AIMC Topic: Species Specificity
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SPASOS 1.1: a program for the inference of ancestral shape ontogenies.
Cladistics : the international journal of the Willi Hennig Society
May 28, 2021
We recently published a method to infer ancestral landmark-based shape ontogenies that takes into account the possible existence of changes in developmental timing. Here we describe SPASOS, a software to perform that analysis. SPASOS is an open-sourc...
Spine dynamics in the brain, mental disorders and artificial neural networks.
Nature reviews. Neuroscience
May 28, 2021
In the brain, most synapses are formed on minute protrusions known as dendritic spines. Unlike their artificial intelligence counterparts, spines are not merely tuneable memory elements: they also embody algorithms that implement the brain's ability ...
Models, Neurological
Memory, Short-Term
Reward
Synapses
Brain
Humans
Animals
Neural Networks, Computer
Neuronal Plasticity
Algorithms
Mental Disorders
Artificial Intelligence
Optogenetics
Machine Learning
Neurotransmitter Agents
Species Specificity
Mental Processes
Receptors, Dopamine
Dopamine
Dendritic Spines
Assessing the potential for deep learning and computer vision to identify bumble bee species from images.
Scientific reports
Apr 7, 2021
Pollinators are undergoing a global decline. Although vital to pollinator conservation and ecological research, species-level identification is expensive, time consuming, and requires specialized taxonomic training. However, deep learning and compute...
Deep6mA: A deep learning framework for exploring similar patterns in DNA N6-methyladenine sites across different species.
PLoS computational biology
Feb 18, 2021
N6-methyladenine (6mA) is an important DNA modification form associated with a wide range of biological processes. Identifying accurately 6mA sites on a genomic scale is crucial for under-standing of 6mA's biological functions. However, the existing ...
Cross-species Data Classification by Domain Adaptation via Discriminative Heterogeneous Maximum Mean Discrepancy.
IEEE/ACM transactions on computational biology and bioinformatics
Feb 3, 2021
Cross-species or Cross-platform data classification is a challenging problem in the field of bioinformatics, which aims to classify data samples in one species/platform by using labeled data samples in another species/platform. Traditional classifica...
Prediction of Total Drug Clearance in Humans Using Animal Data: Proposal of a Multimodal Learning Method Based on Deep Learning.
Journal of pharmaceutical sciences
Jan 23, 2021
Research into pharmacokinetics plays an important role in the development process of new drugs. Accurately predicting human pharmacokinetic parameters from preclinical data can increase the success rate of clinical trials. Since clearance (CL) which ...
iPTT(2 L)-CNN: A Two-Layer Predictor for Identifying Promoters and Their Types in Plant Genomes by Convolutional Neural Network.
Computational and mathematical methods in medicine
Jan 5, 2021
A promoter is a short DNA sequence near to the start codon, responsible for initiating transcription of a specific gene in genome. The accurate recognition of promoters has great significance for a better understanding of the transcriptional regulati...
A Computational Framework Based on Ensemble Deep Neural Networks for Essential Genes Identification.
International journal of molecular sciences
Nov 28, 2020
Essential genes contain key information of genomes that could be the key to a comprehensive understanding of life and evolution. Because of their importance, studies of essential genes have been considered a crucial problem in computational biology. ...
Poly(A)-DG: A deep-learning-based domain generalization method to identify cross-species Poly(A) signal without prior knowledge from target species.
PLoS computational biology
Nov 5, 2020
In eukaryotes, polyadenylation (poly(A)) is an essential process during mRNA maturation. Identifying the cis-determinants of poly(A) signal (PAS) on the DNA sequence is the key to understand the mechanism of translation regulation and mRNA metabolism...
Performance vs. competence in human-machine comparisons.
Proceedings of the National Academy of Sciences of the United States of America
Oct 13, 2020
Does the human mind resemble the machines that can behave like it? Biologically inspired machine-learning systems approach "human-level" accuracy in an astounding variety of domains, and even predict human brain activity-raising the exciting possibil...
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