Results 71 to 80 of about 1,915,617 (250)
Investigating transcription factor dynamics in health and disease using FRAP
FEBS Letters, EarlyView.FRAP analysis of GFP‐tagged transcription factors reveals how molecular mobility and target engagement change in response to drug treatment. By combining live‐cell imaging, quantitative model fitting, and statistical analysis, this approach uncovers transcription factor dynamics linked to disease mechanisms, providing a powerful framework for ...
Kannan Govindaraj +3 more
wiley +1 more source
Structure‐forward targeting of claudins with synthetic binders
FEBS Letters, EarlyView.Claudins form the paracellular barriers between epithelial and endothelial tissues at tight junctions and are targets for molecular binders with the goal of modulating barrier permeability. Claudin‐binding molecules are relevant in drug delivery or in altering claudin interactions with disease‐causing proteins.
Alex J. Vecchio
wiley +1 more source
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 +6 more
core +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 +4 more
doaj
FEBS Letters, EarlyView.Animals must match their growth rate to available nutrients. We show that in Drosophila larvae, the nutrient‐sensing TOR kinase controls growth by regulating levels of TFAM, a key regulator of mitochondrial function, in the adipose tissue. When nutrients are abundant, high TOR activity suppresses TFAM, lowering mitochondrial bioenergetic activity and ...
Shrivani Sriskanthadevan‐Pirahas +4 more
wiley +1 more source
Review of Causal Discovery Methods Based on Graphical Models
Frontiers in Genetics, 2019 A fundamental task in various disciplines of science, including biology, is to find underlying causal relations and make use of them. Causal relations can be seen if interventions are properly applied; however, in many cases they are difficult or even ...
Clark Glymour, Kun Zhang, Peter Spirtes
doaj +1 more source
Enhancing Causal Discovery from Robot Sensor Data in Dynamic Scenarios
, 2023 Identifying the main features and learning the causal relationships of a dynamic system from time-series of sensor data are key problems in many real-world robot applications.
N. Bellotto +7 more
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Engineering peptides into antibodies—opportunities and strategies for therapeutic innovation
FEBS Letters, EarlyView.Peptides and antibodies occupy complementary therapeutic niches. Peptides recognize difficult targets in a compact format, while antibodies add specificity, long half‐life, and effector functions. This review examines strategies that merge both modalities—peptide grafting into loops, terminal and Fc fusions, and bioconjugation—highlighting how ...
Jinling Wang +2 more
wiley +1 more source
BMC CancerCervical 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 +3 more
doaj +1 more source
Causal Modelling Agents: Causal Graph Discovery through Synergising Metadata- and Data-driven Reasoning [PDF]
Scientific discovery hinges on the effective integration of metadata, which refers to a set of conceptual operations such as determining what information is relevant for inquiry, and data, which encompasses physical operations such as observation and ...
Drobnjak, I +7 more
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