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Genomics

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Identifying Depression Through Machine Learning Analysis of Omics Data: Scoping Review.

JMIR nursing
BACKGROUND: Depression is one of the most common mental disorders that affects >300 million people worldwide. There is a shortage of providers trained in the provision of mental health care, and the nursing workforce is essential in filling this gap....

INDELpred: Improving the prediction and interpretation of indel pathogenicity within the clinical genome.

HGG advances
Small insertions and deletions (indels) are critical yet challenging genetic variations with significant clinical implications. However, the identification of pathogenic indels from neutral variants in clinical contexts remains an understudied proble...

Multi-omics based artificial intelligence for cancer research.

Advances in cancer research
With significant advancements of next generation sequencing technologies, large amounts of multi-omics data, including genomics, epigenomics, transcriptomics, proteomics, and metabolomics, have been accumulated, offering an unprecedented opportunity ...

Tracing the genealogy origin of geographic populations based on genomic variation and deep learning.

Molecular phylogenetics and evolution
Assigning a query individual animal or plant to its derived population is a prime task in diverse applications related to organismal genealogy. Such endeavors have conventionally relied on short DNA sequences under a phylogenetic framework. These met...

Machine learning for the advancement of genome-scale metabolic modeling.

Biotechnology advances
Constraint-based modeling (CBM) has evolved as the core systems biology tool to map the interrelations between genotype, phenotype, and external environment. The recent advancement of high-throughput experimental approaches and multi-omics strategies...

Integrated multi-omics analysis and machine learning to refine molecular subtypes, prognosis, and immunotherapy in lung adenocarcinoma.

Functional & integrative genomics
Lung adenocarcinoma (LUAD) has a malignant characteristic that is highly aggressive and prone to metastasis. There is still a lack of suitable biomarkers to facilitate the refinement of precision-based therapeutic regimens. We used a combination of 1...

Machine learning for genomic and pedigree prediction in sugarcane.

The plant genome
Sugarcane (Saccharum spp.) plays a crucial role in global sugar production; however, the efficiency of breeding programs has been hindered by its heterozygous polyploid genomes. Considering non-additive genetic effects is essential in genome predicti...

Integrated Assays of Genome-Wide Association Study, Multi-Omics Co-Localization, and Machine Learning Associated Calcium Signaling Genes with Oilseed Rape Resistance to .

International journal of molecular sciences
(Ss) is one of the most devastating fungal pathogens, causing huge yield loss in multiple economically important crops including oilseed rape. Plant resistance to Ss pertains to quantitative disease resistance (QDR) controlled by multiple minor gene...

Integrating genomics, phenomics, and deep learning improves the predictive ability for Fusarium head blight-related traits in winter wheat.

The plant genome
Fusarium head blight (FHB) remains one of the most destructive diseases of wheat (Triticum aestivum L.), causing considerable losses in yield and end-use quality. Phenotyping of FHB resistance traits, Fusarium-damaged kernels (FDK), and deoxynivaleno...

Species delimitation 4.0: integrative taxonomy meets artificial intelligence.

Trends in ecology & evolution
Although species are central units for biological research, recent findings in genomics are raising awareness that what we call species can be ill-founded entities due to solely morphology-based, regional species descriptions. This particularly appli...