Using data science to enhance protein biogenesis for biotechnology and medicine
Engineered proteins are at the heart of biotechnology and the biopharmaceutical industry. The research community now has several tools available to develop novel protein structures and functions. Computational methods can link fully designed structures back to sequence, and directed evolution can create enzymes that catalyze reactions not found in biology. However, these approaches rely on...
Making light work of dark matter - Algorithms for astronomical matter distributions
The current cosmological paradigm posits that at early times the distribution of matter in our universe had the statistics of a Gaussian process, and evolved primarily via gravitational collapse into the highly non-linear structures we see today. Both the numerical simulations and the observational catalogues produce big data that challenges the computational scaling of our...
An ‘instantaneous’ measure of dynamic functional connectivity
Assessing the function of late-stage cortical processing regions, such as prefrontal cortex, is notoriously challenging. Yet it is these areas that are hypothesized to subserve complex higher-order processes including self-evaluation of decisional uncertainty and even perceptual awareness. How can we measure the degree to which late-stage cortical processing areas have access to the representational content...
Tree Atlas of the California
Vegetation maps are a valuable resource for those interested in vegetation status and change. A map is a static baseline, but distributions frozen in time provide guidance on relationships between species and topography, geologic substrate, surface hydrology, and climate; vegetation maps in time-series can be used to document vegetation change. This atlas comprises maps of...
Open Geospatial Data Science
In this talk, I present an overview of spatial data science research occurring at the newly formed UCR Center for Geospatial Sciences. I first examine the broader context of geospatial data science and its intersection with the open source and open science movements. Next, I provide an overview of the open source Python Spatial Analysis...
Inference of Chromosome-length Haplotypes using Genomic Data of Three to Five Single Gametes
Knowledge of chromosome-length haplotypes will not only advance our understanding of the relationship between DNA and phenotypes, but will also promote a variety of important genetic applications. The current diploid-based phasing methods are costly and only produce haplotype fragments, whereas the alternatives based on analysis of haploid gametes, which are still in their early development...
A Machine Learning Approach for Improving the Accuracy of Medical Diagnoses
The usefulness of two-class statistical classifiers is limited when one or both of the conditional miss-classification rates are unacceptably high. Incorporating a neutral zone region into the classifier provides a mechanism to refer ambiguous cases to follow-up where additional information might be obtained to clarify the classification decision. Through the use of the neutral zone...
Something old in someplace new: Exploring the genetics of adaptation in Capsella and Hordeum
Plants show a remarkable ability to survive in diverse environments. Our work seeks to identify the genetic basis of plant adaptation through computational analysis of whole genome sequences isolated from thousands of individuals. I will discuss the unequal retention of genetic variation in the genomes of plant populations, the potential for ancestral variation to facilitate...
Understanding Galaxies with Neural Networks
The process of galaxy formation and evolution involves a number of interesting and complicated physical processes. However, we have only limited information on the real galaxies we would like to compare to our theories: often just the shape, size, and amount of light given off in a few different color filters. This data set behaves...
Learning from big but finite data: From neural networks to linear dynamical systems
While the amount of data that we store and consume is consistently growing, the similar trend is visible in the scale of the modern machine learning (ML) algorithms. Fueled by big data, these algorithms use many parameters to capture the intricate latent structure in the data. Hence, data continues to fuel the success of machine...