AIMC Topic: Species Specificity

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Prompt-CBP: A Novel Prompt Learning-Based Model for Predicting Cross-Species Promoters.

Journal of chemical information and modeling
Promoter sequences across species exhibit both specificity and conservation. The specificity of promoter sequences typically leads to lower cross-species prediction performance compared to within-species prediction. However, conserved promoter motifs...

Adaptive identity-regularized generative adversarial networks with species-specific loss functions for enhanced fish classification and segmentation through data augmentation.

Scientific reports
Traditional fish classification systems suffer from limited training data and imbalanced datasets, particularly for rare or morphologically complex species. This paper presents a novel Generative Adversarial Network architecture that integrates adapt...

EUP: Enhanced cross-species prediction of ubiquitination sites via a conditional variational autoencoder network based on ESM2.

PLoS computational biology
Ubiquitination is critical in biomedical research. Predicting ubiquitination sites based on deep learning model have advanced the study of ubiquitination. However, traditional supervised model limits in the scenarios where labels are scarcity across ...

CMImpute: cross-species and tissue imputation of species-level DNA methylation samples across mammalian species.

Genome biology
The large-scale application of the mammalian methylation array has substantially expanded the availability of DNA methylation data in mammalian species. However, this data captures only a small portion of species-tissue combinations. To address this,...

Using deep learning artificial intelligence for sex identification and taxonomy of sand fly species.

PloS one
Sandflies are vectors for several tropical diseases such as leishmaniasis, bartonellosis, and sandfly fever. Moreover, sandflies exhibit species-specificity in transmitting particular pathogen species, with females being responsible for disease trans...

Mouse-Geneformer: A deep learning model for mouse single-cell transcriptome and its cross-species utility.

PLoS genetics
Deep learning techniques are increasingly utilized to analyze large-scale single-cell RNA sequencing (scRNA-seq) data, offering valuable insights from complex transcriptome datasets. Geneformer, a pre-trained model using a Transformer Encoder archite...

Dorsoventral comparison of intraspecific variation in the butterfly wing pattern using a convolutional neural network.

Biology letters
Butterfly wing patterns exhibit notable differences between the dorsal and ventral surfaces, and morphological analyses of them have provided insights into the ecological and behavioural characteristics of wing patterns. Conventional methods for dors...

Contrastive machine learning reveals species -shared and -specific brain functional architecture.

Medical image analysis
A deep comparative analysis of brain functional connectome across species in primates has the potential to yield valuable insights for both scientific and clinical applications. However, the interspecies commonality and differences are inherently ent...

AI-based coral species discrimination: A case study of the Siderastrea Atlantic Complex.

PloS one
Species delimitation in hard corals remains controversial even after 250+ years of taxonomy. Confusing taxonomy in Scleractinia is not the result of sloppy work: clear boundaries are hard to draw because most diagnostic characters are quantitative an...

m5C-Seq: Machine learning-enhanced profiling of RNA 5-methylcytosine modifications.

Computers in biology and medicine
Epigenetic modifications, particularly RNA methylation and histone alterations, play a crucial role in heredity, development, and disease. Among these, RNA 5-methylcytosine (m5C) is the most prevalent RNA modification in mammalian cells, essential fo...