MiSurv: an Integrative Web Cloud Platform for User-Friendly Microbiome Data Analysis with Survival Responses.

Investigators have studied the treatment effects on human health or disease, the treatment effects on human microbiome, and the roles of the microbiome on human health or disease. Especially, in a clinical trial, investigators commonly trace disease status over a lengthy period to survey the sequential disease progression for different treatment groups (e.g.

Integrative web cloud computing and analytics using MiPair for design-based comparative analysis with paired microbiome data.

Pairing (or blocking) is a design technique that is widely used in comparative microbiome studies to efficiently control for the effects of potential confounders (e.g., genetic, environmental, or behavioral factors).

MiCloud: A unified web platform for comprehensive microbiome data analysis.

The recent advance in massively parallel sequencing has enabled accurate microbiome profiling at a dramatically lowered cost. Then, the human microbiome has been the subject of intensive investigation in public health and medicine. In the meanwhile, researchers have developed lots of microbiome data analysis methods, protocols, and/or tools.

Deep-learning-based automated terminology mapping in OMOP-CDM.

Objective: Accessing medical data from multiple institutions is difficult owing to the interinstitutional diversity of vocabularies. Standardization schemes, such as the common data model, have been proposed as solutions to this problem, but such schemes require expensive human supervision. This study aims to construct a trainable system that can automate the process of semantic interinstitutional code mapping.

MildInt: Deep Learning-Based Multimodal Longitudinal Data Integration Framework.

As large amounts of heterogeneous biomedical data become available, numerous methods for integrating such datasets have been developed to extract complementary knowledge from multiple domains of sources. Recently, a deep learning approach has shown promising results in a variety of research areas. However, applying the deep learning approach requires expertise for constructing a deep architecture that can take multimodal longitudinal data.

Predicting Alzheimer's disease progression using multi-modal deep learning approach.

Alzheimer's disease (AD) is a progressive neurodegenerative condition marked by a decline in cognitive functions with no validated disease modifying treatment. It is critical for timely treatment to detect AD in its earlier stage before clinical manifestation. Mild cognitive impairment (MCI) is an intermediate stage between cognitively normal older adults and AD.

Biological Brain Age Prediction Using Cortical Thickness Data: A Large Scale Cohort Study.

Brain age estimation from anatomical features has been attracting more attention in recent years. This interest in brain age estimation is motivated by the importance of biological age prediction in health informatics, with an application to early prediction of neurocognitive disorders. It is well-known that normal brain aging follows a specific pattern, which enables researchers and practitioners to predict the age of a human's brain from its degeneration.