Results 101 to 110 of about 2,170,661 (326)

Comparison of sparse biclustering algorithms for gene expression datasets

bioRxiv, 2020
Gene clustering and sample clustering are commonly used to find patterns in gene expression datasets. However, in heterogeneous samples (e.g. different tissues or disease states), genes may cluster differently.
Kath Nicholls, C. Wallace
semanticscholar   +1 more source

Microglial dynamics and ferroptosis induction in human iPSC‐derived neuron–astrocyte–microglia tri‐cultures

FEBS Open Bio, EarlyView.
A tri‐culture of iPSC‐derived neurons, astrocytes, and microglia treated with ferroptosis inducers as an Induced ferroptosis model was characterized by scRNA‐seq, cell survival, and cytokine release assays. This analysis revealed diverse microglial transcriptomic changes, indicating that the system captures key aspects of the complex cellular ...
Hongmei Lisa Li, Hiroko Ohmiya, Sou Sakamoto, Masato Yugami, Akiko Oki, Makoto Furusawa, Yan Ling   +6 more
wiley   +1 more source

edge2vec: Representation learning using edge semantics for biomedical knowledge discovery

, 2019
Representation learning provides new and powerful graph analytical approaches and tools for the highly valued data science challenge of mining knowledge graphs.
Ding, Ying, Foote, Brian, Fu, Gang, Gao, Zheng, Gessner, Christopher, Liu, Xiaozhong, Ouyang, Chunping, Tsutsui, Satoshi, Wild, David, Yang, Jeremy, Yu, Qi   +10 more
core   +1 more source

Evaluation of Appropriate Reference Genes for Reverse Transcription-Quantitative PCR Studies in Different Tissues of a Desert Poplar via Comparision of Different Algorithms [PDF]

International Journal of Molecular Sciences, 2015
Despite the unshakable status of reverse transcription-quantitative PCR in gene expression analysis, it has certain disadvantages, including that the results are highly dependent on the reference genes selected for data normalization. Since inappropriate endogenous control genes will lead to inaccurate target gene expression profiles, the validation of
Hou-Ling Wang, Lan Li, Sha Tang, Chao Yuan, Qianqian Tian, Yanyan Su, Hui-Guang Li, Lin Zhao, Weilun Yin, Rui Zhao, Xinli Xia   +10 more
openaire   +2 more sources

Multi‐omics and low‐input proteomics profiling reveals dynamic regulation driving pluripotency initiation in early mouse embryos

FEBS Open Bio, EarlyView.
Mouse pre‐implantation development involves a transition from totipotency to pluripotency. Integrating transcriptomics, epigenetic profiling, low‐input proteomics and functional assays, we show that eight‐cell embryos retain residual totipotency features, whereas cytoskeletal remodeling regulated by the ubiquitin‐proteasome system drives progression ...
Wanqiong Li, Xi Xiao, Lingbin Qi, Chanyi Li, Sirui Song, Jiaying Qin, Zhigang Xue, Jinfeng Xue, Huaifang Li   +8 more
wiley   +1 more source

Single‐molecule DNA flow‐stretch assays for high‐throughput DNA–protein interaction studies

FEBS Open Bio, EarlyView.
We describe an optimised single‐molecule DNA flow‐stretch assay that visualises DNA–protein interactions in real time. Linear DNA fragments are tethered to a surface and stretched by buffer flow for fluorescence imaging. Using λ and φX174 DNA, this protocol enhances reproducibility and accessibility, providing a versatile approach for studying diverse ...
Ayush Kumar Ganguli, Mohammad Nour Alsamsam, Ugnė Bagdonaitė, Van Truc Vu, Chun‐Jen Huang, Polina Kuzhir, Mindaugas Zaremba, Aurimas Kopūstas, Marijonas Tutkus   +8 more
wiley   +1 more source

MM Algorithms for Minimizing Nonsmoothly Penalized Objective Functions

, 2011
In this paper, we propose a general class of algorithms for optimizing an extensive variety of nonsmoothly penalized objective functions that satisfy certain regularity conditions.
Schifano, Elizabeth D., Strawderman, Robert L., Wells, Martin T.   +2 more
core   +1 more source

Performance Evaluation of Hybrid Bio-Inspired and Deep Learning Algorithms in Gene Selection and Cancer Classification

IEEE Access
Cancer classification based on gene expression data is a critical challenge in modern bioinformatics, requiring efficient and accurate feature selection methods. This study explores the performance of hybrid bio-inspired algorithms and deep learning techniques for gene selection and cancer classification.
Shahad S. Alkamli, Hala M. Alshamlan
openaire   +2 more sources

Exon 7 splicing of ERα predicts poor prognosis and increases phenotypic heterogeneity in luminal a subtype breast cancer

FEBS Open Bio, EarlyView.
ERα splice variant ERα∆7 lacks the C‐terminus, and its expression may change phenotypes of breast cancers. Our results showed that ERα∆7 is found in the luminal A subtype, and elevated ERα∆7 levels are linked to improved cell survival with lower proliferation and migration.
Long Wai Tsui, Taobo Hu, Thomson Christian Husein, Wai Hei Shek, Bingrong Chen, Depeng Wang, Shu Wang, Weichuan Yu, Xiaodan Fan, Mengping Long, Toyotaka Ishibashi   +10 more
wiley   +1 more source

Digital twins to accelerate target identification and drug development for immune‐mediated disorders

FEBS Open Bio, EarlyView.
Digital twins integrate patient‐derived molecular and clinical data into personalised computational models that simulate disease mechanisms. They enable rapid identification and validation of therapeutic targets, prediction of drug responses, and prioritisation of candidate interventions.
Anna Niarakis, Philippe Moingeon
wiley   +1 more source