Results 91 to 100 of about 4,635,657 (309)
Digital twins to accelerate target identification and drug development for immune‐mediated disorders
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
The Intuitive Appeal of Explainable Machines [PDF]
Algorithmic decision-making has become synonymous with inexplicable decision-making, but what makes algorithms so difficult to explain? This Article examines what sets machine learning apart from other ways of developing rules for decision-making and the
Barocas, Solon, Selbst, Andrew D.
core +1 more source
Mass spectrometry based identification of AMP‐O‐Tris generated by Thermococcus onnurineus Cas10
Isolated Thermococcus onnurineus Cas10 generates the noncanonical ATP‐derived product AMP‐O‐Tris while in Tris‐containing buffer as identified via mass spectrometry, revealing relaxed nucleophile selectivity under isolated conditions. These findings suggest that multiprotein Csm complex assembly restricts Cas10 reactivity toward canonical cyclic ...
Su‐Jin Lee +6 more
wiley +1 more source
Pharmacological inhibition of PERK in a DEN‐induced mouse model of liver cancer does not reduce tumor burden but alters cellular stress signaling. Despite blocking PERK activity, downstream stress responses, including CHOP expression, remain active, suggesting compensatory mechanisms within the unfolded protein response that may influence tumor ...
Ada Lerma‐Clavero +5 more
wiley +1 more source
Depression is a common mental health disorder and a leading contributor to mortality and morbidity. Despite several advancements, the current screening methods have limitations in enabling the robust and automated detection of depression, thereby ...
Doljinsuren Enkhbayar +6 more
doaj +1 more source
A Scoresheet for Explainable AI
Explainability is important for the transparency of autonomous and intelligent systems and for helping to support the development of appropriate levels of trust. There has been considerable work on developing approaches for explaining systems and there are standards that specify requirements for transparency.
Winikoff, Michael +2 more
openaire +3 more sources
RoundMi: A quantitative method to analyze mitochondrial morphology in mitotic cells
RoundMi is a workflow for rapid analysis of mitochondrial morphology in mitotic cells. By combining adaptive preprocessing with automated segmentation and quantification, it enables accurate measurements from single focal plane images, reducing acquisition time and computational demands while remaining compatible with high‐throughput fixed and live ...
Elmira Parvindokht Bararpour +2 more
wiley +1 more source
UiO‐66(Zr) metal–organic frameworks are chemically stable, biocompatible, and highly tunable nanomaterials. Their modular structure enables controlled drug delivery, multimodal bioimaging, and light‐activated photodynamic therapy, supporting integrated diagnostic and therapeutic (theranostic) applications in cancer and biomedical research.
Veronika Huntošová +2 more
wiley +1 more source
Directed evolution of enzymes at the crossroads of tradition and innovation
An iterative cycle of data‐driven enzyme optimization comprising four stages: genetic diversification of a template enzyme, expression of protein variants, high‐throughput evaluation, and machine‐learning‐guided redesign of the next variant library.
Maria Tomkova +2 more
wiley +1 more source
Natural products target the aging kidney in diabetic nephropathy by restoring the AMPK–SIRT1–Nrf2 axis, reducing oxidative stress, inflammation, fibrosis, and cellular senescence while enhancing mitochondrial biogenesis and antioxidant defenses.
Sherif Hamidu +8 more
wiley +1 more source

