Evidence map›Paper›PMID 39596878›Full record

ReviewBiology2024

Advancements in Multiple Myeloma Research: High-Throughput Sequencing Technologies, Omics, and the Role of Artificial Intelligence.

Alejandra Gutiérrez-González, Irene Del Hierro, Ariel Ernesto Cariaga-Martínez

Abstract readReview
In one paragraph

Review in Biology, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.

0numbers the graph read from it
0cells of the map it votes in
5citing 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

5 citing papers in PubMed.

  1. Mini review on the laboratory diagnosis of amyloidosis: An overview of methods, applications, and trends in analytical approaches.Biomedical papers of the Medical Faculty of the University Palacky, Olomouc, Czechoslovakia · 2026
    Review
  2. Review
  3. Article
  4. Article
  5. 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

3 authors.

Alejandra Gutiérrez-GonzálezDS-OMICS-Data Science and Omics, AI-Driven Biomedicine Group, Universidad Alfonso X el Sabio, 28619 Villanueva de la Cañada, Spain.
Irene Del HierroDS-OMICS-Data Science and Omics, AI-Driven Biomedicine Group, Universidad Alfonso X el Sabio, 28619 Villanueva de la Cañada, Spain.
Ariel Ernesto Cariaga-MartínezDS-OMICS-Data Science and Omics, AI-Driven Biomedicine Group, Universidad Alfonso X el Sabio, 28619 Villanueva de la Cañada, Spain.ORCID 0000-0003-1457-995X

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Multiple myeloma is a complex and challenging type of blood cancer that affects plasma cells in the bone marrow. In recent years, the development of advanced research techniques, such as omics approaches-which involve studying large sets of biological data like genes and proteins-and high-throughput sequencing technologies, has allowed researchers to analyze vast amounts of genetic information rapidly and gain new insights into the disease. Additionally, the advent of artificial intelligence tools has accelerated data analysis, enabling more accurate predictions and improved treatment strategies. This review aims to highlight recent research advances in multiple myeloma made possible by these novel techniques and to provide guidance for researchers seeking effective approaches in this field.

Indexed as

artificial intelligencebiomarkersdeep learningdiagnosticsmachine learningmultiple myelomanext-generation sequencingomicsprognosisthird-generation sequencing

Identifiers

PMID39596878
PMCPMC11592186

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.