Results 91 to 100 of about 1,043,746 (297)

Unraveling the Molecular Mechanisms of Glioma Recurrence: A Study Integrating Single‐Cell and Spatial Transcriptomics

Annals of Clinical and Translational Neurology, EarlyView.
ABSTRACT Objective Glioma recurrence severely impacts patient prognosis, with current treatments showing limited efficacy. Traditional methods struggle to analyze recurrence mechanisms due to challenges in assessing tumor heterogeneity, spatial dynamics, and gene networks.
Lei Qiu, Yinjiao Fei, Jiaxuan Ding, Kexin Shi, Jinyan Luo, Yuchen Zhu, Xingjian Sun, Gefei Jiang, Yuandong Cao, Weilin Xu, Shu Zhou   +10 more
wiley   +1 more source

Machine learning exciton dynamics

Chemical Science, 2016
Machine learning ground state QM/MM for accelerated computation of exciton dynamics.
Florian Häse, Stéphanie Valleau, Edward Pyzer-Knapp, Alán Aspuru-Guzik   +3 more
openaire   +5 more sources

Filtering cells with high mitochondrial content depletes viable metabolically altered malignant cell populations in cancer single-cell studies

Genome Biology
Background Single-cell transcriptomics has transformed our understanding of cellular diversity, yet noise from technical artifacts and low-quality cells can obscure key biological signals.
Josephine Yates, Agnieszka Kraft, Valentina Boeva   +2 more
doaj   +1 more source

Screening membraneless organelle participants with machine-learning models that integrate multimodal features

, 2022
Zhaoming Chen, Chao Hou, Liang Wang, Chunyu Yu, Taoyu Chen, Boyan Shen, Yaoyao Hou, Pilong Li, Tingting Li   +8 more
openalex   +2 more sources

Remote Assessment of Ataxia Severity in SCA3 Across Multiple Centers and Time Points

Annals of Clinical and Translational Neurology, EarlyView.
ABSTRACT Objective Spinocerebellar ataxia type 3 (SCA3) is a genetically defined ataxia. The Scale for Assessment and Rating of Ataxia (SARA) is a clinician‐reported outcome that measures ataxia severity at a single time point. In its standard application, SARA fails to capture short‐term fluctuations, limiting its sensitivity in trials.
Marcus Grobe‐Einsler, Vivian Maas, Arian Taheri Amin, Jennifer Faber, Tamara Schaprian, Katharina Hill, Matthis Synofzik, Lisa H. Graf, Heike Jacobi, Manuela Lima, Ana F. Ferreira, Bart P. van de Warrenburg, Ilse H. J. Willemse, Dagmar Timmann, Andreas Thieme, Paola Giunti, Hector Garcia‐Moreno, Magda M. Santana, Jeannette Hübener‐Schmid, Elin H. Davies, Thomas Klockgether   +20 more
wiley   +1 more source

Association of Machine Learning–Based Assessment of Tumor-Infiltrating Lymphocytes on Standard Histologic Images With Outcomes of Immunotherapy in Patients With NSCLC

, 2022
Mehrdad Rakaee, Elio Adib, Biagio Ricciuti, Lynette M. Sholl, Weiwei Shi, Joao V. Alessi, Alessio Cortellini, Claudia Angela Maria Fulgenzi, Patrizia Viola, David J. Pinato, Sayed M.S. Hashemi, Idris Bahce, Ilias Houda, Ezgi B Ulas, Teodora Radonic, Juha P. Väyrynen, Elin Richardsen, Simin Jamaly, Sigve Andersen, Tom Dønnem, Mark M. Awad, David J. Kwiatkowski   +21 more
openalex   +2 more sources

Clustering Algorithm Reveals Dopamine‐Motor Mismatch in Cognitively Preserved Parkinson's Disease

Annals of Clinical and Translational Neurology, EarlyView.
ABSTRACT Objective To explore the relationship between dopaminergic denervation and motor impairment in two de novo Parkinson's disease (PD) cohorts. Methods n = 249 PD patients from Parkinson's Progression Markers Initiative (PPMI) and n = 84 from an external clinical cohort.
Rachele Malito, Chiara Meneghini, Alice Galli, Luca Gallo, Pierfrancesco Mitrotti, Paola Dimartino, Marianna Inglese, Cinzia Zatti, Alessandro Lupini, Andrea Pilotto, Alessandro Padovani, Micol Avenali, Enza Maria Valente, Cristina Tassorelli, Silvia P. Caminiti   +14 more
wiley   +1 more source

Analyzing the Performance of Machine Learning Techniques in Disease Prediction [PDF]

, 2022
Khongdet Phasinam, Tamal Mondal, Dony Novaliendry, Cheng‐Hong Yang, Chiranjit Dutta, Mohammad Shabaz   +5 more
openalex   +1 more source

A Scoping Review on Artificial Intelligence–Supported Interventions for Nonpharmacologic Management of Chronic Rheumatic Diseases

Arthritis Care &Research, EarlyView.
This review summarizes artificial intelligence (AI)‐supported nonpharmacological interventions for adults with chronic rheumatic diseases, detailing their components, purpose, and current evidence base. We searched Embase, PubMed, Cochrane, and Scopus databases for studies describing AI‐supported interventions for adults with chronic rheumatic diseases.
Nirali Shah, Alexis Castellanos, Yen Chen, John Piette, Amy Bucher, Susan Murphy   +5 more
wiley   +1 more source

Near-real-time detection of co-seismic ionospheric disturbances using machine learning [PDF]

, 2022
Quentin Brissaud, Elvira Astafyeva
openalex   +1 more source