AI Medical Compendium Topic

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Cluster Analysis

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An Edge-Cloud-Aided Private High-Order Fuzzy C-Means Clustering Algorithm in Smart Healthcare.

IEEE/ACM transactions on computational biology and bioinformatics
Smart healthcare has emerged to provide healthcare services using data analysis techniques. Especially, clustering is playing an indispensable role in analyzing healthcare records. However, large multi-modal healthcare data imposes great challenges o...

Ensemble of machine learning techniques to predict survival in kidney transplant recipients.

Computers in biology and medicine
Kidney transplant recipients face a high cardiovascular risk, which is a leading cause of death in this patient group. This article proposes the application of clustering techniques and feature selection to predict the survival outcomes of kidney tra...

Unlocking the Potential of Clustering and Classification Approaches: Navigating Supervised and Unsupervised Chemical Similarity.

Environmental health perspectives
BACKGROUND: The field of toxicology has witnessed substantial advancements in recent years, particularly with the adoption of new approach methodologies (NAMs) to understand and predict chemical toxicity. Class-based methods such as clustering and cl...

Interactive molecular causal networks of hypertension using a fast machine learning algorithm MRdualPC.

BMC medical research methodology
BACKGROUND: Understanding the complex interactions between genes and their causal effects on diseases is crucial for developing targeted treatments and gaining insight into biological mechanisms. However, the analysis of molecular networks, especiall...

KnowledgeVIS: Interpreting Language Models by Comparing Fill-in-the-Blank Prompts.

IEEE transactions on visualization and computer graphics
Recent growth in the popularity of large language models has led to their increased usage for summarizing, predicting, and generating text, making it vital to help researchers and engineers understand how and why they work. We present KnowledgeVIS, a...

Detecting differentially expressed genes from RNA-seq data using fuzzy clustering.

The international journal of biostatistics
A two-group comparison test is generally performed on RNA sequencing data to detect differentially expressed genes (DEGs). However, the accuracy of this method is low due to the small sample size. To address this, we propose a method using fuzzy clus...

Communicating exploratory unsupervised machine learning analysis in age clustering for paediatric disease.

BMJ health & care informatics
BACKGROUND: Despite the increasing availability of electronic healthcare record (EHR) data and wide availability of plug-and-play machine learning (ML) Application Programming Interfaces, the adoption of data-driven decision-making within routine hos...

Differentiable self-supervised clustering with intrinsic interpretability.

Neural networks : the official journal of the International Neural Network Society
Self-supervised clustering has garnered widespread attention due to its ability to discover latent clustering structures without the need for external labels. However, most existing approaches on self-supervised clustering lack of inherent interpreta...

AAontology: An Ontology of Amino Acid Scales for Interpretable Machine Learning.

Journal of molecular biology
Amino acid scales are crucial for protein prediction tasks, many of them being curated in the AAindex database. Despite various clustering attempts to organize them and to better understand their relationships, these approaches lack the fine-grained ...