AIMC Topic: Biological Evolution
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A Novel Approach Utilizing Domain Adversarial Neural Networks for the Detection and Classification of Selective Sweeps.
Advanced science (Weinheim, Baden-Wurttemberg, Germany)
Feb 2, 2024
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...
A neural network model for the evolution of learning in changing environments.
PLoS computational biology
Jan 30, 2024
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...
Evolutionary gravitational neocognitron neural network optimized with marine predators optimization algorithm for MRI brain tumor classification.
Electromagnetic biology and medicine
Jan 13, 2024
Magnetic resonance imaging (MRI) is a powerful tool for tumor diagnosis in human brain. Here, the MRI images are considered to detect the brain tumor and classify the regions as meningioma, glioma, pituitary and normal types. Numerous existing method...
Towards Personalised Mood Prediction and Explanation for Depression from Biophysical Data.
Sensors (Basel, Switzerland)
Dec 27, 2023
Digital health applications using Artificial Intelligence (AI) are a promising opportunity to address the widening gap between available resources and mental health needs globally. Increasingly, passively acquired data from wearables are augmented wi...
A deep learning method for drug-target affinity prediction based on sequence interaction information mining.
PeerJ
Dec 11, 2023
BACKGROUND: A critical aspect of drug discovery involves the prediction of drug-target affinity (DTA). Conducting wet lab experiments to determine affinity is both expensive and time-consuming, making it necessary to find alternative approaches. In ...
On convolutional neural networks for selection inference: Revealing the effect of preprocessing on model learning and the capacity to discover novel patterns.
PLoS computational biology
Nov 27, 2023
A central challenge in population genetics is the detection of genomic footprints of selection. As machine learning tools including convolutional neural networks (CNNs) have become more sophisticated and applied more broadly, these provide a logical ...
Uncovering developmental time and tempo using deep learning.
Nature methods
Nov 23, 2023
During animal development, embryos undergo complex morphological changes over time. Differences in developmental tempo between species are emerging as principal drivers of evolutionary novelty, but accurate description of these processes is very chal...
A novel method for identifying key genes in macroevolution based on deep learning with attention mechanism.
Scientific reports
Nov 13, 2023
Macroevolution can be regarded as the result of evolutionary changes of synergistically acting genes. Unfortunately, the importance of these genes in macroevolution is difficult to assess and hence the identification of macroevolutionary key genes is...
Automatic identification and morphological comparison of bivalve and brachiopod fossils based on deep learning.
PeerJ
Oct 11, 2023
Fossil identification is an essential and fundamental task for conducting palaeontological research. Because the manual identification of fossils requires extensive experience and is time-consuming, automatic identification methods are proposed. Howe...
EQNAS: Evolutionary Quantum Neural Architecture Search for Image Classification.
Neural networks : the official journal of the International Neural Network Society
Sep 29, 2023
Quantum neural network (QNN) is a neural network model based on the principles of quantum mechanics. The advantages of faster computing speed, higher memory capacity, smaller network size and elimination of catastrophic amnesia make it a new idea to ...
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