AIMC Topic: Cluster Analysis

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Deep-Learning-Guided Mining and Clustering of Remote Amino Acid Residues for the Simultaneous Engineering of the Catalytic Activity and Thermostability of a Processive Endoglucanase.

ACS synthetic biology
Processive endoglucanases, which possess both endo- and exoglucanase activities, are considered highly promising catalysts in cellulose degradation. In this study, we employed multiple deep learning models, including MutCompute, DeepSequence, and ESM...

Energy-efficient clustering and routing for IoT-enabled healthcare using adaptive fuzzy logic and hybrid optimization.

Scientific reports
Leveraging Internet of Things technology in healthcare, including wireless sensor networks and next-generation networks, enhances the seamless integration of medical equipment and enables intelligent interaction among devices. This advancement plays ...

Activities of Daily Living Detection through Energy Consumption Data and Machine Learning to Support Independent Aging.

Journal of medical systems
The aging population presents significant challenges for healthcare and social services, emphasizing the need for innovative solutions that support independent living. This study explores the feasibility of identifying Instrumental Activities of Dail...

Boosting K-nearest neighbor regression performance for longitudinal data through a novel learning approach.

BMC bioinformatics
BACKGROUND: Longitudinal studies often require flexible methodologies for predicting response trajectories based on time-dependent and time-independent covariates. To address the complexities of longitudinal data, this study proposes a novel extensio...

The Impact of Comorbidity Patterns on Clinical Outcomes in Heart Failure: A Machine Learning-Based Cluster Analysis.

The American journal of cardiology
Heart failure (HF) is a major global health burden, and complex comorbidity patterns can worsen clinical outcomes and complicate patient care. This study aimed to identify distinct comorbidity-based clusters among HF patients and evaluate their assoc...

A Novel Framework for Airshed Delineation and PM Estimation across India Using Machine Learning and Spatial Clustering.

Environmental science & technology
Air pollution continues to pose a major challenge in India, with PM being a key contributor to serious health risks. Its spatial distribution is influenced by climatic, topographic, and anthropogenic factors, which are often poorly represented in ana...

Improving attachment style clustering with ROCKET and CatBoost: Insights from EEG analysis.

PloS one
Understanding attachment styles is essential in psychology and neuroscience, yet predicting them using objective neural data remains challenging. This study explores the use of machine learning (ML) models and EEG analysis to improve attachment style...

Overlapping community detection based on bridging structural features and fuzzy C-means.

PloS one
In recent years, research on community structure for complex networks has received increasing greater attention, and the overlapping community structure is more closely related to the actual social structure than the non-overlapping community structu...

Exploring preparatory reading in bidirectional sight and written translation through clustering analysis of eye-tracking data.

PloS one
Preparatory reading-the phase between a translator's initial reading of the source text and the production of the first word of the target text-remains underexplored despite its crucial role in both sight (SiT) and written translation (WT). This stud...

A review on multi-omics integration for aiding study design of large scale TCGA cancer datasets.

BMC genomics
BACKGROUND: Rapid advancements in high-throughput sequencing technologies allow for detailed and accurate measurement of omics features within their biological context. The integration of different omics types creates heterogeneous datasets, presenti...