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Biological Evolution

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A neural network model for the evolution of learning in changing environments.

PLoS computational biology
Learning from past experience is an important adaptation and theoretical models may help to understand its evolution. Many of the existing models study simple phenotypes and do not consider the mechanisms underlying learning while the more complex ne...

AI-based discovery of habitats from museum collections.

Trends in ecology & evolution
Museum collection records are a source of historic data for species occurrence, but little attention is paid to the associated descriptions of habitat at the sample locations. We propose that artificial intelligence methods have potential to use thes...

A Novel Approach Utilizing Domain Adversarial Neural Networks for the Detection and Classification of Selective Sweeps.

Advanced science (Weinheim, Baden-Wurttemberg, Germany)
The identification and classification of selective sweeps are of great significance for improving the understanding of biological evolution and exploring opportunities for precision medicine and genetic improvement. Here, a domain adaptation sweep de...

Enhancing cryo-EM structure prediction with DeepTracer and AlphaFold2 integration.

Briefings in bioinformatics
Understanding the protein structures is invaluable in various biomedical applications, such as vaccine development. Protein structure model building from experimental electron density maps is a time-consuming and labor-intensive task. To address the ...

PSE-Net: Channel pruning for Convolutional Neural Networks with parallel-subnets estimator.

Neural networks : the official journal of the International Neural Network Society
Channel Pruning is one of the most widespread techniques used to compress deep neural networks while maintaining their performances. Currently, a typical pruning algorithm leverages neural architecture search to directly find networks with a configur...

Self-replicating artificial neural networks give rise to universal evolutionary dynamics.

PLoS computational biology
In evolutionary models, mutations are exogenously introduced by the modeler, rather than endogenously introduced by the replicator itself. We present a new deep-learning based computational model, the self-replicating artificial neural network (SeRAN...

Artificial neural networks reconstruct missing perikymata in worn teeth.

Anatomical record (Hoboken, N.J. : 2007)
Dental evolutionary studies in hominins are key to understanding how our ancestors and close fossil relatives grew from the early stages of embryogenesis into adults. In a sense, teeth are like an airplane's 'black box' as they record important varia...

Comparing cognition across major transitions using the hierarchy of formal automata.

Wiley interdisciplinary reviews. Cognitive science
The evolution of cognition can be understood in terms of a few major transitions-changes in the computational architecture of nervous systems that changed what cognitive capacities could be evolved by downstream lineages. We demonstrate how the idea ...

DeepDive: estimating global biodiversity patterns through time using deep learning.

Nature communications
Understanding how biodiversity has changed through time is a central goal of evolutionary biology. However, estimates of past biodiversity are challenged by the inherent incompleteness of the fossil record, even when state-of-the-art statistical meth...

Biological computation through recurrence.

Biochemical and biophysical research communications
One of the defining features of living systems is their adaptability to changing environmental conditions. This requires organisms to extract temporal and spatial features of their environment, and use that information to compute the appropriate resp...