1篇
您的检索式：作者名="Chad A.Steed"
|
|
|
题名
|
作者
|
年代
|
出处
|
被引量
|
| 1 | Deep data analytics for genetic engineering of diatoms linking genotype to phenotype via machine learning显示文摘Genome engineering for materials synthesis is a promising avenue for manufacturing materials with unique properties under ambient conditions.Biomineralization in diatoms,unicellular algae that use silica to construct micron-scale cell walls with nanoscale features,is an attractive candidate for functional synthesis of materials for applications including photonics,sensing,filtration,and drug delivery.Therefore,controllably modifying diatom structure through targeted genetic modifications for these applications is a very promising field.In this work,we used gene knockdown in Thalassiosira pseudonana diatoms to create modified strains with changes to structural morphology and linked genotype to phenotype using supervised machine learning.An artificial neural network(NN)was developed to distinguish wild and modified diatoms based on the SEM images of frustules exhibiting phenotypic changes caused by a specific protein(Thaps3_21880),resulting in 94% detection accuracy.Class activation maps visualized physical changes that allowed the NNs to separate diatom strains,subsequently establishing a specific gene that controls pores.A further NN was created to batch process image data,automatically recognize pores,and extract pore-related parameters.Class interrelationship of the extracted paraments was visualized using a multivariate data visualization tool,called CrossVis,and allowed to directly link changes in morphological diatom phenotype of pore size and distribution with changes in the genotype. | Artem A.Trofimov Alison A.Pawlicki Nikolay Borodinov Shovon Mandal Teresa J.Mathews Mark Hildebrand Maxim A.Ziatdinov Katherine A.Hausladen Paulina K.Urbanowicz Chad A.Steed Anton V.Ievlev Alex Belianinov Joshua K.Michener Rama Vasudevan Olga S.Ovchinnikova | 2019 | npj Computational Materials2019，，1： | 0 |
您所在的机构暂未开通此部分的功能，请联系管理员