Evidence map›Paper›PMID 40037787›Full record

ReviewGenomics, proteomics & bioinformatics2025

Biological Data Resources and Machine Learning Frameworks for Hematology Research.

Ying Yi, Yongfei Hu, Juanjuan Kang, Qifa Liu, Yan Huang, Dong Wang

Abstract readReview
PubMed Full text at PMC Publisher
In one paragraph

Review in Genomics, proteomics & bioinformatics, 2025. 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

6 authors.

Ying YiInstitute of Dermatology and Venereology, Dermatology Hospital, Southern Medical University, Guangzhou 510091, China.ORCID 0009-0003-1925-1942
Yongfei HuInstitute of Dermatology and Venereology, Dermatology Hospital, Southern Medical University, Guangzhou 510091, China.ORCID 0000-0002-9288-0994
Juanjuan KangDepartment of Bioinformatics, Guangdong Province Key Laboratory of Molecular Tumor Pathology, School of Basic Medical Sciences, Southern Medical University, Guangzhou 510515, China.ORCID 0000-0003-1076-4672
Qifa LiuDepartment of Hematology, Nanfang Hospital, Southern Medical University, Guangzhou 510515, China.ORCID 0000-0003-2623-8394
Yan HuangCancer Research Institute, School of Basic Medical Sciences, Southern Medical University, Guangzhou 510515, China.ORCID 0000-0002-7940-8468
Dong WangInstitute of Dermatology and Venereology, Dermatology Hospital, Southern Medical University, Guangzhou 510091, China.ORCID 0000-0002-6860-6864

Funding

Guangdong Basic and Applied Basic Research Foundation 2021A1515110653Guangdong Basic and Applied Basic Research Foundation 2022A1515011253Guangdong Basic and Applied Basic Research Foundation 2024A1515011769National Key R&D Project of China 2019YFA0801800National Key R&D Project of China 2021YFC2500300National Key R&D Project of China 2022YFA0806300National Natural Science Foundation of Chin 82070109National Natural Science Foundation of Chin 82370106
6 · The paper itself

Abstract

Hematology research has greatly benefited from the integration of diverse biological data resources and advanced machine learning (ML) frameworks. This integration has not only deepened our understanding of blood diseases such as leukemia and lymphoma, but also enhanced diagnostic accuracy and personalized treatment strategies. By applying ML algorithms to analyze large-scale biological data, researchers can more effectively identify disease patterns, predict treatment responses, and provide new perspectives for the diagnosis and treatment of hematologic disorders. Here, we provide an overview of the current landscape of biological data resources and the application of ML frameworks pertinent to hematology research.

Indexed as

Hematologic DiseasesHematologyMachine LearningHumansBiological resourceClinical resourceHematologic disorderHematopoiesisMachine learning

Identifiers

PMID40037787
PMCPMC12321297

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Registered trials

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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.