Results 161 to 170 of about 131,123 (260)

Pasta, a Versatile Transcriptomic Clock, Maps the Chemical and Genetic Determinants of Aging and Rejuvenation

Advanced Science, EarlyView.
Pasta is a transcriptomic aging clock built on an age‐shift learning framework and trained on 17 000 samples across 21 datasets. It accurately predicts relative biological age across tissues, platforms, and species, captures stemness‐to‐senescence transitions, and identifies age‐modulatory perturbations.
Jérôme Salignon, Maria Tsiokou, Patricia Marqués, Enriqueta Rodríguez‐Diaz, Hazel Ang, Federico Pietrocola, Christian G. Riedel   +6 more
wiley   +1 more source

MVIAeval: a web tool for comprehensively evaluating the performance of a new missing value imputation algorithm. [PDF]

BMC Bioinformatics, 2017
Wu WS, Jhou MJ.
europepmc   +1 more source

The Natural Product Corramycin Acts as a DNA Gyrase Poison and Overcomes Fluoroquinolone Resistance in Mycobacterium tuberculosis

Advanced Science, EarlyView.
Corramycin, a myxobacterial natural product antibiotic, exhibits potent bactericidal activity against multidrug‐resistant Mycobacterium tuberculosis. The compound hijacks bacterial transporters such as BacA and OppABCD to enter the cell and inhibits DNA synthesis through a previously unrecognized mode of DNA gyrase poisoning, locking the enzyme in an ...
Franziska Fries, Jennifer Herrmann, Michael Dal Molin, Yaëlle Wormser, Timo Risch, Yunsheng Gao, Gareth Prosser, Francesca Gubellini, Ariel Mechaly, Joshua B. Wallach, Anthony Castro, Arka Banerjee, Sari Rasheed, Alexander Horn, Mathias Müsken, Michael Kurz, Lindsay Sonnenkalb, Viola Dreyer, Stefan Niemann, Sönke Andres, Dirk Schnappinger, Uli Kazmaier, Norbert Reiling, Rolf Müller, Jan Rybniker, Stéphanie Petrella   +25 more
wiley   +1 more source

Performance Evaluation of Missing-Value Imputation Clustering Based on a Multivariate Gaussian Mixture Model. [PDF]

PLoS One, 2016
Xiao J, Xu Q, Wu C, Gao Y, Hua T, Xu C.
europepmc   +1 more source

Biochemically Constrained Multi‐Omics Integration Reveals Protein–Metabolite Dependencies Across Diseases

Advanced Science, EarlyView.
ProMetNet introduces a biologically constrained deep learning framework for proteo‐metabolomic integration by embedding Reactome‐derived pathway topology into neural networks. It captures non‐linear molecular dependencies and pathway‐level metabolic reorganization, enabling interpretable discrimination.
Minghui Zhao, Na Zhou, Ruotong Liu, Xiaofei Li, Jian Li, Fuzhong Xue, Qingzhen Hou   +6 more
wiley   +1 more source

Correction to: MetImputBERT: a pretrained BERT framework for missing value imputation in NMR metabolomics data. [PDF]

Brief Bioinform
europepmc   +1 more source

Missing value imputation in high-dimensional phenomic data: imputable or not, and how? [PDF]

BMC Bioinformatics, 2014
Liao SG, Lin Y, Kang DD, Chandra D, Bon J, Kaminski N, Sciurba FC, Tseng GC.   +7 more
europepmc   +1 more source

BraMARS: An Interpretable Histopathology‐Driven Deep Learning Model for Brain Metastasis Risk Stratification in Surgically Resected Limited‐Stage SCLC

Advanced Science, EarlyView.
This study presents BraMARS, an explainable deep learning model that estimates future brain metastasis risk in surgically resected limited‐stage small‐cell lung cancer using routine H&E‐stained whole‐slide images. By linking model‐attributed spatial histopathology with clinical outcomes and proteomic programs, BraMARS provides a biologically ...
Zijian Yang, Taolue Wang, Shilong Liu, Zicheng Zhang, Yibo Zhang, Fan Yang, Bo Yu, Shuaishuai Gao, Yu Chen, Lin Yang, Meng Zhou   +10 more
wiley   +1 more source

Missing value imputation for microRNA expression data by using a GO-based similarity measure. [PDF]

BMC Bioinformatics, 2016
Yang Y, Xu Z, Song D.
europepmc   +1 more source

Livestock Multi‐Omics Integration: A Systematic Framework From Statistical Association to Causal Interpretation

Advanced Science, EarlyView.
A three‐tier livestock multi‐omics framework resolves four typical analytical pitfalls. Moving from statistical association through machine learning preprocessing to triple‐modal causal inference, it converts omics results into genomic selection and gene editing strategies to achieve One Health, underpinned by multi‐omics data, multimodal sequencing ...
Jiying Wen, Zhongyu Wang, Jieping Huang, Fen Li, Ningbo Chen, Yun Ma   +5 more
wiley   +1 more source