AIMC Topic: Genomics

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Unveiling Long Non-coding RNA Networks from Single-Cell Omics Data Through Artificial Intelligence.

Methods in molecular biology (Clifton, N.J.)
Single-cell omics technologies have revolutionized the study of long non-coding RNAs (lncRNAs), offering unprecedented resolution in elucidating their expression dynamics, cell-type specificity, and associated gene regulatory networks (GRNs). Concurr...

Enhancing Privacy-Preserving Cancer Classification with Convolutional Neural Networks.

Pacific Symposium on Biocomputing. Pacific Symposium on Biocomputing
Precision medicine significantly enhances patients prognosis, offering personalized treatments. Particularly for metastatic cancer, incorporating primary tumor location into the diagnostic process greatly improves survival rates. However, traditional...

Machine and Deep Learning Methods for Predicting 3D Genome Organization.

Methods in molecular biology (Clifton, N.J.)
Three-dimensional (3D) chromatin interactions, such as enhancer-promoter interactions (EPIs), loops, topologically associating domains (TADs), and A/B compartments, play critical roles in a wide range of cellular processes by regulating gene expressi...

Deep learning insights into distinct patterns of polygenic adaptation across human populations.

Nucleic acids research
Response to spatiotemporal variation in selection gradients resulted in signatures of polygenic adaptation in human genomes. We introduce RAISING, a two-stage deep learning framework that optimizes neural network architecture through hyperparameter t...

Artificial Intelligence-Driven Precision Medicine: Multi-Omics and Spatial Multi-Omics Approaches in Diffuse Large B-Cell Lymphoma (DLBCL).

Frontiers in bioscience (Landmark edition)
In this comprehensive review, we delve into the transformative role of artificial intelligence (AI) in refining the application of multi-omics and spatial multi-omics within the realm of diffuse large B-cell lymphoma (DLBCL) research. We scrutinized ...

Towards the genomic sequence code of DNA fragility for machine learning.

Nucleic acids research
Genomic DNA breakages and the subsequent insertion and deletion mutations are important contributors to genome instability and linked diseases. Unlike the research in point mutations, the relationship between DNA sequence context and the propensity f...

Inferring tumor purity using multi-omics data based on a uniform machine learning framework MoTP.

Briefings in bioinformatics
Existing algorithms for assessing tumor purity are limited to a single omics data, such as gene expression, somatic copy number variations, somatic mutations, and DNA methylation. Here we proposed the machine learning Multi-omics Tumor Purity predict...

Multi-view multi-level contrastive graph convolutional network for cancer subtyping on multi-omics data.

Briefings in bioinformatics
Cancer is a highly diverse group of diseases, and each type of cancer can be further divided into various subtypes according to specific characteristics, cellular origins, and molecular markers. Subtyping helps in tailoring treatment and prognosis ac...

A review of deep learning models for the prediction of chromatin interactions with DNA and epigenomic profiles.

Briefings in bioinformatics
Advances in three-dimensional (3D) genomics have revealed the spatial characteristics of chromatin interactions in gene expression regulation, which is crucial for understanding molecular mechanisms in biological processes. High-throughput technologi...

AutoXAI4Omics: an automated explainable AI tool for omics and tabular data.

Briefings in bioinformatics
Machine learning (ML) methods offer opportunities for gaining insights into the intricate workings of complex biological systems, and their applications are increasingly prominent in the analysis of omics data to facilitate tasks, such as the identif...