Evidence map›Paper›PMID 35595808›Full record

ArticleScientific reports2022

Machine learning phenomics (MLP) combining deep learning with time-lapse-microscopy for monitoring colorectal adenocarcinoma cells gene expression and drug-response.

M D'Orazio, M Murdocca, A Mencattini, P Casti, J Filippi, G Antonelli, D Di Giuseppe, M C Comes, C Di Natale, F Sangiuolo and 1 more

Open access · goldAbstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Scientific reports, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 19 papers.

0numbers the graph read from it
0cells of the map it votes in
19citing papers in PubMed
8.2field-weighted citation impact, top 3% of its field
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

19 citing papers in PubMed, 33 citations in OpenAlex.

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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

11 authors at 1 institution in 1 country.

M D'OrazioDepartment of Electronic Engineering, University of Rome Tor Vergata, via del Politecnico 1, 00133, Rome, Italy.
M MurdoccaDepartment of Biomedicine and Prevention, Tor Vergata University, Rome, Italy.
A MencattiniDepartment of Electronic Engineering, University of Rome Tor Vergata, via del Politecnico 1, 00133, Rome, Italy. mencattini@ing.uniroma2.it.
P CastiDepartment of Electronic Engineering, University of Rome Tor Vergata, via del Politecnico 1, 00133, Rome, Italy.
J FilippiDepartment of Electronic Engineering, University of Rome Tor Vergata, via del Politecnico 1, 00133, Rome, Italy.
G AntonelliDepartment of Electronic Engineering, University of Rome Tor Vergata, via del Politecnico 1, 00133, Rome, Italy.
D Di GiuseppeDepartment of Electronic Engineering, University of Rome Tor Vergata, via del Politecnico 1, 00133, Rome, Italy.
M C ComesDepartment of Electronic Engineering, University of Rome Tor Vergata, via del Politecnico 1, 00133, Rome, Italy.
C Di NataleDepartment of Electronic Engineering, University of Rome Tor Vergata, via del Politecnico 1, 00133, Rome, Italy.
F SangiuoloDepartment of Biomedicine and Prevention, Tor Vergata University, Rome, Italy.
E MartinelliDepartment of Electronic Engineering, University of Rome Tor Vergata, via del Politecnico 1, 00133, Rome, Italy.
University of Rome Tor Vergata · IT

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

High-throughput phenotyping is becoming increasingly available thanks to analytical and bioinformatics approaches that enable the use of very high-dimensional data and to the availability of dynamic models that link phenomena across levels: from genes to cells, from cells to organs, and through the whole organism. The combination of phenomics, deep learning, and machine learning represents a strong potential for the phenotypical investigation, leading the way to a more embracing approach, called machine learning phenomics (MLP). In particular, in this work we present a novel MLP platform for phenomics investigation of cancer-cells response to therapy, exploiting and combining the potential of time-lapse microscopy for cell behavior data acquisition and robust deep learning software architectures for the latent phenotypes extraction. A two-step proof of concepts is designed. First, we demonstrate a strict correlation among gene expression and cell phenotype with the aim to identify new biomarkers and targets for tailored therapy in human colorectal cancer onset and progression. Experiments were conducted on human colorectal adenocarcinoma cells (DLD-1) and their profile was compared with an isogenic line in which the expression of LOX-1 transcript was knocked down. In addition, we also evaluate the phenotypic impact of the administration of different doses of an antineoplastic drug over DLD-1 cells. Under the omics paradigm, proteomics results are used to confirm the findings of the experiments.

Indexed as

AdenocarcinomaColorectal NeoplasmsDeep LearningGene ExpressionHumansMachine LearningMicroscopyPhenomicsPhenotypeTime-Lapse Imaging

Identifiers

PMID35595808
PMCPMC9123013
OpenAlexW4281252324

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.