Results 221 to 230 of about 201,162 (313)

Gallstone disease classification using SLOA-optimized CatBoost classifier with explainable AI. [PDF]

PLoS One
Das P, Khan MAU, Sarker P, Nahid AA.
europepmc   +1 more source

The KIF6‐RBP Complex Orchestrates mRNA Transport Required for Sperm Flagellar Assembly

Advanced Science, EarlyView.
Two homozygous deleterious KIF6 variants are identified in unrelated men with impaired sperm motility. Mouse models and multi‐omics analyses reveal that KIF6 cooperates with the RNA‐binding proteins FMRP and FXR1 to deliver mRNAs essential for sperm flagellar assembly, linking disrupted mRNA transport to reduced abundance of key structural and ...
Chunbo Xie, Sibing Yi, Xinle Lin, Shen Zhang, Weili Wang, Shimin Yuan, Lanlan Meng, Yong Li, Chen Tan, Chunjia Wei, Yanyan Yu, Yaoqiong Liang, Huan Zhang, Liang Hu, Guangxiu Lu, Wenbin He, Qianjun Zhang, Juan Du, Ge Lin, Chaofeng Tu, Yue‐Qiu Tan   +20 more
wiley   +1 more source

Single‐Cell Profiling Identifies SLC2A5‐Mediated Fructose Metabolism as a Vulnerability in Primary CNS Lymphoma

Advanced Science, EarlyView.
Glucose deprivation in the primary CNS lymphoma (PCNSL) tumor microenvironment drives SLC2A5 (encoding GLUT5)‐dependent fructose metabolism in tumor cells, while hypoxia induces HIF‐mediated SLC2A5 expression in tumor‐supportive macrophages, revealing SLC2A5‐driven fructose utilization as a shared and targetable metabolic vulnerability across malignant
Qiaoli Wu, Qianru Zhang, Wenqiang Yan, Lu Sun, Yiran Cui, Bolin Liu, Guoqing Han, Feng Zhang, Ke Pu, Mingchao Zhang, Xiaobing Zhang, Qingguo Li, Gang An, Yuxuan Liu   +13 more
wiley   +1 more source

Unified comparison of machine learning paradigms for blood transfusion prediction in pediatric congenital heart surgery. [PDF]

iScience
Yin MW, Li J, Huang J, Zhu Z, Chen BH, Shi Z, Jiang Q, Chen XJ, Yu G.   +8 more
europepmc   +1 more source

Molecular and Cellular Hallmarks of Age‐Related Vestibular Hair Cell Degeneration

Advanced Science, EarlyView.
This study utilizes single‐cell RNA‐seq transcriptomes, advanced imaging, and electrophysiology to examine universal and cell‐type‐specific aging signatures of vestibular hair cells. The study shows that impaired hair bundle function is a key driver of age‐related vestibular dysfunction.
Samadhi Kulasooriya, Huizhan Liu, Sarath Vijayakumar, Celia Bloom, Zhenhang Xu, Shu Tu, Benjamin J. Borgmeier, Mi Zhou, Litao Tao, Bechara Kachar, David Z. He   +10 more
wiley   +1 more source

Cross-domain transfer learning strategy enhances interpretability of deep learning model explanations. [PDF]

Sci Rep
Zannini M, Hammer A, Malberg H, Schmidt M.   +3 more
europepmc   +1 more source

ORBIT‐AMD: Ordinal Risk, Bilateral Imaging, and Trajectory Learning for Age‐Related Macular Degeneration in Multi‐Cohorts

Advanced Science, EarlyView.
Eligibility flow and real‐world AMD burden in the UKB retinal imaging cohort and TMUEH external‐validation cohort. Overview of the ORBIT‐AMD architecture, integrating retinal representation pretraining, bilateral eye‐graph modeling and concept bottleneck learning to support ordered risk, bilateral context, interpretable lesion concepts, longitudinal ...
Xuehao Cui, Dejia Wen, Patrick Yu‐Wai‐Man, Xiaorong Li   +3 more
wiley   +1 more source

Automated bone marrow cell classification using ensemble learning: performance, generalization, and clinical interpretability. [PDF]

Front Med (Lausanne)
Mehmood S, Zubair M, Abbas S, Alharbi RM, Alduailij M, Khan MA, Ghazal TM.   +6 more
europepmc   +1 more source

Error Propagation Impact on F1 Scores in Cross-Lingual NER Model Projection

Cross-lingual Named Entity Recognition (NER) leverages knowledge transfer between languages to identify and classify named entities, making it particularly useful for low-resource languages. We show that the data-based cross-lingual transfer method is an effective technique for crosslingual NER and can outperform multilingual language models for low ...
openaire   +1 more source

Prediction of Structural Stability of Layered Oxide Cathode Materials: Combination of Machine Learning and Ab Initio Thermodynamics

Advanced Energy Materials, EarlyView.
In this work, we developed a phase‐stability predictor by combining machine learning and ab initio thermodynamics approaches, and identified the key factors determining the favorable phase for a given composition. Specifically, a lower TM ionic potential, higher Na content, and higher mixing entropy favor the O3 phase.
Liang‐Ting Wu, Milica Zdravković, Dijana Milosavljević, Konstantin Köster, Olivier Guillon, Jyh‐Chiang Jiang, Payam Kaghazchi   +6 more
wiley   +1 more source