Evidence map›Paper›PMID 34873596›Full record

ArticleResearch square2021

Exploration of Dark Chemical Genomics Space via Portal Learning: Applied to Targeting the Undruggable Genome and COVID-19 Anti-Infective Polypharmacology.

Tian Cai, Li Xie, Muge Chen, Yang Liu, Di He, Shuo Zhang, Cameron Mura, Philip E Bourne, Lei Xie

Open access · greenAbstract readPreprint
PubMed Full text at PMC Publisher
In one paragraph

Article in Research square, 2021. 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, 4 citations in OpenAlex.

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

9 authors at 4 institutions in 1 country.

Tian CaiPh.D. Program in Computer Science, The Graduate Center, The City University of New York, New York, 10016, USA.
Li XieDepartment of Computer Science, Hunter College, The City University of New York, New York, 10065, USA.
Muge ChenMaster Program in Computer Science, Courant Institute of Mathematical Sciences, New York University.
Yang LiuDepartment of Computer Science, Hunter College, The City University of New York, New York, 10065, USA.
Di HePh.D. Program in Computer Science, The Graduate Center, The City University of New York, New York, 10016, USA.
Shuo ZhangPh.D. Program in Computer Science, The Graduate Center, The City University of New York, New York, 10016, USA.
Cameron MuraSchool of Data Science & Department of Biomedical Engineering, University of Virginia, Virginia, 22903, USA.
Philip E BourneSchool of Data Science & Department of Biomedical Engineering, University of Virginia, Virginia, 22903, USA.
Lei XiePh.D. Program in Computer Science, The Graduate Center, The City University of New York, New York, 10016, USA.
The Graduate Center, CUNY · USCity University of New York · USUniversity of Virginia · USCourant Institute of Mathematical Sciences · US

Funding

Omics data integration and analysis for structure-based multi-target drug designR01GM122845 · NIGMS · NORTHEASTERN UNIVERSITY · PI Lei Xie · 2017 to 2026
$3.0M
NIGMS NIH HHS R01 GM122845
6 · The paper itself

Abstract

Advances in biomedicine are largely fueled by exploring uncharted territories of human biology. Machine learning can both enable and accelerate discovery, but faces a fundamental hurdle when applied to unseen data with distributions that differ from previously observed ones-a common dilemma in scientific inquiry. We have developed a new deep learning framework, called

Indexed as

biomedicineCOVID-19machine learningportal learningSARS-CoV-2

Identifiers

PMID34873596
PMCPMC8647653
OpenAlexW3215146618

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.