Machine learning approaches for image classification in developmental biology and clinical embryology.

IF 3.7 2区生物学 Q1 DEVELOPMENTAL BIOLOGY

Development Pub Date : 2025-02-15 Epub Date: 2025-02-17 DOI:10.1242/dev.202066

Camilla Mapstone, Berenika Plusa

引用次数: 0

Abstract

The rapid increase in the amount of available biological data together with increasing computational power and innovative new machine learning algorithms has resulted in great potential for machine learning approaches to revolutionise image analysis in developmental biology and clinical embryology. In this Spotlight, we provide an introduction to machine learning for developmental biologists interested in incorporating machine learning techniques into their research. We give an overview of essential machine learning concepts and models and describe a few recent examples of how these techniques can be used in developmental biology. We also briefly discuss latest advancements in the field and how it might develop in the future.

查看原文本刊更多论文

求助全文

约1分钟内获得全文求助全文

来源期刊

Development 生物-发育生物学

CiteScore

6.70

自引率

4.30%

发文量

433

审稿时长

3 months

期刊介绍： Development’s scope covers all aspects of plant and animal development, including stem cell biology and regeneration. The single most important criterion for acceptance in Development is scientific excellence. Research papers (articles and reports) should therefore pose and test a significant hypothesis or address a significant question, and should provide novel perspectives that advance our understanding of development. We also encourage submission of papers that use computational methods or mathematical models to obtain significant new insights into developmental biology topics. Manuscripts that are descriptive in nature will be considered only when they lay important groundwork for a field and/or provide novel resources for understanding developmental processes of broad interest to the community. Development includes a Techniques and Resources section for the publication of new methods, datasets, and other types of resources. Papers describing new techniques should include a proof-of-principle demonstration that the technique is valuable to the developmental biology community; they need not include in-depth follow-up analysis. The technique must be described in sufficient detail to be easily replicated by other investigators. Development will also consider protocol-type papers of exceptional interest to the community. We welcome submission of Resource papers, for example those reporting new databases, systems-level datasets, or genetic resources of major value to the developmental biology community. For all papers, the data or resource described must be made available to the community with minimal restrictions upon publication. To aid navigability, Development has dedicated sections of the journal to stem cells & regeneration and to human development. The criteria for acceptance into these sections is identical to those outlined above. Authors and editors are encouraged to nominate appropriate manuscripts for inclusion in one of these sections.