Results 111 to 120 of about 1,819,138 (329)

Structural insights and therapeutic targets in Acinetobacter baumannii capsule biosynthesis

FEBS Letters, EarlyView.
Hypervirulent KL49 A. baumannii's capsular polysaccharide contains the nonulosonic acid 8‐epi‐Leg5,7Ac2, synthesized by epimerization via ElaA, ElaB, and ElaC. Crystal structures of ElaA, ElaB, and ElaC reveal their role in CMP‐Leg5,7Ac2 synthesis and regioselective C8 epimerization.
Woo Cheol Lee, Junho Jeong, Chae Yeong Lee, Hyunjoon Cho, Man Hwan Oh, Je Chul Lee, Jee‐Young Lee, Yangmee Kim   +7 more
wiley   +1 more source

Causal feedback-based imputation causal discovery

四川大学学报. 自然科学版
Causal discovery is an important part of causal inference and the goal is to discover the data generation mechanism in the form of Directed Acyclic Graphs （DAGs）.
MA Cong-Li, HUANG Fei-Hu, YI Pei-Yu, WANG Lin-Na, PENG Jian   +4 more
doaj  

Three phosphatase families form a community: The phosphohydrolases that act upon inositol pyrophosphates

FEBS Letters, EarlyView.
Inositol pyrophosphates are energy‐rich signaling molecules that perform critical functions in cells. Three different families of phosphatases hydrolyze the β phosphate of the inositol pyrophosphate molecules: two have narrow specificities and one is promiscuous.
Ronda J. Rolfes
wiley   +1 more source

Large Causal Models for Temporal Causal Discovery

CoRR
Causal discovery for both cross-sectional and temporal data has traditionally followed a dataset-specific paradigm, where a new model is fitted for each individual dataset. Such an approach limits the potential of multi-dataset pretraining. The concept of large causal models (LCMs) envisions a class of pre-trained neural architectures specifically ...
Nikolaos Kougioulis, Nikolaos Gkorgkolis, MingXue Wang, Bora Caglayan, Dario Simionato, Andrea Tonon, Ioannis Tsamardinos   +6 more
openaire   +2 more sources

Modelling stem cell differentiation related processes—A practical overview for biologists

FEBS Letters, EarlyView.
Stem cell differentiation is complex and difficult to control experimentally. This review introduces suitable computational modelling approaches that can support stem cell research, from mechanistic ODE and abstract models to multiscale and deep learning methods.
Ricco Zeegelaar, Xinqi Yan, Lucas Jansen Klomp, Stefano Schivo, Janine N. Post   +4 more
wiley   +1 more source

Enhanced Causal Discovery for Autocorrelated Time Series via Adaptive Momentary Conditional Independence

Mathematics
Discovering causal relationships from time series data is essential for understanding complex dynamical systems across a range of domains. However, strong autocorrelation often limits the detection power of existing algorithms and increases the risk of ...
Minglong Gao, Yingchun Zhou
doaj   +1 more source

Design and analysis strategies for robust microbiome ageing research

FEBS Letters, EarlyView.
The gut microbiome changes with age and associates with age‐related morbidity and mortality, establishing it as a potential biomarker and intervention target for ageing. Realising this potential requires methodological rigour, yet distinguishing biological signals from methodological artefacts remains challenging across cohorts. This review provides an
Mark Olenik, İsmail Güderer, Yi Wang, Tayyaba Alvi, Prasoon Pandey, Handan Melike Dönertaş   +5 more
wiley   +1 more source

Ordering-Based Causal Discovery with Reinforcement Learning [PDF]

, 2021
It is a long-standing question to discover causal relations among a set of variables in many empirical sciences. Recently, Reinforcement Learning (RL) has achieved promising results in causal discovery from observational data.
Wang, X, Du, Y, Du, Yali, Chen, Z, Wang, Xiaoqiang, Hao, J, Ke, Liangjun, Hao, Jianye, Wang, Jun, Ke, L, Chen, Zhitang, Wang, J, Zhu, S, Zhu, Shengyu   +13 more
core  

Causal Discovery from Conditionally Stationary Time Series

Causal discovery, i.e., inferring underlying causal relationships from observational data, is highly challenging for AI systems. In a time series modeling context, traditional causal discovery methods mainly consider constrained scenarios with fully ...
Schweikert, Gabriele, Kjellström, Hedvig, Narendra, Tanmayee; id_orcid, Li, Yingzhen, Balsells-Rodas, Carles, Tu, Ruibo, Sumba, Xavier   +6 more
core   +1 more source

CausalCervixNet: convolutional neural networks with causal insight (CICNN) in cervical cancer cell classification—leveraging deep learning models for enhanced diagnostic accuracy

BMC Cancer
Cervical cancer is a significant global health issue affecting women worldwide, necessitating prompt detection and effective management. According to the World Health Organization (WHO), approximately 660,000 new cases of cervical cancer and 350,000 ...
Zahra Taghados, Zohreh Azimifar, Malihezaman Monsefi, Mojgan Akbarzadeh Jahromi   +3 more
doaj   +1 more source