AIMC Topic: Genomics

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From cancer big data to treatment: Artificial intelligence in cancer research.

The journal of gene medicine
In recent years, developing the idea of "cancer big data" has emerged as a result of the significant expansion of various fields such as clinical research, genomics, proteomics and public health records. Advances in omics technologies are making a si...

Computational methods in glaucoma research: Current status and future outlook.

Molecular aspects of medicine
Advancements in computational techniques have transformed glaucoma research, providing a deeper understanding of genetics, disease mechanisms, and potential therapeutic targets. Systems genetics integrates genomic and clinical data, aiding in identif...

Deep Learning for Genomics: From Early Neural Nets to Modern Large Language Models.

International journal of molecular sciences
The data explosion driven by advancements in genomic research, such as high-throughput sequencing techniques, is constantly challenging conventional methods used in genomics. In parallel with the urgent demand for robust algorithms, deep learning has...

Unsupervised learning for medical data: A review of probabilistic factorization methods.

Statistics in medicine
We review popular unsupervised learning methods for the analysis of high-dimensional data encountered in, for example, genomics, medical imaging, cohort studies, and biobanks. We show that four commonly used methods, principal component analysis, K-m...

Emerging trends in post-translational modification: Shedding light on Glioblastoma multiforme.

Biochimica et biophysica acta. Reviews on cancer
Recent multi-omics studies, including proteomics, transcriptomics, genomics, and metabolomics have revealed the critical role of post-translational modifications (PTMs) in the progression and pathogenesis of Glioblastoma multiforme (GBM). Further, PT...

Batch normalization followed by merging is powerful for phenotype prediction integrating multiple heterogeneous studies.

PLoS computational biology
Heterogeneity in different genomic studies compromises the performance of machine learning models in cross-study phenotype predictions. Overcoming heterogeneity when incorporating different studies in terms of phenotype prediction is a challenging an...

disperseNN2: a neural network for estimating dispersal distance from georeferenced polymorphism data.

BMC bioinformatics
Spatial genetic variation is shaped in part by an organism's dispersal ability. We present a deep learning tool, disperseNN2, for estimating the mean per-generation dispersal distance from georeferenced polymorphism data. Our neural network performs ...

Genomics refined: AI-powered perspectives on structural analysis.

Trends in plant science
Understanding protein function by deciphering 3D structure has distinct limitations. A recent study by Huang et al. used AlphaFold2, an artificial intelligence (AI) protein-folding prediction model, to predict and classify deaminase proteins based on...

Machine Learning and Omics Analysis in Aortic Aneurysm.

Angiology
Aortic aneurysm is a life-threatening condition and mechanisms underlying its formation and progression are still incompletely understood. Omics approach has brought new insights to identify a broad spectrum of biomarkers and better understand cellul...

Deciphering RNA splicing logic with interpretable machine learning.

Proceedings of the National Academy of Sciences of the United States of America
Machine learning methods, particularly neural networks trained on large datasets, are transforming how scientists approach scientific discovery and experimental design. However, current state-of-the-art neural networks are limited by their uninterpre...