Cargando…

Editorial: Transparent machine learning in bio-medicine

Detalles Bibliográficos
Autores principales:	Diaz Ochoa, Juan G., Marquardt, André
Formato:	Online Artículo Texto
Lenguaje:	English
Publicado:	Frontiers Media S.A. 2023
Materias:	Bioinformatics
Acceso en línea:	https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10436735/ https://www.ncbi.nlm.nih.gov/pubmed/37600968 http://dx.doi.org/10.3389/fbinf.2023.1264803

Ejemplares similares

Bayesian logical neural networks for human-centered applications in medicine
por: Diaz Ochoa, Juan G., et al.
Publicado: (2023)

Editorial: Graph representation learning in biological network
por: Roy, Swarup, et al.
Publicado: (2023)

Machine learning enhanced cell tracking
por: Soelistyo, Christopher J., et al.
Publicado: (2023)

Identification of predictive factors of the degree of adherence to the Mediterranean diet through machine-learning techniques
por: Arceo-Vilas, Alba, et al.
Publicado: (2020)

Solving musculoskeletal biomechanics with machine learning
por: Smirnov, Yaroslav, et al.
Publicado: (2021)

Machine-learning-based automated quantification machine for virus plaque assay counting
por: Phanomchoeng, Gridsada, et al.
Publicado: (2022)

Machine learning analysis of TCGA cancer data
por: Liñares-Blanco, Jose, et al.
Publicado: (2021)

An interpretable machine learning model for diagnosis of Alzheimer's disease
por: Das, Diptesh, et al.
Publicado: (2019)

Comparison of machine learning and deep learning techniques in promoter prediction across diverse species
por: Bhandari, Nikita, et al.
Publicado: (2021)

A systematic hybrid machine learning approach for stress prediction
por: Ding, Cheng, et al.
Publicado: (2023)

Comparison of machine learning approaches for enhancing Alzheimer’s disease classification
por: Li, Qi, et al.
Publicado: (2021)

Machine Learning for Causal Inference in Biological Networks: Perspectives of This Challenge
por: Lecca, Paola
Publicado: (2021)

Predicting liver cancer on epigenomics data using machine learning
por: Vekariya, Vishalkumar, et al.
Publicado: (2022)

Drought stress detection technique for wheat crop using machine learning
por: Gupta, Ankita, et al.
Publicado: (2023)

Deep learning and support vector machines for transcription start site identification
por: Barbero-Aparicio, José A., et al.
Publicado: (2023)

Barcode demultiplexing of nanopore sequencing raw signals by unsupervised machine learning
por: Papetti, Daniele M., et al.
Publicado: (2023)

Prioritizing bona fide bacterial small RNAs with machine learning classifiers
por: Eppenhof, Erik J.J., et al.
Publicado: (2019)

Delineating the impact of machine learning elements in pre-microRNA detection
por: Saçar Demirci, Müşerref Duygu, et al.
Publicado: (2017)

Automatic single- and multi-label enzymatic function prediction by machine learning
por: Amidi, Shervine, et al.
Publicado: (2017)

Corrigendum: Machine Learning for Causal Inference in Biological Networks: Perspectives of This Challenge
por: Lecca, Paola
Publicado: (2022)

From command-line bioinformatics to bioGUI
por: Joppich, Markus, et al.
Publicado: (2019)

Multi-level machine learning prediction of protein–protein interactions in Saccharomyces cerevisiae
por: Zubek, Julian, et al.
Publicado: (2015)

Cross-platform normalization of microarray and RNA-seq data for machine learning applications
por: Thompson, Jeffrey A., et al.
Publicado: (2016)

Integrative machine learning analysis of multiple gene expression profiles in cervical cancer
por: Tan, Mei Sze, et al.
Publicado: (2018)

A systematic review of the application of machine learning in the detection and classification of transposable elements
por: Orozco-Arias, Simon, et al.
Publicado: (2019)

TransPrise: a novel machine learning approach for eukaryotic promoter prediction
por: Pachganov, Stepan, et al.
Publicado: (2019)

Comparison of machine learning techniques to handle imbalanced COVID-19 CBC datasets
por: Dorn, Marcio, et al.
Publicado: (2021)

Diagnosis of vertebral column pathologies using concatenated resampling with machine learning algorithms
por: Reshi, Aijaz Ahmad, et al.
Publicado: (2021)

Comparative analysis of machine learning approaches to analyze and predict the COVID-19 outbreak
por: Naeem, Muhammad, et al.
Publicado: (2021)

Machine learning methods for protein-protein binding affinity prediction in protein design
por: Guo, Zhongliang, et al.
Publicado: (2022)

BioShake: a Haskell EDSL for bioinformatics workflows
por: Bedő, Justin
Publicado: (2019)

Rhea: a transparent and modular R pipeline for microbial profiling based on 16S rRNA gene amplicons
por: Lagkouvardos, Ilias, et al.
Publicado: (2017)

Graph data science and machine learning for the detection of COVID-19 infection from symptoms
por: Alqaissi, Eman, et al.
Publicado: (2023)

Comparative analysis of machine learning approaches for predicting respiratory virus infection and symptom severity
por: Işık, Yunus Emre, et al.
Publicado: (2023)

Omics data integration in computational biology viewed through the prism of machine learning paradigms
por: Fouché, Aziz, et al.
Publicado: (2023)

Non-H3 CDR template selection in antibody modeling through machine learning
por: Long, Xiyao, et al.
Publicado: (2019)

Toward insights on determining factors for high activity in antimicrobial peptides via machine learning
por: Li, Hao, et al.
Publicado: (2019)

Early survey with bibliometric analysis on machine learning approaches in controlling COVID-19 outbreaks
por: Chiroma, Haruna, et al.
Publicado: (2020)

A machine learning framework for the prediction of chromatin folding in Drosophila using epigenetic features
por: Rozenwald, Michal B., et al.
Publicado: (2020)

Application of machine learning algorithms to predict the thyroid disease risk: an experimental comparative study
por: Islam, Saima Sharleen, et al.
Publicado: (2022)

Cannot write session to /tmp/vufind_sessions/sess_6temkn5t30lbk43e3bkoatc6eq