Results 121 to 130 of about 1,171,833 (351)

ML Workflows for Screening Degradation‐Relevant Properties of Forever Chemicals

Advanced Science, EarlyView.
The environmental persistence of per‐ and polyfluoroalkyl substances (PFAS) necessitates efficient remediation strategies. This study presents physics‐informed machine learning workflows that accurately predict critical degradation properties, including bond dissociation energies and polarizability.
Pranoy Ray, Andrew R. Castillo, Manoj Kolel‐Veetil, Surya R. Kalidindi   +3 more
wiley   +1 more source

PAIR: Reconstructing Single‐Cell Open‐Chromatin Landscapes for Transcription Factor Regulome Mapping

Advanced Science, EarlyView.
scATAC‐seq analysis is often constrained by limited sequencing depth, extreme sparsity, and pervasive technical missingness. PAIR is a probabilistic framework that restores scATAC‐seq accessibility profiles by directly modeling the native cell–peak bipartite structure of chromatin accessibility.
Yanchi Su, Qi Qi, Yi Fan, Yubo Wang, Gaoyang Hao, Ka‐Chun Wong, Yunhe Wang, Xiangtao Li   +7 more
wiley   +1 more source

Distinct Biotypes of Visual Perception in Major Depressive Disorder

Advanced Science, EarlyView.
In a discover dataset (272 acute MDD patients), this work identifies a novel depression biotype characterized by impaired visual motion perception, using machine learning clustering. An independent dataset confirms the robustness of this biotype through cross‐validation and demonstrates its generalizability.
Zhuoran Cai, Xi‐Wen Hu, Yuyang Li, Zheng Lin, Yongjie Li, Yong‐Chun Cai, Tao Xu, Ke Jia, Jin‐Fang Han, Zhong‐Lin Tan, Dezhong Yao, Xue Mei Song, Sugai Liang, Georg Northoff   +13 more
wiley   +1 more source

Unsupervised Learning Approach for Anomaly Detection in Industrial Control Systems

Applied System Innovation
Industrial control systems (ICSs) play a crucial role in managing and monitoring critical processes across various industries, such as manufacturing, energy, and water treatment.
Woo-Hyun Choi, Jongwon Kim
doaj   +1 more source

Graphs in unsupervised machine learning

Graphs are versatile data structures that represent individual data points and the intricate relationships between them, making them invaluable for modelling complex real-world systems. Their flexibility allows them to be applied to a wide range of problems, and with appropriate algorithms, they have become crucial in many domains.
openaire   +3 more sources

Modeling the Physical Characteristics of Ultrasound Pretreated Dialium guineense Whole Fruits Using Neural Network Pattern Recognition Model

AgriFood: Journal of Agricultural Products for Food, EarlyView.
The study of Physical characteristics of agricultural materials is necessary for the design of processes and machines for food production. Length, width, thickness, sphericity, unit mass, and aspect ratio were the physical characteristics of ultrasound pretreated Dialium guineense whole fruits subjected to Neural network modeling.
Mfrekemfon Akpan, Uduak George, Okon Esua   +2 more
wiley   +1 more source

Unsupervised and supervised machine learning to identify variability of tumor-educated platelets and association with pan-cancer: A cross-national study

, 2023
Xiong Chen, Runnan Shen, Lin Lv, Dongxi Zhu, Guochang You, Zhenluan Tian, Jinwei Chen, Lin Shen, Jiatang Xu, Guibin Hong, Hu Li, Mingli Luo, Lin Cao, Shaoxu Wu, Kai Huang   +14 more
openalex   +1 more source

Import Wheat Tenders and the Effects of the Russian Invasion

Agribusiness, EarlyView.
ABSTRACT Risk and volatility for many commodities escalated sharply following the Russian invasion of Ukraine, creating numerous uncertainties for trading firms and importers. The purpose of this study is to analyze the bidding behavior in Egyptian wheat import tenders in the pre‐ and post‐invasion periods.
William W. Wilson, Sumadhur Shakya, Isaac Dubovoy   +2 more
wiley   +1 more source

Handling DNA malfunctions by unsupervised machine learning model

, 2023
Mutaz Khazaaleh, Mohammad A. Alsharaiah, Wafa Alsharafat, Ahmad Adel Abu-Shareha, Feras A. Haziemeh, Malek M. Al-Nawashi, Mwaffaq Abu Alhija   +6 more
openalex   +1 more source

Predicting High‐Resolution Spatial and Spectral Features in Mass Spectrometry Imaging with Machine Learning and Multimodal Data Fusion

Advanced Intelligent Discovery, EarlyView.
A multimodal fusion pipeline predicts high‐resolution ion distributions in imaging mass spectrometry by integrating Fourier transform ion cyclotron resonance, time‐of‐flight matrix‐assisted laser desorption/ionization, and time‐of‐flight secondary ion mass spectrometry data.
Md Inzamam Ul Haque, Ramakrishnan Kannan, Jacob D. Hinkle, Sylwia A. Stopka, Anton V. Ievlev, Nathalie Y. R. Agar, Olga S. Ovchinnikova, Debangshu Mukherjee   +7 more
wiley   +1 more source