12 papers · ranked by Valyu relevance
Purushottam D. Dixit
In modern biological physics, there is a great interest in building generative probabilistic models for ensembles of covarying binary variables. A popular approach is to use the maximum entropy principle. Here, one builds generative models that use as constraints lower level statistics estimated from the data. While…
Stefan Häusler
Deciphering the functional organization of large biological networks is a major challenge for current mathematical methods. A common approach is to decompose networks into largely independent functional modules, but inferring these modules and their organization from network activity is difficult, given the…
Lukas Gabor, Jeremy Cohen, Walter Jetz
Species distribution models (SDMs) are an important tool for predicting species occurrences in geographic space and for understanding the drivers of these occurrences. An effect of environmental variable selection on SDM outcomes has been noted, but how the treatment of variables influences models, including model…
Theo Knijnenburg, Gunnar Klau, Francesco Iorio, Mathew Garnett + 3 more
Mining large datasets using machine learning approaches often leads to models that are hard to interpret and not amenable to the generation of hypotheses that can be experimentally tested. Finding ‘actionable knowledge’ is becoming more important, but also more challenging as datasets grow in size and complexity. We…
Ajay Subbaroyan, Olivier C. Martin, Areejit Samal
The properties of random Boolean networks as models of gene regulation have been investigated extensively by the statistical physics community. In the past two decades, there has been a dramatic increase in the reconstruction and analysis of Boolean models of biological networks. In such models, neither network…
Suchetana Mitra, Priyotosh Sil, Ajay Subbaroyan, Olivier C. Martin + 1 more
Boolean networks (BNs) have been extensively used to model the dynamics of gene regulatory networks (GRNs) that underlie cellular decisions. The dynamics of BNs depend on the network architecture and regulatory logic rules (or Boolean functions (BFs)) associated with nodes, both of which have been shown to be far from…
Chaitanya K. Ryali, Gautam Reddy, Angela J. Yu
Understanding how humans and animals learn about statistical regularities in stable and volatile environments, and utilize these regularities to make predictions and decisions, is an important problem in neuroscience and psychology. Using a Bayesian modeling framework, specifically the Dynamic Belief Model (DBM), it…
Guilherme C.P. Innocentini, Sarah Guiziou, Jerome Bonnet, Ovidiu Radulescu
We propose an analytic solution for the stochastic dynamics of a binary biological switch, defined as a DNA unit with two mutually exclusive configurations, each one triggering the expression of a different gene. Such a device could be used as a memory unit for biological computing systems designed to operate in noisy…
Timothy J. Rudge, Gonzalo Vidal
Bio-computation is the implementation of computational operations using biological substrates, such as cells engineered with synthetic genetic circuits. These genetic circuits can be composed of DNA parts with specific functions, such as promoters that initiate transcription, ribosome binding sites that initiate…
Emre Dil, Andrew Rutenberg
We predictively model damage transition probabilities for binary health outputs of 19 diseases and 25 activities of daily living states (ADLs) between successive waves of the English Longitudinal Study of Aging (ELSA). Model selection between deep neural networks (DNN), random forests, and logistic regression found…
Zeinab Mohammadi, Zoe C. Ashwood, Jonathan W. Pillow
Recent work has revealed that mice do not rely on a stable strategy during perceptual decision-making, but switch between multiple strategies within a single session [1, 2]. However, this switching behavior has not yet been characterized in non-stationary environments, and the factors that govern switching remain…
Arno Onken, Jue Xie, Stefano Panzeri, Camillo Padoa-Schioppa
A fundamental and recurrent question in systems neuroscience is that of assessing what variables are encoded by a given population of neurons. Such assessments are often challenging because neurons in one brain area may encode multiple variables, and because neuronal representations might be categorical (different…