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Projects
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HARVIST
Heterogeneous Agricultural Research Via Interactive, Scalable Technology
Collaboration with Stephan Sain of CU Denver. We are developing and demonstrating a machine learning analysis toolkit that uses Support Vector Machines, clustering, and multivariate spatial models to identify the connections between weather and agriculture (e.g., crop yield). We will integrate data from orbiting satellites, weather stations on the ground, land cover types, soil properties, and historical crop yield archives. Funded by a two-year grant from NASA's Earth Science Technology Office.
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HSA19
Bioinformatics Support for the Functional Annotation of Human chromosome 19
The MLS Group is developing a variety of bioinformatics tools to
support Caltech and LLNL biologists' efforts to produce a complete
functional annotation genes and regulatory sequences in the unusually
gene-dense human chromosome HSA19. The tools include automated image
analysis software that enables the high-throughput interpretation of
tissue arrays, and systems for integrated analysis of diverse sequence
annotation and transcript expression data.
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MISR Automated Cloud Classification
The Multi-angle Imaging SpectroRadiometer (MISR) instrument captures
images of the earth at moderate resolution (275 m or 1.1 km) from
nine different angles, ranging from straight down to 70 degrees in
either direction. By comparing images of the same area of the earth
from different angles, scientists are able to identify thin clouds and
determine approximate cloud heights with unprecedented accuracy, leading
to greater understanding of the planet's global distribution of clouds,
and how that affects the global climate. Automating the process of
detecting clouds and distinguishing between different types of clouds
and aerosols remains a challenge, and we are applying machine learning
technology to this problem to complement the Physics-based algorithms
currently being used by scientists.
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Cellerator is a Mathematica® package designed to facilitate biological
modeling via automated equation generation. Cellerator was designed
with the intent of simulating at least the following essential
biological processes:
- signal transduction networks (STNs);
- cells that are represented by interacting signal transduction networks; and
- multi-cellular tissues that are represented by
interacting networks of cells that may themselves contain internal STNs.
These processes combine to form an obvious hierarchy that can be
further subdivided for notational simplicity (e.g., STNs as elements
of STNs, and so forth). In the past it has been necessary to manually
translate chemical networks from cartoon-diagrams to chemical
equations and thence to ordinary differential equations. This process
is tedious and highly error prone, and impractical for all but the
simplest of systems because of the combinatoric increase in the number
of equations with the number of chemical species. Click here for an
example of a simple cascade (3-stage MAPK in solution). Cellerator
provides a framework for generating, translating, and numerically
solving a potentially unlimited number of biochemical interactions.
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Onboard Autonomous Science Investigation System
Rover traverse distances are increasing at a faster rate than downlink
capacity is increasing. As this trend continues, the quantity of data that
can be returned to Earth per meter traversed is reduced. The capacity of
the rover to collect data, however, remains high. This circumstance leads
to an opportunity to increase mission science return by carefully selecting
the data with the highest science interest for downlink. We have developed
an onboard science analysis technology for increasing science return from
missions. Our technology evaluates the geologic data gather by the
rover. This analysis is used to prioritize the data for transmission, so
that the data with the highest science value is transmitted to Earth. In
addition, the onboard analysis results are used to identify science
opportunities. A planning and scheduling component of the system enables
the rover to take advantage of the identified science opportunity.
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