Open Call for Research Projects
Are you a researcher with a research project that requires advanced computational skills?
If you have a research project that requires advanced computational skills, let us help you.
Tell us more about your research project and the type of skills that you need by filling out the Research Engagement intake form. One of our team members will be in touch to set up a consultation.
2024 Research projects
Facilitator: Deshon Miguel
Project Title: Enhanced Models for Antibody Epitope Prediction
Researcher: Neal Woodbury
Project Description: Peptide array-based antibody molecular recognition profiles can be used to train machine learning models to predict the amino acid sequences most involved in antibody recognition. This project will incorporate docking software and postprocessing data analysis into this lead binding site validation process. The goal will be to provide relative estimated binding levels of the off-target sequences and structural regions relative to the actual target sites. Workflows developed in the Sol shell environment will apply shell scripting to implement the preprocessing, docking, and postprocessing stages. Additionally, Job Arrays will be employed to manage these runs at a large scale.
Facilitator: Juan Jose Garcia Mesa
Project Title: Deep Generative Models for Animal Behavior: Deep Faking Wasps to Understand Individual Recognition
Researcher: Ted Pavlic
Project Description: The research project aims to explore individual recognition in animal behavior, specifically focusing on paper wasps of the genus Polistes. While individual recognition has been extensively studied in vertebrates, its understanding of invertebrates is limited. Paper wasps, known for their complex social systems, offer a unique opportunity for observation in laboratory settings. The proposal involves using generative AI to create deep fakes of wasp interactions, where videos of interactions between wasps are altered to depict the faces of other individuals. This approach allows for manipulating social hierarchies and testing hypotheses about hierarchy formation and maintenance.
Facilitator: Dan Jackson
Project Title: CoMSES Net: the Network for Computational Modeling in the Social and Ecological Sciences
Researcher: Allen Lee
Project Description: CoMSES Net is an NSF-funded science gateway and global research community that serves computational modelers interested in studying complex social and ecological systems. This exploratory project in AI/ML aims to 1/ summarize existing computational models 2/ provide concrete guidance on model analysis and documentation via a chatGPT / llama-like interface and 3/ improve computational model discoverability across multiple domains. Working closely with the COMSES development team and student developers, prototype curation workflows will be implemented to explore new research directions.
2023 Research projects
Facilitator: Rebecca Belshe
Project title: Enabling Multiple Allele Effects Faculty
Researcher: Michael Lynch
Project description: This project pairs CI facilitator Rebecca Belshe with Michael Lynch of the ASU Center for Mechanisms of Evolution. A computational model of phylogenetic lineages incorporating effects such as selection and drift incurs a large memory footprint with multiple mutations. Several multidimensional arrays have been employed to characterize the dynamics, including interference between these mutations, within the population. These arrays are very sparse, and their scale limits the number of mutations that can be included in the model. This project aims to explore and implement a new memory model, benchmark its impact on performance, deliver a new working code, and present a summary report. The project is scheduled to conclude in January 2024.
Facilitator: Susan Massey
Project title: TCGA Sex Chromosome Status Pipeline Faculty
Researcher: Melissa Wilson
Project description: This project pairs CI facilitator Susan Massey with Melissa Wilson of the ASU School of Life Sciences. The goal of this project is to develop a workflow for the analysis of sex chromosomes in cancer genomics. Sex chromosomes have long been overlooked in cancer research. As such, there is a need to assess the status of sex chromosomes in the existing sequenced samples of the Cancer Genome Atlas and evaluate their impact on patient outcomes. This project supports the effort to study this through the development of a computational workflow to import TCGA genomic data and determine the presence of a Y chromosome and/or the status of XIST, a marker of X chromosome inactivation, in the samples. The resulting inferred sex chromosome complement will be compared to subjects’ clinically recorded sex, and will then be used in further analyses of outcome (survival analysis) and disease severity (cancer stage). After completion, this reproducible workflow can be applied to additional cancer types. The project is scheduled to conclude in February 2024 with the delivery of a data table, an R script, and a summary report.