We consider the ancestral state reconstruction problem where we need to infer phenotypes of ancestors using observations from present-day species. For this problem, we propose a multi-task learning method that uses regularized maximum likelihood to e...
Many biological organisms (e.g. insects, birds, and mammals) rely on the perception of an informational variable called time-to-contact (TTC) to control their motion for various tasks such as avoiding obstacles, landing, or interception. TTC, defined...
Despite rapid advances in sequencing technologies, accurately calling genetic variants present in an individual genome from billions of short, errorful sequence reads remains challenging. Here we show that a deep convolutional neural network can call...
Neurons in sensory cortex are tuned to diverse features in natural scenes. But what determines which features neurons become selective to? Here we explore the idea that neuronal selectivity is optimized to represent features in the recent sensory pas...
In order to accelerate computing the convex hull on a set of n points, a heuristic procedure is often applied to reduce the number of points to a set of s points, s ≤ n, which also contains the same hull. We present an algorithm to precondition 2D da...
Methods in molecular biology (Clifton, N.J.)
Jan 1, 2024
Mathematical modeling plays a vital role in mammalian synthetic biology by providing a framework to design and optimize design circuits and engineered bioprocesses, predict their behavior, and guide experimental design. Here, we review recent models ...
Intravital microscopy in small animals growingly contributes to the visualization of short- and long-term mammalian biological processes. Miniaturized fluorescence microscopy has revolutionized the observation of live animals' neural circuits. The te...
Toxicological sciences : an official journal of the Society of Toxicology
May 12, 2023
We present a mechanistic machine-learning quantitative structure-activity relationship (QSAR) model to predict mammalian acute oral toxicity. We trained our model using a rat acute toxicity database compiled by the US National Toxicology Program. We ...
MOTIVATION: Large-scale neuronal morphologies are essential to neuronal typing, connectivity characterization and brain modeling. It is widely accepted that automation is critical to the production of neuronal morphology. Despite previous survey pape...
We present a new algorithm to efficiently simulate random models of large neural networks satisfying the property of time asynchrony. The model parameters (average firing rate, number of neurons, synaptic connection probability, and postsynaptic dura...