Suchergebnisse - Autoencoder-based phenotypic*

Multi-Omics Data Integration for Improved Cancer Subtyping via Denoising Autoencoder-Based Multi-Kernel Learning.

Autoren: Yao, Xiukun Wang, Tong Yang, Qi et al.

Quelle: Genes; Nov2025, Vol. 16 Issue 11, p1246, 24p

Schlagwörter: MULTIOMICS, DATA integration, AUTOENCODERS, ENSEMBLE learning, BIOMARKERS, INDIVIDUALIZED medicine, SURVIVAL analysis (Biometry), TUMOR classification

Phenotypic screening and genetic insights for predicting major vascular-related diseases using retinal imaging.

Autoren: Lu, Menglin Mao, Yiheng Zhu, Hui et al.

Quelle: NPJ Digital Medicine; 7/14/2025, Vol. 8 Issue 1, p1-16, 16p

Schlagwörter: VASCULAR disease diagnosis, RETINA abnormalities, RISK assessment, MYOCARDIAL infarction, RESEARCH funding, OPTICAL coherence tomography, MENDELIAN randomization, QUANTITATIVE research, DESCRIPTIVE statistics, GENETIC risk score, CHRONIC kidney failure, RETINA, MEDICAL screening, ATTRIBUTION (Social psychology), STROKE, DATA analysis software, VASCULAR diseases, PHENOTYPES, DISEASE risk factors

Firma/Körperschaft: UK Biobank Ltd.

Anomaly detection for high-content image-based phenotypic cell profiling

Autoren: Alon Shpigler Naor Kolet Shahar Golan et al.

Quelle: bioRxiv

Schlagwörter: Article

Zugangs-URL: https://pubmed.ncbi.nlm.nih.gov/38895267

A machine learning approach for predicting familial and sporadic disease cases based on clinical symptoms: introduction of a new dataset.

Alternate Title: Klinik belirtilere dayalı ailesel ve sporadik hastalık vakalarını tahmin etmek için bir makine öğrenimi yaklaşımı: yeni bir veri kümesinin tanıtımı. (Turkish)

Autoren: SHARAFI, Parisa ARSLAN, Hilal ERSOY EVANS, Sibel et al.

Quelle: Turkish Bulletin of Hygiene & Experimental Biology / Türk Hijyen ve Deneysel Biyoloji; 2025, Vol. 82 Issue 1, p99-106, 8p

Schlagwörter: MACHINE learning, ARTIFICIAL neural networks, SUPPORT vector machines, NEUROFIBROMATOSIS 1, PHENOTYPIC plasticity

Automated Honey Bee Subspecies Identification Using Advanced Wing Venation Analysis and Adaptive Hierarchical Clustering.

Autoren: Dadgostar, Shahram Mobini Dehkordi, Parsa Khodarahmi, Saman et al.

Quelle: Ecology & Evolution (20457758); Sep2025, Vol. 15 Issue 9, p1-16, 16p

Schlagwörter: SUBSPECIES, IMAGE processing, MORPHOMETRICS, CLUSTER analysis (Statistics), HONEYBEES, POLLINATION, BIODIVERSITY conservation

Anomaly detection for high-content image-based phenotypic cell profiling.

Autoren: Shpigler, Alon Kolet, Naor Golan, Shahar et al.

Quelle: Cell Syst ; ISSN:2405-4720

Schlagwörter: anomaly detection, explainability, high-content image-based cell profiling

Relation: https://doi.org/10.1016/j.cels.2025.101429; https://pubmed.ncbi.nlm.nih.gov/41167192

Verfügbarkeit: https://doi.org/10.1016/j.cels.2025.101429
https://pubmed.ncbi.nlm.nih.gov/41167192

Deep Sparse Autoencoder-based Feature Selection for SNPs validation in Prostate Cancer Radiogenomics

Autoren: M. C. Massi F. Ieva A. Paganoni et al.

Schlagwörter: Deep Learning, Feature selection, Radiogenomics, Autoencoder, SNPs

Dateibeschreibung: application/pdf

Zugangs-URL: https://hdl.handle.net/11311/1150035

CopyVAE: a variational autoencoder-based approach for copy number variation inference using single-cell transcriptomics.

Autoren: Kurt, Semih Chen, Mandi Toosi, Hosein et al.

Quelle: Bioinformatics; May2024, Vol. 40 Issue 5, p1-9, 9p

Schlagwörter: TRANSCRIPTOMES, RNA sequencing, DEEP learning, CANCER invasiveness, SENSITIVITY & specificity (Statistics)

Firma/Körperschaft: MASSACHUSETTS Institute of Technology

Technical and legal aspects of federated learning in bioinformatics: applications, challenges and opportunities.

Autoren: Malpetti, Daniele Scutari, Marco Gualdi, Francesco et al.

Quelle: Frontiers in Digital Health; 2025, p1-17, 17p

Schlagwörter: FEDERATED learning, GENOME-wide association studies, MULTIOMICS, PRIVACY, BIOINFORMATICS, PROTEOMICS, MASS spectrometry, MEDICAL ethics, GENOTYPES, PHENOTYPES

Geografische Kategorien: UNITED States, SWITZERLAND, UNITED Kingdom, NETHERLANDS

Firma/Körperschaft: UK Biobank Ltd.

A Deep Autoencoder Compression-Based Genomic Prediction Method for Whole-Genome Sequencing Data.

Autoren: Song, Hailiang Dong, Tian Wang, Wei et al.

Quelle: Biology (2079-7737); Nov2025, Vol. 14 Issue 11, p1622, 17p

Schlagwörter: WHOLE genome sequencing, AUTOENCODERS, ANIMAL breeding, OPTIMIZATION algorithms, DATA compression, DATA structures, ARTIFICIAL intelligence

Enhancing yield prediction from plot-level satellite imagery through genotype and environment feature disentanglement.

Autoren: Powadi, Anirudha A. Jubery, Talukder Z. Tross, Michael et al.

Quelle: Frontiers in Plant Science; 2025, p1-16, 16p

Schlagwörter: GENOTYPES, DEEP learning, FEATURE extraction, PLANT breeding, REMOTE-sensing images, HUMAN ecology, PRECISION farming, AGRICULTURAL forecasts

Statistical Methods for Multi-Omics Analysis in Neurodevelopmental Disorders: From High Dimensionality to Mechanistic Insight.

Autoren: Airoldi, Manuel Remori, Veronica Fasano, Mauro

Quelle: Biomolecules (2218-273X); Oct2025, Vol. 15 Issue 10, p1401, 18p

Schlagwörter: MULTIOMICS, MULTIVARIATE analysis, DATA analysis, MACHINE learning, BIOMARKERS, MATHEMATICAL statistics, MECHANISM (Philosophy), NEURAL development

ODBAE: a high-performance model identifying complex phenotypes in high-dimensional biological datasets.

Autoren: Shen, Yafei Zhang, Tao Liu, Zhiwei et al.

Quelle: Communications Biology; 10/2/2025, Vol. 8 Issue 1, p1-19, 19p

Schlagwörter: PHENOTYPES, BIOLOGICAL systems, ENERGY metabolism, BIOLOGICAL databases, MACHINE learning, OUTLIER detection, AUTOENCODERS

AE-TPGG: a novel autoencoder-based approach for single-cell RNA-seq data imputation and dimensionality reduction.

Autoren: Zhao, Shuchang Zhang, Li Liu, Xuejun

Quelle: Frontiers of Computer Science; Jun2023, Vol. 17 Issue 3, p1-18, 18p

Detecting antibiotic resistance: classical, molecular, advanced bioengineering, and AI-enhanced approaches.

Autoren: Aldea, Alexandru Constantin Diguṭă, Filofteia Camelia Presacan, Oriana et al.

Quelle: Frontiers in Microbiology; 2025, p1-33, 33p

Schlagwörter: DRUG resistance in bacteria, ARTIFICIAL intelligence, BIOENGINEERING, DETECTION algorithms, MICROFLUIDICS, CRISPRS, BIOSENSORS, MOLECULAR diagnosis

A comparison of integration methods for single‐cell RNA sequencing data and ATAC sequencing data.

Autoren: Kan, Yulong Wang, Weihao Qi, Yunjing et al.

Quelle: Quantitative Biology; Jun2025, Vol. 13 Issue 2, p1-19, 19p

Schlagwörter: DATA integration, RNA sequencing, TASK analysis, CLUSTER analysis (Statistics), METABOLOMICS

Drug–drug interaction prediction with Wasserstein Adversarial Autoencoder-based knowledge graph embeddings.

Autoren: Dai, Yuanfei Guo, Chenhao Guo, Wenzhong et al.

Quelle: Briefings in Bioinformatics; Jul2021, Vol. 22 Issue 4, p1-16, 16p

Schlagwörter: DRUG interactions, KNOWLEDGE graphs, DRUG development, FORECASTING

Genomic selection: Essence, applications, and prospects.

Autoren: Escamilla, Diana M. Li, Dongdong Negus, Karlene L. et al.

Quelle: Plant Genome; Jun2025, Vol. 18 Issue 2, p1-32, 32p

Machine Learning for Prediction of Resistance Scores in Wheat (Triticum aestivum L.).

Autoren: Heilmann, Philipp Georg Difabachew, Yohannes Fekadu Frisch, Matthias et al.

Quelle: Plant Breeding; Apr2025, Vol. 144 Issue 2, p192-205, 14p

Schlagwörter: FEATURE selection, SUPPORT vector machines, MYCOSES, RANDOM forest algorithms, WHEAT

Interpretable Fine‐Grained Phenotypes of Subcellular Dynamics via Unsupervised Deep Learning.

Autoren: Wang, Chuangqi Choi, Hee June Woodbury, Lucy et al.

Quelle: Advanced Science; 11/6/2024, Vol. 11 Issue 41, p1-21, 21p

Schlagwörter: ARTIFICIAL neural networks, CELL imaging, MACHINE learning, FEATURE extraction, CELL migration