 2021
$5.0M
Systems biological assessment of vaccination-induced protective immunity in African childrenU01AI165527 · NIAID · STANFORD UNIVERSITY · PI James Beeson, BALI PULENDRAN · 2022 to 2026
$4.0M
Ultra-Fast Epigenomic Maps for Monitoring AutoimmunityU19AI110491 · NIAID · STANFORD UNIVERSITY · PI UTZ, PAUL JOSEPH · 2014 to 2018
$3.6M
NIAID NIH HHS 75N93022C00052NIAID NIH HHS R01 AI125197NIAID NIH HHS U01 AI165527NIAID NIH HHS U19 AI057229NIAID NIH HHS U19 AI109662NIAID NIH HHS U19 AI110491NIAID NIH HHS U19 AI167903
6 · The paper itself

Abstract

Current technologies do not allow predicting interactions between histone post-translational modifications (HPTMs) at a system-level. We describe a computational framework, imputation-followed-by-inference, to predict directed association between two HPTMs using EpiTOF, a mass cytometry-based platform that allows profiling multiple HPTMs at a single-cell resolution. Using EpiTOF profiles of >55,000,000 peripheral mononuclear blood cells from 158 healthy human subjects, we show that neural processes (NP) have significantly higher accuracy than linear regression and

Indexed as

BiochemistryBiocomputational methodEpigeneticImmunity

Identifiers

PMID36619977
PMCPMC9813700

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.