AIMC Topic: Genomics

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Artificial intelligence-driven integration of multi-omics and radiomics: A new hope for precision cancer diagnosis and prognosis.

Biochimica et biophysica acta. Molecular basis of disease
Despite advances in cancer diagnosis and treatment, the disease remains a major health challenge. Integrating multi-omics, radiomics, and artificial intelligence has improved detection, prognosis, and treatment monitoring. Molecular multi-omics provi...

Integrative Multi-Omics Analysis Reveals Molecular Subtypes of Ovarian Cancer and Constructs Prognostic Models.

Journal of immunotherapy (Hagerstown, Md. : 1997)
Ovarian cancer (OV) remains the most lethal gynecological malignancy. The aim of this study was to identify molecular subtypes of OV through integrative multi-omics analysis and construct machine learning-based prognostic models for predicting the ef...

DTreePred: an online viewer based on machine learning for pathogenicity prediction of genomic variants.

BMC bioinformatics
BACKGROUND: A significant challenge in precision medicine is confidently identifying mutations detected in sequencing processes that play roles in disease treatment or diagnosis. Furthermore, the lack of representativeness of single nucleotide varian...

MIDAA: deep archetypal analysis for interpretable multi-omic data integration based on biological principles.

Genome biology
High-throughput multi-omic molecular profiling allows the probing of biological systems at unprecedented resolution. However, integrating and interpreting high-dimensional, sparse, and noisy multimodal datasets remains challenging. Deriving new biolo...

Subtractive genomics approach: A guide to unveiling therapeutic targets across pathogens.

Journal of microbiological methods
Subtractive genomics is an adaptable bioinformatics technique that is used to identify potential therapeutic targets by differentiating essential genes in pathogens and non-pathogenic genes. Since, identification of therapeutic targets and understand...

Predicting genes associated with ossification of the posterior longitudinal ligament using graph attention network.

Methods (San Diego, Calif.)
Ossification of the posterior longitudinal ligament is a degenerative disease that severely impacts the spine, with a complex pathogenesis involving the interplay of multiple genes. This study utilizes a combination of graph neural networks and deep ...

How did we get there? AI applications to biological networks and sequences.

Computers in biology and medicine
The rapidly advancing field of artificial intelligence (AI) has transformed numerous scientific domains, including biology, where a vast and complex volume of data is available for analysis. This paper provides a comprehensive overview of the current...

Machine learning and multi-omics integration: advancing cardiovascular translational research and clinical practice.

Journal of translational medicine
The global burden of cardiovascular diseases continues to rise, making their prevention, diagnosis and treatment increasingly critical. With advancements and breakthroughs in omics technologies such as high-throughput sequencing, multi-omics approach...

Causal machine learning for single-cell genomics.

Nature genetics
Advances in single-cell '-omics' allow unprecedented insights into the transcriptional profiles of individual cells and, when combined with large-scale perturbation screens, enable measuring of the effect of targeted perturbations on the whole transc...

Biological Prior Knowledge-Embedded Deep Neural Network for Plant Genomic Prediction.

Genes
Genomic prediction is a powerful approach that predicts phenotypic traits from genotypic information, enabling the acceleration of trait improvement in plant breeding. Traditional genomic prediction methods have primarily relied on linear mixed mode...