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Similar Items: SNP-based prediction of schizophrenia using machine learning

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Dynamic Fusion of Genomics and Functional Network Connectivity in UK Biobank Reveals Schizophrenia‐Related SNP Manifolds
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Prioritization of SNP markers for genomic prediction in closed beef cattle populations
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Predicting Biochar‐Induced Changes in Soil Organic Carbon With Ensemble Machine Learning
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Generalizable machine learning models for rapid antimicrobial resistance prediction in unseen health care settings
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A Machine Learning Approach for Predicting Failed First-Attempt Radial Artery Puncture Patients with Heart Failure
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Application of machine learning in diabetes diagnosis
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Correction to: Spatiotemporal predictions of toxic urban plumes using deep learning
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Complexity of resting cortical activity predicts neurophysiological responses to theta-burst stimulation but fails to generalize: A rigorous machine-learning approach
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Insights into the origin of DNA methylation differences between monozygotic twins discordant for schizophrenia
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Rare Natural SNP Activates SHOOT APICAL MERISTEM ENLARGER1 to Increase Branch Number and Silique Number on the Main Inflorescence in Brassica napus
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Predicting the Plastic Rotational Capacity of Reinforced Concrete Elements Using Machine Learning
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Learning from Digital Collections: Digitisation and Machine Learning for Large-Scale Museum Studies
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Enhanced formulation of precision probiotics through active machine learning
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A hybrid framework for disease biomarker discovery in microbiome research combining Bayesian networks, machine learning, and network-based methods
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Perceptual prediction error supports implicit process in motor learning
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Harnessing mmWave signals and machine learning for noninvasive taxonomic classification of insects
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Are routinely collected clinical and sociodemographic characteristics associated with social functioning and activities of daily living in schizophrenia? A machine learning approach descriptive of a schizophrenia cohort
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Data-based clustering in prediction of cervical cancer DNA methylation using pan-cancer genetic and clinical data
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Accurate and Generalizable Protein-Ligand Binding Affinity Prediction With Geometric Deep Learning
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ASCENT: an active transfer learning paradigm for efficient drug-target interaction prediction
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Machine learning can distinguish orphans that have resulted from sequence divergence beyond recognition
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pyRootHair: Machine learning accelerated software for high-throughput phenotyping of plant root hair traits
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CanDrivR-CS: a cancer-specific machine learning framework for distinguishing recurrent and rare variants
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A machine learning approach to driver attribution of dissolved organic matter dynamics in two contrasting freshwater systems