Results 51 to 60 of about 6,531,013 (293)
Reconstructing enzyme evolution by protein engineering
FEBS Letters, EarlyView.Natural enzyme evolution can be retraced by protein engineering methods such as directed evolution, rational design, and ancestral sequence reconstruction. These approaches reveal how enzymes emerged from ligand‐binding scaffolds, developed varying substrate preferences, formed oligomeric complexes, adapted to environmental changes, and evolved novel ...
Lukas Drexler +2 more
wiley +1 more source
Reinforcement Learning Configuration Interaction [PDF]
, 2021 A reinforcement learning algorithm is developed for the selected configuration interaction problem. We explore how reinforcement learning can obtain compact wave functions at near full configuration interaction ...
Joshua, Goings +3 more
core +1 more source
Parallel model-based and model-free reinforcement learning for card sorting performance
Scientific Reports, 2020 The Wisconsin Card Sorting Test (WCST) is considered a gold standard for the assessment of cognitive flexibility. On the WCST, repeating a sorting category following negative feedback is typically treated as indicating reduced cognitive flexibility ...
Alexander Steinke +2 more
doaj +1 more source
Molecular Oncology, EarlyView.Single‐cell multi‐omics reveals epigenetic heterogeneity across therapy‐adaptive tumor states, including quiescent/dormant, drug‐tolerant persister, and EMT‐like phenotypes. By linking regulatory features with state‐associated biomarkers, these approaches inform biomarker‐guided therapeutic strategies for evolving tumors.
Hee Jung Kim +3 more
wiley +1 more source
Real-World Reinforcement Learning
, 2021 This is our first public release of real-world reinforcement learning, a document with associated code showing how to design and deploy reinforcement learning solutions in customer-facing applications.If you use this software, please cite it as ...
Mason, Douglas
core +1 more source
Molecular Oncology, EarlyView.Single‐cell DNA methylation (scDNAme) profiling maps epimutational clonal evolution, revealing mechanisms of malignancy and therapeutic resistance across diverse cancer types. By providing a high‐resolution landscape of intratumoral heterogeneity, these technologies empower precise patient stratification, guide the development of enhanced ...
Ik Soo Kim
wiley +1 more source
Risk-Sensitive Reinforcement Learning [PDF]
Neural Computation, 2014 We derive a family of risk-sensitive reinforcement learning methods for agents, who face sequential decision-making tasks in uncertain environments. By applying a utility function to the temporal difference (TD) error, nonlinear transformations are effectively applied not only to the received rewards but also to the true transition probabilities of ...
Yun Shen +3 more
openaire +3 more sources
FEBS Open Bio, EarlyView.The cytoskeleton‐mediated transport of mitochondria via tunnelling nanotubes restores respiration, increases ATP production, rescues cells from apoptosis, activates the AKT/mTOR signalling pathway, promotes cell migration and invasiveness, contributes to cancer progression and treatment resistance.
Stanislava Martínková, Jan Trnka
wiley +1 more source
FEBS Open Bio, EarlyView.Research is strongest when conducted alongside patients, not just about them. Patient research organizations help integrate patient perspectives into research priorities, study design, and scientific meetings, leading to meaningful patient outcomes and development of relevant therapies.
Jenica H. Kakadia +9 more
wiley +1 more source
Imitation Learning by Reinforcement Learning
CoRR, 2021 Imitation learning algorithms learn a policy from demonstrations of expert behavior. We show that, for deterministic experts, imitation learning can be done by reduction to reinforcement learning with a stationary reward. Our theoretical analysis both certifies the recovery of expert reward and bounds the total variation distance between the expert and
openaire +3 more sources