Development of resources for training machine learning systems for the eye

IF 3 3区 医学 Q1 OPHTHALMOLOGY
Salil A. Lachke
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引用次数: 0

Abstract

Regulation of distinct molecular pathways in coordinately developing tissues drives eye development. This process involves factors in signaling, transcription, translation and post-transcriptional control that together determine the proteome of individual cells. Characterizing these regulatory events and their relationships with each other in distinct ocular tissues will lead to the derivation of gene regulatory networks (GRNs) for eye development, which in turn will allow building of the “developmental oculome” (Lachke, Maas (2010) Wiley Interdiscip Rev Syst Biol Med 2(3):305-323 doi: 10.1002/wsbm.59). Toward this goal, we generated and analyzed various omics datasets on developing eye tissues, performed functional characterization of key molecules, and developed a web resource called iSyTE (Anand, Lachke (2017) Exp Eye Res 156:22-33 PMC5026553; Kakrana et al (2018) Nucleic Acids Res 46(D1): D875-D885 doi: 10.1093/nar/gkx837; Lachke (2022) Exp Eye Res 214:108889 PMC8792301). To extract further insights from these omics data, it is necessary to analyze them in the context of experimentally verified data on the eye. These data can be used for training machine learning systems to develop models for eye development. The data used for training represents a an important factor in machine learning systems, i.e. the quality of these data is a key contributor that determines the robustness of the resulting models. There is a wealth of molecular functional (i.e. experimental evidence-based) data on the eye that has been generated by researchers worldwide over the past several decades. These individual regulatory data “nodes” (regulators and targets) – currently “static” in the literature with limited connectivity – hold rich potential to define detailed, and importantly, experimentally validated GRNs that can be used for training machine learning systems. As proof of principle, we have focused on ocular lens development and curated mechanistic data from individual gene perturbation evidences in the mouse from >100 original research articles. This has led to definition of thousands of experimentally validated relationships (edges) between key factors and their targets (nodes) in lens development. These resources serve as strong foundational data for training machine learning systems and represent the essential first step toward advancing our understanding of the natural computing governing eye development, and how its perturbation results in ocular developmental disorders.

开发用于训练机器学习系统的资源
在协调发育的组织中调节不同的分子通路驱动眼睛的发育。这一过程涉及信号、转录、翻译和转录后控制等因素,它们共同决定了单个细胞的蛋白质组。表征这些调节事件及其在不同眼部组织中的相互关系将导致眼睛发育的基因调控网络(grn)的衍生,这反过来将允许建立“发育眼”(Lachke, Maas (2010) Wiley interdisp Rev system Biol Med 2(3):305-323 doi: 10.1002/wsbm.59)。为了实现这一目标,我们生成并分析了各种关于眼部组织发育的组学数据集,对关键分子进行了功能表征,并开发了一个名为iSyTE (Anand, Lachke (2017) Exp eye Res 156:22-33 PMC5026553;Kakrana et al . (2018) Nucleic Acids Res 46(D1): D875-D885 doi: 10.1093/nar/gkx837;Lachke (2022) Exp Eye Res 214:108889 PMC8792301)。为了从这些组学数据中获得进一步的见解,有必要在实验验证的眼睛数据的背景下对它们进行分析。这些数据可以用于训练机器学习系统来开发眼睛发育模型。用于训练的数据代表了机器学习系统中的一个重要因素,即这些数据的质量是决定最终模型鲁棒性的关键因素。在过去的几十年里,世界各地的研究人员已经产生了丰富的关于眼睛的分子功能(即实验证据)数据。这些单独的监管数据“节点”(监管机构和目标)-目前在文献中是“静态的”,连接有限-具有丰富的潜力,可以定义详细的,重要的是,经过实验验证的grn,可用于训练机器学习系统。作为原则的证明,我们关注了晶状体的发育,并从100篇原创研究文章中整理了小鼠个体基因扰动证据的机制数据。这导致了透镜发育中关键因素与其目标(节点)之间数千个实验验证关系(边缘)的定义。这些资源为训练机器学习系统提供了强大的基础数据,并代表了推进我们对控制眼睛发育的自然计算以及其扰动如何导致眼睛发育障碍的理解的重要的第一步。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
Acta Ophthalmologica
Acta Ophthalmologica 医学-眼科学
CiteScore
7.60
自引率
5.90%
发文量
433
审稿时长
6 months
期刊介绍: Acta Ophthalmologica is published on behalf of the Acta Ophthalmologica Scandinavica Foundation and is the official scientific publication of the following societies: The Danish Ophthalmological Society, The Finnish Ophthalmological Society, The Icelandic Ophthalmological Society, The Norwegian Ophthalmological Society and The Swedish Ophthalmological Society, and also the European Association for Vision and Eye Research (EVER). Acta Ophthalmologica publishes clinical and experimental original articles, reviews, editorials, educational photo essays (Diagnosis and Therapy in Ophthalmology), case reports and case series, letters to the editor and doctoral theses.
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