Results 81 to 90 of about 2,307,608 (312)
Deep active learning for classifying cancer pathology reports
BMC Bioinformatics, 2021 Background Automated text classification has many important applications in the clinical setting; however, obtaining labelled data for training machine learning and deep learning models is often difficult and expensive.
Kevin De Angeli +11 more
doaj +1 more source
Life, 2022 This study was a multicenter retrospective cohort study of term nulliparous women who underwent labor, and was conducted to develop an automated machine learning model for prediction of emergent cesarean section (CS) before onset of labor.
Jeong Ha Wie +15 more
doaj +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
Rapid screening of staphylokinase protein variants using an unpurified cell‐free expression system
FEBS Open Bio, EarlyView.An unpurified cell‐free protein synthesis (CFPS) platform enables rapid functional screening of staphylokinase variants. Direct plasminogen‐activation assays performed in microplate format provide real‐time activity readouts, allowing rapid identification and ranking of variants with improved or reduced fibrinolytic activity without protein ...
Maria Tomková +3 more
wiley +1 more source
Prediction and optimization of epoxy adhesive strength from a small dataset through active learning
Science and Technology of Advanced Materials, 2019 Machine learning is emerging as a powerful tool for the discovery of novel high-performance functional materials. However, experimental datasets in the polymer-science field are typically limited and they are expensive to build. Their size (< 100 samples)
Sirawit Pruksawan +4 more
doaj +1 more source
Perspective: Predicting and optimizing thermal transport properties with machine learning methods
Energy and AI, 2022 In recent years, (big) data science has emerged as the “fourth paradigm” in physical science research. Data-driven techniques, e.g. machine learning, are advantageous in dealing with problems of high-dimensional features and complex mappings between ...
Han Wei, Hua Bao, Xiulin Ruan
doaj +1 more source
A surrogate modeling and adaptive sampling toolbox for computer based design [PDF]
, 2010 An exceedingly large number of scientific and engineering fields are confronted with the need for computer simulations to study complex, real world phenomena or solve challenging design problems.
Couckuyt, Ivo +4 more
core
Multi-Target Prediction: A Unifying View on Problems and Methods
, 2018 Multi-target prediction (MTP) is concerned with the simultaneous prediction of multiple target variables of diverse type. Due to its enormous application potential, it has developed into an active and rapidly expanding research field that combines ...
A Ben-Hur +61 more
core +1 more source
FEBS Open Bio, EarlyView.Activation of the mitochondrial protein OXR1 increases pSyn129 αSynuclein aggregation by lowering ATP levels and altering mitochondrial membrane potential, particularly in response to MSA‐derived fibrils. In contrast, ablation of the ER protein EMC4 enhances autophagic flux and lysosomal clearance, broadly reducing α‐synuclein aggregates.
Sandesh Neupane +11 more
wiley +1 more source
Employing active learning in the optimization of culture medium for mammalian cells
npj Systems Biology and Applications, 2023 Medium optimization is a crucial step during cell culture for biopharmaceutics and regenerative medicine; however, this step remains challenging, as both media and cells are highly complex systems.
Takamasa Hashizume +2 more
doaj +1 more source