The rapid development of diagnostic technologies in healthcare is leading to higher
requirements for physicians to handle and integrate the heterogeneous, yet complementary …

Single-cell-resolved systems biology methods, including omics-and imaging-based
measurement modalities, generate a wealth of high-dimensional data characterizing the …

Abstract Analysis of gene expression data is crucial for disease prognosis and diagnosis.
Gene expression data has high redundancy and noise that brings challenges in extracting …

While attracting increasing research attention in science and technology, Machine Learning
(ML) is playing a critical role in the digitalization of manufacturing operations towards …

Modern oncology offers a wide range of treatments and therefore choosing the best option
for particular patient is very important for optimal outcome. Multi-omics profiling in …

When clinicians assess the prognosis of patients in intensive care, they take imaging and
non-imaging data into account. In contrast, many traditional machine learning models rely …

The growing interest in the use of IoT technologies has generated the development of
numerous and diverse applications. Many of the services provided by the applications are …

The development of computer vision-based deep learning models for accurate two-
dimensional (2D) image classification has enabled us to surpass existing machine learning …

Annotation of cell-types is a critical step in the analysis of single-cell RNA sequencing
(scRNA-seq) data that allows the study of heterogeneity across multiple cell populations …

Although human activity recognition (HAR) based on WiFi channel state information (CSI)
has been widely studied, as the CSI signal is susceptible to the external environment, a …