Results 111 to 120 of about 22,635,901 (285)

Genetic dissection of human ABCE1 in yeast reveals separable requirements for ribosome recycling and suppression of aberrant reinitiation

FEBS Open Bio, EarlyView.
Human ABCE1 cannot functionally replace its yeast ortholog. Yeast–human chimera analysis identified NBD1 as a major interspecies barrier. Genetic screening yielded hABCE1 revertants that rescue yeast viability but fail to suppress aberrant translation reinitiation in the 3′ UTR.
Eriko Nakata, Yuxin Li, Kei Endo, Koichi Ito   +3 more
wiley   +1 more source

Hyperbolic Large Language Models

CoRR
Large language models (LLMs) have achieved remarkable success and demonstrated superior performance across various tasks, including natural language processing (NLP), weather forecasting, biological protein folding, text generation, and solving mathematical problems. However, many real-world data exhibit highly non-Euclidean latent hierarchical anatomy,
Sarang Patil, Zeyong Zhang, Yiran Huang, Tengfei Ma, Mengjia Xu   +4 more
openaire   +2 more sources

Retrieval-Augmented Generation with Large Language Models for Genetic Counseling on Rare Diseases and Mutations [PDF]

openLo scopo di questa tesi consiste nello sviluppo di un sistema per agilizzare la ricerca di informazioni riguardanti malattie e mutazioni genetiche rare per offrire un aiuto ai ricercatori presso R&I Genetics.
DA RE, LEONARDO
core  

Evaluating GenAI‐produced feedback on undergraduate bioscience essays against good higher education feedback practice

FEBS Open Bio, EarlyView.
This pilot study investigates the potential of Generative AI to provide formative feedback to students. ChatGPT was prompted to provide feedback on Year 1 Bioscience essays, which were evaluated against established good feedback practices. GenAI‐authored feedback had useful elements, but was limited in scope. GenAI may have potential to provide instant,
Annabel Court, Nigel Francis, Andrew Shore, Stephen Rutherford   +3 more
wiley   +1 more source

Entropy in Large Language Models

CoRR
In this study, the output of large language models (LLM) is considered an information source generating an unlimited sequence of symbols drawn from a finite alphabet. Given the probabilistic nature of modern LLMs, we assume a probabilistic model for these LLMs, following a constant random distribution and the source itself thus being stationary.
openaire   +3 more sources

Large Language Models, human language and acquisition: a comparison.

openLa tesi ha l’obiettivo di illustrare un generale confronto fra l’apprendimento linguistico dei Large Language Models e l’apprendimento umano. Il primo capitolo si sofferma sui fondamenti teorici principali riguardanti i grandi modelli di linguaggio ...
FORCATO, ALESSIA
core  

Understanding and Mitigating the Risk of Accelerated Biological Aging in Survivors of Cancer: A Scoping Review

Aging and Cancer, EarlyView.
Cancer treatment is associated with measurable acceleration of biological aging across epigenetic, telomere, senescence, and immune biomarkers. However, biomarker validation and interventional strategies remain limited, especially in hematologic malignancies, underscoring the need for standardized multi‐omic aging assessments and adequately powered ...
Moataz Ellithi, Emily Herdman, Danielle Westmark, Vijaya R. Bhatt   +3 more
wiley   +1 more source

Applications of Large Language Models in Document Analysis and Automation

reservedThis thesis explores the application of Large Language Models (LLMs), such as GPT and Gem- ini, to automate the transcription of medical reports.
MAZZA, DAVIDE
core  

Evaluating Large Language Models’ Ability Using a Psychiatric Screening Tool Based on Metaphor and Sarcasm Scenarios

Journal of Intelligence
Metaphors and sarcasm are precious fruits of our highly evolved social communication skills. However, children with the condition then known as Asperger syndrome are known to have difficulties in comprehending sarcasm, even if they possess adequate ...
Hiromu Yakura
doaj   +1 more source

Self‐Regulated Learning Meets AI: Reinterpreting Self‐Regulation, Co‐Regulation, and Socially Shared Regulation in Human–AI Interaction

New Directions for Adult and Continuing Education, EarlyView.
ABSTRACT Advancing artificial intelligence (AI) has transformed learning and work, yet higher education and professional development programs have not systematically equipped learners for AI‐prevalent environments. This lack of preparation creates uncertainty regarding control, responsibility, trust, and accountability.
Moon‐Heum Cho, Jerusalem Merkebu
wiley   +1 more source