AIMC Topic: Phenotype

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Bridging scales in cancer progression: mapping genotype to phenotype using neural networks.

Seminars in cancer biology May 12, 2014
In this review we summarise our recent efforts in trying to understand the role of heterogeneity in cancer progression by using neural networks to characterise different aspects of the mapping from a cancer cells genotype and environment to its pheno...
Evolution, Molecular Humans Disease Progression Neural Networks, Computer Genotype Phenotype Models, Theoretical Neoplasms
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Expanding biobank pharmacogenomics through machine learning calls of structural variation.

Genetics Jul 9, 2025
Biobanks linking genetic data with clinical health records provide exciting opportunities for pharmacogenomic (PGx) research on genetic variation and drug response. Designed as central and multiuse resources, biobanks can facilitate diverse PGx resea...
Genotype Phenotype Humans Alleles Cytochrome P-450 CYP2D6 Machine Learning Pharmacogenetics Biological Specimen Banks Polymorphism, Single Nucleotide
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Label-free single-cell phenotyping to determine tumor cell heterogeneity in pancreatic cancer in real time.

JCI insight Jul 8, 2025
Resistance to chemotherapy of pancreatic ductal adenocarcinoma (PDAC) is largely driven by intratumoral heterogeneity (ITH) due to tumor cell plasticity and clonal diversity. To develop alternative strategies to overcome this defined mechanism of res...
Carcinoma, Pancreatic Ductal Holography Pancreatic Neoplasms Cell Differentiation Epithelial-Mesenchymal Transition Machine Learning Phenotype Animals Cell Line, Tumor Single-Cell Analysis Mice Humans
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Phenotype-driven risk stratification of cerebral aneurysms using Shapley Additive Explanations-based supervised clustering: a novel approach to rupture prediction.

Neurosurgical focus Jul 1, 2025
OBJECTIVE: The aim of this study was to address the limitations of traditional aneurysm risk scoring systems and computational fluid dynamics (CFD) analyses by applying a supervised clustering framework to identify distinct aneurysm phenotypes and im...
Risk Factors Aged Adult Machine Learning Phenotype Female Cluster Analysis Aneurysm, Ruptured Intracranial Aneurysm Male Humans Middle Aged Risk Assessment
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Revisiting Abnormalities of Ventricular Depolarization: Redefining Phenotypes and Associated Outcomes Using Tree-Based Dimensionality Reduction.

Journal of the American Heart Association Jul 1, 2025
BACKGROUND: Abnormal ventricular depolarization, evident as a broad QRS complex on an ECG, is traditionally categorized into left bundle-branch block (LBBB) and right bundle-branch block or nonspecific intraventricular conduction delay. This categori...
Bundle-Branch Block Dimensionality Reduction Unsupervised Machine Learning Heart Ventricles Female Humans Middle Aged Aged Phenotype Electrocardiography Male Action Potentials
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Artificial intelligence approaches for tumor phenotype stratification from single-cell transcriptomic data.

eLife Jun 13, 2025
Single-cell RNA-sequencing (scRNA-seq) coupled with robust computational analysis facilitates the characterization of phenotypic heterogeneity within tumors. Current scRNA-seq analysis pipelines are capable of identifying a myriad of malignant and no...
Humans Artificial Intelligence Phenotype Male Neoplasms Gene Expression Profiling Transcriptome Sequence Analysis, RNA Single-Cell Analysis Computational Biology
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f-statistics-based ancestry profiling and convolutional neural network phenotyping shed new light on the structure of genetic and spike shape diversity in Aegilops tauschii Coss.

Proceedings of the Japan Academy. Series B, Physical and biological sciences Jun 11, 2025
Aegilops tauschii Coss., a progenitor of bread wheat, is an important wild genetic resource for breeding. The species comprises three genetically defined lineages (TauL1, TauL2, and TauL3), each displaying valuable phenotypes in agronomic traits, inc...
Convolutional Neural Networks Genetic Variation Aegilops Neural Networks, Computer Phenotype
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Predicting bacterial phenotypic traits through improved machine learning using high-quality, curated datasets.

Communications biology Jun 7, 2025
Predicting prokaryotic phenotypes-observable traits that govern functionality, adaptability, and interactions-holds significant potential for fields such as biotechnology, environmental sciences, and evolutionary biology. In this study, we leverage m...
Phenotype Machine Learning Bacteria Software Computational Biology
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Environment ensemble models for genomic prediction in common bean (Phaseolus vulgaris L.).

The plant genome Jun 1, 2025
For important food crops such as the common bean (Phaseolus vulgaris, L.), global demand continues to outpace the rate of genetic gain for quantitative traits. In this study, we leveraged the multi-environment trial (MET) dataset from the cooperative...
Phenotype Models, Genetic Phaseolus Genome, Plant Genomics Polymorphism, Single Nucleotide Genotype Plant Breeding Quantitative Trait Loci Gene-Environment Interaction
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Enhancing prediction accuracy of key biomass partitioning traits in wheat using multi-kernel genomic prediction models integrating secondary traits and environmental covariates.

The plant genome Jun 1, 2025
Achieving significant genetic gains in grain yield (GY) in wheat (Triticum aestivum L.) requires optimization of the key biomass partitioning traits such as spike partitioning index (SPI) and fruiting efficiency (FE). However, traditional manual phen...
Genomics Edible Grain Phenotype Models, Genetic Plant Breeding Biomass Triticum
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