Statistical normalization methods in microbiome data with application to microbiome cancer research
Y **a - Gut Microbes, 2023 - Taylor & Francis
Mounting evidence has shown that gut microbiome is associated with various cancers,
including gastrointestinal (GI) tract and non-GI tract cancers. But microbiome data have …
including gastrointestinal (GI) tract and non-GI tract cancers. But microbiome data have …
Overview of data preprocessing for machine learning applications in human microbiome research
Although metagenomic sequencing is now the preferred technique to study microbiome-host
interactions, analyzing and interpreting microbiome sequencing data presents challenges …
interactions, analyzing and interpreting microbiome sequencing data presents challenges …
Optimal deep learning based fusion model for biomedical image classification
RF Mansour, NM Alfar, S Abdel‐Khalek… - Expert …, 2022 - Wiley Online Library
Automated examination of biomedical signals plays a vital role to diagnose diseases and
offers useful data to several applications in the areas of physiology, sports medicine, and …
offers useful data to several applications in the areas of physiology, sports medicine, and …
DeepFeature: feature selection in nonimage data using convolutional neural network
Artificial intelligence methods offer exciting new capabilities for the discovery of biological
mechanisms from raw data because they are able to detect vastly more complex patterns of …
mechanisms from raw data because they are able to detect vastly more complex patterns of …
Stacking and chaining of normalization methods in deep learning-based classification of colorectal cancer using gut microbiome data
Machine learning (ML)-based detection of diseases using sequence-based gut microbiome
data has been of great interest within the artificial intelligence in medicine (AIM) community …
data has been of great interest within the artificial intelligence in medicine (AIM) community …
[HTML][HTML] Supervised machine learning for microbiomics: bridging the gap between current and best practices
NK Dudek, M Chakhvadze, S Kobakhidze… - Machine Learning with …, 2024 - Elsevier
Abstract Machine learning (ML) is poised to drive innovations in clinical microbiomics, such
as in disease diagnostics and prognostics. However, the successful implementation of ML in …
as in disease diagnostics and prognostics. However, the successful implementation of ML in …
Data augmentation for compositional data: Advancing predictive models of the microbiome
E Gordon-Rodriguez, T Quinn… - Advances in Neural …, 2022 - proceedings.neurips.cc
Data augmentation plays a key role in modern machine learning pipelines. While numerous
augmentation strategies have been studied in the context of computer vision and natural …
augmentation strategies have been studied in the context of computer vision and natural …
Multi-stage biomedical feature selection extraction algorithm for cancer detection
Cancer is a significant cause of death worldwide. Early cancer detection is greatly aided by
machine learning and artificial intelligence (AI) to gene microarray data sets (microarray …
machine learning and artificial intelligence (AI) to gene microarray data sets (microarray …
A Multistage Hybrid Deep Learning Model for Enhanced Solar Tracking
Solar tracking helps maximize the efficiency of solar applications, such as photovoltaic (PV)
solar panels. In the recent past, machine learning (ML) techniques have been extensively …
solar panels. In the recent past, machine learning (ML) techniques have been extensively …
A review of machine learning methods for cancer characterization from microbiome data
Recent studies have shown that the microbiome can impact cancer development,
progression, and response to therapies suggesting microbiome-based approaches for …
progression, and response to therapies suggesting microbiome-based approaches for …