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    Supplementary materials for positive-unlabeled learning identifies vaccine candidate antigens in the malaria parasite Plasmodium falciparum
    (2023) Chou, Renee Ti; Ouattara, Amed; Adams, Matthew; Berry, Andrea A.; Takala-Harrison, Shannon; Cummings, Michael P.
    Malaria vaccine development is hampered by extensive antigenic variation and complex life stages of Plasmodium species. Vaccine development has focused on a small number of antigens identified prior to availability of the P. falciparum genome. In this study, we implement a machine learning-based reverse vaccinology approach to predict potential new malaria vaccine candidate antigens. We assemble and analyze P. falciparum proteomic, structural, functional, immunological, genomic, and transcriptomic data, and use positive-unlabeled learning to predict potential antigens based on the properties of known antigens and remaining proteins. We prioritize candidate antigens based on model performance on reference antigens with different genetic diversity and quantify the protein properties that contribute the most to identifying top candidates. Candidate antigens are characterized by gene essentiality, gene ontology, and gene expression in different life stages to inform future vaccine development. This approach provides a framework for identifying and prioritizing candidate vaccine antigens for a broad range of pathogens.
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    Supplementary materials for machine learning-driven multifunctional peptide engineering for sustained ocular drug delivery
    (2023) Chou, Renee Ti; Hsueh, Henry T.; Rai, Usha; Liyanage, Wathsala; Kim, Yoo Chun; Appell, Matthew B.; Pejavar, Jahnavi; Leo, Kirby T.; Davison, Charlotte; Kolodziejski, Patricia; Mozzer, Ann; Kwon, HyeYoung; Sista, Maanasa; Anders, Nicole M.; Hemingway, Avelina; Rompicharla, Sri Vishnu Kiran; Edwards, Malia; Pitha, Ian; Hanes, Justin; Cummings, Michael P.; Ensign, Laura M.; Cummings, Michael P.; Ensign, Laura M.
    Sustained drug delivery strategies have many potential benefits for treating a range of diseases, particularly chronic diseases that require treatment for years. For many chronic ocular diseases, patient adherence to eye drop dosing regimens and the need for frequent intraocular injections are significant barriers to effective disease management. Here, we utilize peptide engineering to impart melanin binding properties to peptide-drug conjugates to act as a sustained-release depot in the eye. We developed a super learning-based methodology to engineer multifunctional peptides that efficiently enter cells, bind to melanin, and have low cytotoxicity. When the lead multifunctional peptide (HR97) was conjugated to brimonidine, an intraocular pressure (IOP)-lowering drug that is prescribed for three times per day topical dosing, IOP reduction was observed for up to 18 days after a single intracameral HR97-brimonidine injection in rabbits. Further, the cumulative IOP-lowering effect was increased ~17-fold compared to free brimonidine injection. Engineered multifunctional peptide-drug conjugates are a promising approach for providing sustained therapeutic delivery in the eye and beyond.
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    Development of cortical connections in the absence of NMDA receptors
    (2022) Deng, Rongkang; Kao, Joseph; Kanold, Patrick
    Figure_data.mat has all the data points used to generate the boxplots and CDF plot. Data are stored in variables fX (X = 1 to 10) for each figure. plot_figureX.m (X = 1 to 10) can be used to regenerate boxplots and CDF plot.
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    Ascidian gene-expression profiles
    (Springer Nature, 2002-09-25) Jeffery, William R
    With the advent of gene-expression profiling, a large number of genes can now be investigated simultaneously during critical stages of development. This approach will be particularly informative in studies of ascidians, basal chordates whose genomes and embryology are uniquely suited for mapping developmental gene networks.
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    Visualization and analysis of microarray and gene ontology data with treemaps
    (Springer Nature, 2004-06-28) Baehrecke, Eric H; Dang, Niem; Babaria, Ketan; Shneiderman, Ben
    The increasing complexity of genomic data presents several challenges for biologists. Limited computer monitor views of data complexity and the dynamic nature of data in the midst of discovery increase the challenge of integrating experimental results with information resources. The use of Gene Ontology enables researchers to summarize results of quantitative analyses in this framework, but the limitations of typical browser presentation restrict data access. Here we describe extensions to the treemap design to visualize and query genome data. Treemaps are a space-filling visualization technique for hierarchical structures that show attributes of leaf nodes by size and color-coding. Treemaps enable users to rapidly compare sizes of nodes and sub-trees, and we use Gene Ontology categories, levels of RNA, and other quantitative attributes of DNA microarray experiments as examples. Our implementation of treemaps, Treemap 4.0, allows user-defined filtering to focus on the data of greatest interest, and these queried files can be exported for secondary analyses. Links to model system web pages from Treemap 4.0 enable users access to details about specific genes without leaving the query platform. Treemaps allow users to view and query the data from an experiment on a single computer monitor screen. Treemap 4.0 can be used to visualize various genome data, and is particularly useful for revealing patterns and details within complex data sets.