Evidence map›Paper›PMID 42071149›Full record

ArticleAdvances in experimental medicine and biology2026

Primer on Modelling Approaches for Omics Data.

Guillem A Santamaria, João Miranda, Margarida Carrolo, Sara Silva, Francisco Rodrigues Pinto

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Article in Advances in experimental medicine and biology, 2026. 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

5 authors.

Guillem A SantamariaBioISI - Biosystems & Integrative Sciences Institute, Faculty of Sciences, University of Lisboa, Lisbon, Portugal.
João MirandaBioISI - Biosystems & Integrative Sciences Institute, Faculty of Sciences, University of Lisboa, Lisbon, Portugal.
Margarida CarroloCentro Hospitalar Universitário Lisboa Norte, Hospital de Santa Maria, Lisbon, Portugal.
Sara SilvaLASIGE, Faculdade de Ciências da Universidade de Lisboa, Lisbon, Portugal.
Francisco Rodrigues PintoBioISI - Biosystems & Integrative Sciences Institute, Faculty of Sciences, University of Lisboa, Lisbon, Portugal. frpinto@ciencias.ulisboa.pt.ORCID https://orcid.org/0000-0002-4217-0054

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Omics data allow us to study complex biological systems. Mathematical models can help us understand these systems and extract more knowledge from the data. In this chapter, we introduce three types of mathematical models that can be used to potentiate the interpretation of omics datasets: statistical, machine learning and mechanistic models. We delve into the characteristics of these modelling approaches, the essential stages involved in their development and their potential applications to omics datasets.

Indexed as

GenomicsModels, BiologicalModels, StatisticalAnimalsHumansMachine LearningMultiomicsMachine learning modelMechanistic modelOmics dataStatistical model

Identifiers

PMID42071149

What OpenQuestion holds

Textmetadata
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.