AIMC Topic: Genomics

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Advancing optical nanosensors with artificial intelligence: A powerful tool to identify disease-specific biomarkers in multi-omics profiling.

Talanta
Multi-omics profiling integrates genomic, epigenomic, transcriptomic, and proteomic data, essential for understanding complex health and disease pathways. This review highlights the transformative potential of combining optical nanosensors with artif...

Meet the author: Hae Kyung Im.

Cell genomics
Hae Kyung Im's research group focuses on quantitative computational and statistical methods to tackle genomic data analysis and provides methods to translate the vast amount of genomic data for health research. In collaboration with Mengjie Chen's gr...

Perceptual and technical barriers in sharing and formatting metadata accompanying omics studies.

Cell genomics
Metadata, or "data about data," is essential for organizing, understanding, and managing large-scale omics datasets. It enhances data discovery, integration, and interpretation, enabling reproducibility, reusability, and secondary analysis. However, ...

Effective integration of multi-omics with prior knowledge to identify biomarkers via explainable graph neural networks.

NPJ systems biology and applications
The rapid growth of multi-omics datasets and the wealth of biological knowledge necessitates the development of effective methods for their integration. Such methods are essential for building predictive models and identifying drug targets based on a...

Optimizing Treatment: The Role of Pharmacology, Genomics, and AI in Improving Patient Outcomes.

Drug development research
Recent advances in pharmacology are revolutionizing drug discovery and treatment strategies through personalized medicine, pharmacogenomics, and artificial intelligence (AI). The objective of the present study is to review the role of personalized me...

Omics data classification using constitutive artificial neural network optimized with single candidate optimizer.

Network (Bristol, England)
Recent technical advancements enable omics-based biological study of molecules with very high throughput and low cost, such as genomic, proteomic, and microbionics'. To overcome this drawback, Omics Data Classification using Constitutive Artificial N...

Deciphering genomic codes using advanced natural language processing techniques: a scoping review.

Journal of the American Medical Informatics Association : JAMIA
OBJECTIVES: The vast and complex nature of human genomic sequencing data presents challenges for effective analysis. This review aims to investigate the application of natural language processing (NLP) techniques, particularly large language models (...

WheatGP, a genomic prediction method based on CNN and LSTM.

Briefings in bioinformatics
Wheat plays a crucial role in ensuring food security. However, its complex genetic structure and trait variation pose significant challenges for breeding superior varieties. In this study, a genomic prediction method for wheat (WheatGP) is proposed. ...

Federated transfer learning with differential privacy for multi-omics survival analysis.

Briefings in bioinformatics
Multi-omics data often suffer from the "big $p$, small $n$" problem where the dimensionality of features is significantly larger than the sample size, making the integration of multi-omics data for survival analysis of a specific cancer particularly ...

DOMSCNet: a deep learning model for the classification of stomach cancer using multi-layer omics data.

Briefings in bioinformatics
The rapid advancement of next-generation sequencing (NGS) technology and the expanding availability of NGS datasets have led to a significant surge in biomedical research. To better understand the molecular processes, underlying cancer and to support...