Ask2Me VarHarmonizer: A Python-Based Tool to Harmonize Variants from Cancer Genetic Testing Reports and Map them to the ClinVar Database. (arXiv:1911.08408v1 [q-bio.QM])

PURPOSE: The popularity of germline genetic panel testing has led to a vast accumulation of variant-level data. Variant names are not always consistent across laboratories and not easily mappable to public variant databases such as ClinVar. A tool that can automate the process of variants harmonization and mapping is needed to help clinicians ensure their…

Neural networks with motivation. (arXiv:1906.09528v2 [q-bio.NC] UPDATED)

How can animals behave effectively in conditions involving different motivational contexts? Here, we propose how reinforcement learning neural networks can learn optimal behavior for dynamically changing motivational salience vectors. First, we show that Q-learning neural networks with motivation can navigate in environment with dynamic rewards. Second, we show that such networks can learn complex behaviors…

Boosting heritability: estimating the genetic component of phenotypic variation with multiple sample splitting. (arXiv:1910.11743v2 [stat.AP] UPDATED)

Heritability is a central measure in genetics quantifying how much of the variability observed in a trait is attributable to genetic differences. Existing methods for estimating heritability are most often based on random-effect models, typically for computational reasons. The alternative of using a fixed-effect model has received much more limited attention in the literature. In…

Brain reaction times: Linking individual and collective behaviour through Physics modelling. (arXiv:1910.12725v2 [physics.bio-ph] UPDATED)

An individual’s reaction time data to visual stimuli have usually been represented in Experimental Psychology by means of an ex-Gaussian function (EGF). In most previous works, researchers have mainly aimed at finding a meaning for the parameters of the EGF function in relation to psychological phenomena. We will focus on interpreting the reaction times (RTs)…

Fragmentation and inefficiencies in US equity markets: Evidence from the Dow 30. (arXiv:1902.04690v4 [q-fin.TR] UPDATED)

Using the most comprehensive source of commercially available data on the US National Market System, we analyze all quotes and trades associated with Dow 30 stocks in 2016 from the vantage point of a single and fixed frame of reference. We find that inefficiencies created in part by the fragmentation of the equity marketplace are…

Merton’s portfolio problem under Volterra Heston model. (arXiv:1905.05371v2 [q-fin.PM] UPDATED)

This paper investigates Merton’s portfolio problem in a rough stochastic environment described by Volterra Heston model. The model has a non-Markovian and non-semimartingale structure. By considering an auxiliary random process, we solve the portfolio optimization problem with the martingale optimality principle. Optimal strategies for power and exponential utilities are derived in semi-closed form solutions depending…

Transfer Learning of fMRI Dynamics. (arXiv:1911.06813v1 [eess.IV])

As a mental disorder progresses, it may affect brain structure, but brain function expressed in brain dynamics is affected much earlier. Capturing the moment when brain dynamics express the disorder is crucial for early diagnosis. The traditional approach to this problem via training classifiers either proceeds from handcrafted features or requires large datasets to combat…

X-ray Multimodal Intrinsic-Speckle-Tracking. (arXiv:1911.06814v1 [eess.IV])

We develop X-ray Multimodal Intrinsic-Speckle-Tracking (MIST), a form of X-ray speckle-tracking that is able to recover both the refractive index decrement and the small-angle X-ray scattering (SAXS) signal of a phase object. MIST is based on combining a Fokker-Planck description of paraxial X-ray optics, with an optical-flow formalism for X-ray speckle-tracking. Only two images need…

Precise Spatial Memory in Local Random Networks. (arXiv:1911.06921v1 [q-bio.NC])

Self-sustained, elevated neuronal activity persisting on time scales of ten seconds or longer is thought to be vital for aspects of working memory, including brain representations of real space. Continuous-attractor neural networks, one of the most well-known modeling frameworks for persistent activity, have been able to model crucial aspects of such spatial memory. These models…