AIMC Topic: Computational Biology

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EDeepSSP: Explainable deep neural networks for exact splice sites prediction.

Journal of bioinformatics and computational biology
Splice site prediction is crucial for understanding underlying gene regulation, gene function for better genome annotation. Many computational methods exist for recognizing the splice sites. Although most of the methods achieve a competent performanc...

DNC4mC-Deep: Identification and Analysis of DNA N4-Methylcytosine Sites Based on Different Encoding Schemes By Using Deep Learning.

Cells
N4-methylcytosine as one kind of modification of DNA has a critical role which alters genetic performance such as protein interactions, conformation, stability in DNA as well as the regulation of gene expression same cell developmental and genomic im...

Capsule networks as recurrent models of grouping and segmentation.

PLoS computational biology
Classically, visual processing is described as a cascade of local feedforward computations. Feedforward Convolutional Neural Networks (ffCNNs) have shown how powerful such models can be. However, using visual crowding as a well-controlled challenge, ...

Cell type prioritization in single-cell data.

Nature biotechnology
We present Augur, a method to prioritize the cell types most responsive to biological perturbations in single-cell data. Augur employs a machine-learning framework to quantify the separability of perturbed and unperturbed cells within a high-dimensio...

LogicNet: probabilistic continuous logics in reconstructing gene regulatory networks.

BMC bioinformatics
BACKGROUND: Gene Regulatory Networks (GRNs) have been previously studied by using Boolean/multi-state logics. While the gene expression values are usually scaled into the range [0, 1], these GRN inference methods apply a threshold to discretize the d...

Cross-species regulatory sequence activity prediction.

PLoS computational biology
Machine learning algorithms trained to predict the regulatory activity of nucleic acid sequences have revealed principles of gene regulation and guided genetic variation analysis. While the human genome has been extensively annotated and studied, mod...

A novel riboswitch classification based on imbalanced sequences achieved by machine learning.

PLoS computational biology
Riboswitch, a part of regulatory mRNA (50-250nt in length), has two main classes: aptamer and expression platform. One of the main challenges raised during the classification of riboswitch is imbalanced data. That is a circumstance in which the recor...

α-Satellite: An AI-Driven System and Benchmark Datasets for Dynamic COVID-19 Risk Assessment in the United States.

IEEE journal of biomedical and health informatics
The fast evolving and deadly outbreak of coronavirus disease (COVID-19) has posed grand challenges to human society. To slow the spread of virus infections and better respond for community mitigation, by advancing capabilities of artificial intellige...

Large-scale DNA-based phenotypic recording and deep learning enable highly accurate sequence-function mapping.

Nature communications
Predicting effects of gene regulatory elements (GREs) is a longstanding challenge in biology. Machine learning may address this, but requires large datasets linking GREs to their quantitative function. However, experimental methods to generate such d...