Cargando…

Unsupervised machine learning and prognostic factors of survival in chronic lymphocytic leukemia

OBJECTIVE: Unsupervised machine learning approaches hold promise for large-scale clinical data. However, the heterogeneity of clinical data raises new methodological challenges in feature selection, choosing a distance metric that captures biological meaning, and visualization. We hypothesized that...

Descripción completa

Detalles Bibliográficos
Autores principales:	Coombes, Caitlin E, Abrams, Zachary B, Li, Suli, Abruzzo, Lynne V, Coombes, Kevin R
Formato:	Online Artículo Texto
Lenguaje:	English
Publicado:	Oxford University Press 2020
Materias:	Research and Applications
Acceso en línea:	https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7647286/ https://www.ncbi.nlm.nih.gov/pubmed/32483590 http://dx.doi.org/10.1093/jamia/ocaa060

Ejemplares similares

Mercator: a pipeline for multi-method, unsupervised visualization and distance generation
por: Abrams, Zachary B., et al.
Publicado: (2021)

CytoGPS: a web-enabled karyotype analysis tool for cytogenetics
por: Abrams, Zachary B, et al.
Publicado: (2019)

Pattern recognition in lymphoid malignancies using CytoGPS and Mercator
por: Abrams, Zachary B., et al.
Publicado: (2021)

Trisomy 12 chronic lymphocytic leukemia expresses a unique set of activated and targetable pathways
por: Abruzzo, Lynne V., et al.
Publicado: (2018)

Immunohistochemical detection of ZAP70 in chronic lymphocytic leukemia predicts immunoglobulin heavy chain gene mutation status and time to progression
por: Admirand, Joan H., et al.
Publicado: (2010)

Thirty biologically interpretable clusters of transcription factors distinguish cancer type
por: Abrams, Zachary B., et al.
Publicado: (2018)

Learning unsupervised contextual representations for medical synonym discovery
por: Schumacher, Elliot, et al.
Publicado: (2019)

A Two-Gene Signature, SKI and SLAMF1, Predicts Time-to-Treatment in Previously Untreated Patients with Chronic Lymphocytic Leukemia
por: Schweighofer, Carmen D., et al.
Publicado: (2011)

Prognostic significance of translocations in the presence of mutated IGHV and of cytogenetic complexity at diagnosis of chronic lymphocytic leukemia
por: Heerema, Nyla A., et al.
Publicado: (2020)

mlr3proba: an R package for machine learning in survival analysis
por: Sonabend, Raphael, et al.
Publicado: (2021)

Chronic Lymphocytic Leukemia Progression Diagnosis with Intrinsic Cellular Patterns via Unsupervised Clustering
por: Chen, Pingjun, et al.
Publicado: (2022)

Cell Layers: uncovering clustering structure in unsupervised single-cell transcriptomic analysis
por: Blair, Andrew P, et al.
Publicado: (2022)

Transcriptome Complexity Disentangled: A Regulatory Molecules Approach
por: Asiaee, Amir, et al.
Publicado: (2023)

An approach to predicting patient experience through machine learning and social network analysis
por: Bari, Vitej, et al.
Publicado: (2020)

ODNA: identification of organellar DNA by machine learning
por: Martin, Roman, et al.
Publicado: (2023)

Del(20q) in patients with chronic lymphocytic leukemia: A therapy-related abnormality involving lymphoid or myeloid cells
por: Yin, C. Cameron, et al.
Publicado: (2015)

Machine learning for initial insulin estimation in hospitalized patients
por: Nguyen, Minh, et al.
Publicado: (2021)

Unsupervised Machine Learning to Identify Depressive Subtypes
por: Kung, Benson, et al.
Publicado: (2022)

Unsupervised Machine Learning Applied to Seismic Interpretation: Towards an Unsupervised Automated Interpretation Tool
por: Celecia, Alimed, et al.
Publicado: (2021)

Application of machine learning methods in clinical trials for precision medicine
por: Wang, Yizhuo, et al.
Publicado: (2022)

mOWL: Python library for machine learning with biomedical ontologies
por: Zhapa-Camacho, Fernando, et al.
Publicado: (2022)

Genome mining for anti-CRISPR operons using machine learning
por: Yang, Bowen, et al.
Publicado: (2023)

Prognostic Role of Lymphocyte/Monocyte Ratio in Chronic Lymphocytic Leukemia
por: Yokus, Osman, et al.
Publicado: (2020)

From leaves to labels: Building modular machine learning networks for rapid herbarium specimen analysis with LeafMachine2
por: Weaver, William N., et al.
Publicado: (2023)

POOL server: machine learning application for functional site prediction in proteins
por: Somarowthu, Srinivas, et al.
Publicado: (2012)

ClinicNet: machine learning for personalized clinical order set recommendations
por: Wang, Jonathan X, et al.
Publicado: (2020)

Machine learning for early detection of sepsis: an internal and temporal validation study
por: Bedoya, Armando D, et al.
Publicado: (2020)

Scalable machine learning-assisted model exploration and inference using Sciope
por: Singh, Prashant, et al.
Publicado: (2020)

Comparison and interpretability of machine learning models to predict severity of chest injury
por: Kulshrestha, Sujay, et al.
Publicado: (2021)

Aliro: an automated machine learning tool leveraging large language models
por: Choi, Hyunjun, et al.
Publicado: (2023)

Unsupervised learning for prognostic validity in patients with chronic pain in transdisciplinary pain care
por: Strigo, Irina A., et al.
Publicado: (2023)

A novel model to label delirium in an intensive care unit from clinician actions
por: Coombes, Caitlin E., et al.
Publicado: (2021)

Identification of Six Diagnostic Biomarkers for Chronic Lymphocytic Leukemia Based on Machine Learning Algorithms
por: Zhu, Yidong, et al.
Publicado: (2022)

Detecting diabetic retinopathy through machine learning on electronic health record data from an urban, safety net healthcare system
por: Ogunyemi, Omolola I, et al.
Publicado: (2021)

Thresher: determining the number of clusters while removing outliers
por: Wang, Min, et al.
Publicado: (2018)

Sources of nonlinearity in cDNA microarray expression measurements
por: Ramdas, Latha, et al.
Publicado: (2001)

Trends in anesthesiology research: a machine learning approach to theme discovery and summarization
por: Rusanov, Alexander, et al.
Publicado: (2018)

Predictive modeling in urgent care: a comparative study of machine learning approaches
por: Tang, Fengyi, et al.
Publicado: (2018)

Machine learning identifies girls with central precocious puberty based on multisource data
por: Pan, Liyan, et al.
Publicado: (2020)

Linkage of Hospital Records and Death Certificates by a Search Engine and Machine Learning
por: Cossin, Sebastien, et al.
Publicado: (2021)

Cannot write session to /tmp/vufind_sessions/sess_5uqt72gd1bg8h83mjocj92ejsg