The Cure: Making a game of gene selection for breast cancer survival prediction

Motivation: Molecular signatures for predicting breast cancer prognosis could greatly improve care through personalization of treatment. Computational analyses of genome-wide expression datasets have identified such signatures, but these signatures leave much to be desired in terms of accuracy, reproducibility and biological interpretability. Methods that take advantage of structured prior knowledge (e.g. protein interaction networks) show promise in helping to define better signatures but most knowledge remains unstructured. Crowdsourcing via scientific discovery games is an emerging methodology that has the potential to tap into human intelligence at scales and in modes previously unheard of. Here, we developed and evaluated a game called The Cure on the task of gene selection for breast cancer survival prediction. Our central hypothesis was that knowledge linking expression patterns of specific genes to breast cancer outcomes could be captured from game players. We envisioned capturing knowledge both from the players prior experience and from their ability to interpret text related to candidate genes presented to them in the context of the game. Results: Between its launch in Sept. 2012 and Sept. 2013, The Cure attracted more than 1,000 registered players who collectively played nearly 10,000 games. Gene sets assembled through aggregation of the collected data clearly demonstrated the accumulation of relevant expert knowledge. In terms of predictive accuracy, these gene sets provided comparable performance to gene sets generated using other methods including those used in commercial tests. The Cure is available at http://genegames.org/cure

Photometric Redshifts of Quasars

We demonstrate that the design of the Sloan Digital Sky Survey (SDSS) filter system and the quality of the SDSS imaging data are sufficient for determining accurate and precise photometric redshifts (``photo-z''s) of quasars. Using a sample of 2625 quasars, we show that photo-z determination is even possible for z<=2.2 despite the lack of a strong continuum break that robust photo-z techniques normally require. We find that, using our empirical method on our sample of objects known to be quasars, approximately 70% of the photometric redshifts are correct to within delta z = 0.2; the fraction of correct photometric redshifts is even better for z>3. The accuracy of quasar photometric redshifts does not appear to be dependent upon magnitude to nearly 21st magnitude in i'. Careful calibration of the color-redshift relation to 21st magnitude may allow for the discovery of on the order of 10^6 quasars candidates in addition to the 10^5 quasars that the SDSS will confirm spectroscopically. We discuss the efficient selection of quasar candidates from imaging data for use with the photometric redshift technique and the potential scientific uses of a large sample of quasar candidates with photometric redshifts.Comment: 29 pages, 8 figures, submitted to A

Autonomous System-Level Fault Diagnosis in Satellites Using Housekeeping Telemetry

To continue the growing success of scientific discovery through deep space exploration, missions need to be able to be able to travel further and operate more efficiently than ever before. To ensure resilience in this capability, on-board autonomous fault mitigation methods must be developed and matured. To this end, we present a system for cross-subsystem fault diagnosis of satellites using spacecraft telemetry. Our system leverages a combination of Kalman Filters, Autoencoders, and Causality algorithms. We test our system for accuracy against three data sets of varying complexity levels, along with baseline testing. Additionally, we perform an ablation study to evaluate on-board tractability

Using Artificial Intelligence to aid Scientific Discovery of Climate Tipping Points

We propose a hybrid Artificial Intelligence (AI) climate modeling approach that enables climate modelers in scientific discovery using a climate-targeted simulation methodology based on a novel combination of deep neural networks and mathematical methods for modeling dynamical systems. The simulations are grounded by a neuro-symbolic language that both enables question answering of what is learned by the AI methods and provides a means of explainability. We describe how this methodology can be applied to the discovery of climate tipping points and, in particular, the collapse of the Atlantic Meridional Overturning Circulation (AMOC). We show how this methodology is able to predict AMOC collapse with a high degree of accuracy using a surrogate climate model for ocean interaction. We also show preliminary results of neuro-symbolic method performance when translating between natural language questions and symbolically learned representations. Our AI methodology shows promising early results, potentially enabling faster climate tipping point related research that would otherwise be computationally infeasible.Comment: This is the preprint of work presented at the 2022 AAAI Fall Symposium Series, Third Symposium on Knowledge-Guided ML, November 202

On the Completeness of Reflex Astrometry on Extrasolar Planets near the Sensitivity Limit

We provide a preliminary estimate of the performance of reflex astrometry on Earth-like planets in the habitable zones of nearby stars. In Monte Carlo experiments, we analyze large samples of astrometric data sets with low to moderate signal-to-noise ratios. We treat the idealized case of a single planet orbiting a single star, and assume there are no non-Keplerian complications or uncertainties. The real case can only be more difficult. We use periodograms for discovery and least-squares fits for estimating the Keplerian parameters. We find a completeness for detection compatible with estimates in the literature. We find mass estimation by least squares to be biased, as has been found for noisy radial-velocity data sets; this bias degrades the completeness of accurate mass estimation. When we compare the true planetary position with the position predicted from the fitted orbital parameters, at future times, we find low completeness for an accuracy goal of 0.3 times the semimajor axis of the planet, even with no delay following the end of astrometric observations. Our findings suggest that the recommendation of the ExoPlanet Task Force (Lunine et al. 2008) for "the capability to measure convincingly wobble semi-amplitudes down to 0.2 $\mu$as integrated over the mission lifetime," may not be satisfied by an instrument characterized by the noise floor of the Space Interferometry Mission, $\sigma_\mathrm{floor}\approx0.035\mu$as. An important, unsolved, strategic challenge for the exoplanetary science program is figuring out how to predict the future position of an Earth-like planet with accuracy sufficient to ensure the efficiency and success of the science operations for follow-on spectroscopy, which would search for biologically significant molecules in the atmosphere.Comment: v2: 16 pages, 4 figures; ApJ accepte