GraphTME: graph-based framework for predicting immunotherapy response by interpreting tumour microenvironment interactions using spatial transcriptomics [0.03%]
基于空间转录组学解释肿瘤微环境相互作用的预测免疫治疗反应的图框架(GraphTME)
Hoyeon Jeong,Junghan Oh,Yoon-La Choi
Hoyeon Jeong
Immune checkpoint inhibitors (ICIs), which reactivate T-cell responses against tumours, show limited clinical efficacy due to low response rates and the lack of robust predictive biomarkers. Cell-cell interactions within the tumour microenv...
Mapping gene expression dynamics to developmental phenotypes with information entropy analysis [0.03%]
基于信息熵分析的基因表达动力学及其发育表型之间的关系映射研究
Ben Ansbacher,Malachy Guzman,Jordi Garcia-Ojalvo et al.
Ben Ansbacher et al.
The development of multicellular organisms entails a deep connection between time-dependent biochemical processes taking place at the subcellular level and the resulting macroscopic phenotypes that arise in populations of up to trillions of...
A novel approach to quantify out-of-distribution uncertainty in Neural and Universal Differential Equations [0.03%]
一种量化神经和万能微分方程中OutOfDistribution不确定性的新方法
Stefano Giampiccolo,Giovanni Iacca,Luca Marchetti
Stefano Giampiccolo
Dynamical systems play a central role across the quantitative sciences, offering a powerful mathematical framework to describe, analyze, and predict the evolution of complex processes over time. In systems biology, dynamical systems provide...
Untargeted metabolomics reveals organism specific biomarkers of carbapenem resistance in Klebsiella pneumoniae and Escherichia coli [0.03%]
未靶向代谢组学揭示了卡巴_pen_耐药肺炎克雷伯氏菌和大肠杆菌的物种特异性生物标志物
Nicholas Bartelo,Yaxin Li,Ying Hao et al.
Nicholas Bartelo et al.
Antimicrobial resistance (AMR) is one of the greatest global concerns due to the increase in the rate of AMR infections and the lack of development of antimicrobial agents to combat AMR. The development of resistance to carbapenems among co...
Decoding cellular population dynamics through mechanistic modelling and statistical data analysis [0.03%]
通过机制模型和统计数据分析解码细胞种群动力学
Nissrin Alachkar,Nicholas Kwasi-Do Ohene Opoku,Nicholas A M Monk et al.
Nissrin Alachkar et al.
Cell-cell communication underlies key processes in development, immunity, and disease, yet capturing its mechanistic complexity remains challenging. While advances in single-cell omics have revealed new insights into cell-type diversity, ma...
Dewei Hu,Anna-Lisa Schaap-Johansen,Julia Villarroel et al.
Dewei Hu et al.
Identifying disease-relevant proteins and pathways remains a fundamental challenge in understanding disease mechanisms and supporting therapeutic development. While omics analyses can provide valuable insights, they typically consider each ...
Cell trajectory inference based on schrödinger problem and a mechanistic model of stochastic gene expression [0.03%]
基于薛定谔问题和随机基因表达机制模型的细胞轨迹推理
Clémence Fournié,Elias Ventre,Ulysse Herbach et al.
Clémence Fournié et al.
Cellular differentiation is the biological process that leads a cell to opt for a particular cellular identity. Recently, single-cell RNA sequencing has enabled the simultaneous measurement of gene expression levels at specific times for a ...
Gene overexpression reduces inhibitory metabolites to enhance CHO cell growth and IgG1 production [0.03%]
基因过表达减少抑制型代谢物可提高中国仓鼠卵巢细胞的生长和IgG1产量
Duc Hoang,Bingyu Kuang,Zhao Wang et al.
Duc Hoang et al.
Controlling the generation of toxic by-products in mammalian bioprocess to maximize therapeutic protein production and glycosylation patterns is a challenge. Intracellular metabolism is often not well-regulated and known to secrete toxic in...
Interpretable multitask model for clinical pathology image prediction and interpretation [0.03%]
临床病理图像预测和解释的可解释多任务模型
Qitao Chen,Zhe Wang,Xia Lin et al.
Qitao Chen et al.
Deep learning (DL)-based pathological image modelling and analysis approaches offer transformative potential for early cancer diagnostics, yet limited sample sizes and a lack of interpretability often hinder efficient clinical translation. ...
Supervised machine learning identifies impaired mitochondrial quality control in β-cells with development of type 2 diabetes [0.03%]
监督型机器学习识别出线粒体质量控制受损是2型糖尿病发病的一个因素
Mirza Muhammad Fahd Qadir,Charles Dana,Madeleine Pittigher et al.
Mirza Muhammad Fahd Qadir et al.
Defining molecular pathways driving β-cell failure in type 2 diabetes (T2D) is challenging given donor heterogeneity. We developed an interpretable machine learning framework coupling sparse rule-based classification, pathway constrained m...