Simple controls exceed best deep learning algorithms and reveal foundation model effectiveness for predicting genetic perturbations

bioRxiv (Cold Spring Harbor Laboratory), Год журнала: 2025, Номер unknown

Опубликована: Янв. 8, 2025

Abstract Modeling genetic perturbations and their effect on the transcriptome is a key area of pharmaceutical research. Due to complexity transcriptome, there has been much excitement development in deep learning (DL) because its ability model complex relationships. In particular, transformer-based foundation paradigm emerged as gold-standard predicting post-perturbation responses. However, understanding these increasingly models evaluating practical utility lacking, along with simple but appropriate benchmarks compare predictive methods. Here, we present baseline method that outperforms both state art (SOTA) DL other proposed simpler neural architectures, setting necessary benchmark evaluate field prediction. We also elucidate for task prediction via generalizable fine-tuning experiments can be translated different applications tasks interest. Furthermore, provide corrected version popular dataset used benchmarking perturbation models. Our hope this work will properly contextualize further space control procedures.

Язык: Английский

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Decoding the Molecular Language of Proteins with Evola

bioRxiv (Cold Spring Harbor Laboratory), Год журнала: 2025, Номер unknown

Опубликована: Янв. 6, 2025

Abstract Proteins, nature’s intricate molecular machines, are the products of billions years evolution and play fundamental roles in sustaining life. Yet, deciphering their language - that is, understanding how protein sequences structures encode determine biological functions remains a cornerstone challenge modern biology. Here, we introduce Evola, an 80 billion frontier protein-language generative model designed to decode proteins. By integrating information from sequences, structures, user queries, Evola generates precise contextually nuanced insights into function. A key innovation lies its training on unprecedented AI-generated dataset: 546 million question-answer pairs 150 word tokens, reflect immense complexity functional diversity Post-pretraining, integrates Direct Preference Optimization (DPO) refine based preference signals Retrieval-Augmented Generation (RAG) for external knowledge incorporation, improving response quality relevance. To evaluate performance, propose novel framework, Instructional Response Space (IRS), demonstrating delivers expert-level insights, advancing research proteomics genomics while shedding light logic encoded The online demo is available at http://www.chat-protein.com/ .

Язык: Английский

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Ai-enabled language models (LMs) to large language models (LLMs) and multimodal large language models (MLLMs) in drug discovery and development

Journal of Advanced Research, Год журнала: 2025, Номер unknown

Опубликована: Фев. 1, 2025

Язык: Английский

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A Review of Recent Artificial Intelligence for Traditional Medicine

Journal of Traditional and Complementary Medicine, Год журнала: 2025, Номер unknown

Опубликована: Фев. 1, 2025

Язык: Английский

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Evaluation of the performance of ChatGPT‐4 and ChatGPT‐4o as a learning tool in endodontics

International Endodontic Journal, Год журнала: 2025, Номер unknown

Опубликована: Март 2, 2025

Abstract Aims The aim of this study was to evaluate the accuracy and consistency responses given by two different versions Chat Generative Pre‐trained Transformer (ChatGPT), ChatGPT‐4, ChatGPT‐4o, multiple‐choice questions prepared from undergraduate endodontic education topics at times day on days. Methodology In total, 60 multiple‐choice, text‐based 6 were prepared. Each question asked ChatGPT‐4 ChatGPT‐4o 3 a (morning, noon, evening) for consecutive AIs compared using SPSS R programs ( p < .05, 95% confidence interval). Results rate (92.8%) significantly higher than that (81.7%; .001). groups affected rates both which did not affect either AI > .05). There no statistically significant difference in between = .123). AI, too Conclusions According results study, better ChatGPT‐4. These findings demonstrate chatbots can be used dental education. However, it is also necessary consider limitations potential risks associated with AI.

Язык: Английский

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Prompt-engineering enabled LLM or MLLM and instigative bioinformatics pave the way to identify and characterize the significant SARS-CoV-2 antibody escape mutations

International Journal of Biological Macromolecules, Год журнала: 2024, Номер unknown, С. 138547 - 138547

Опубликована: Дек. 1, 2024

Язык: Английский

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