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وضعيت: ريوايزد🔥

💠Advancements in Deep Learning for predicting Drug-Lipid interactions in liposomal drug delivery
 
 
🔹Abstract
 
Liposomal drug delivery systems have improved cancer therapeutics by enhancing drug stability, allowing selective tissue targeting, and reducing off-target effects. One of the main problems, however, is how to maximize drug-lipid interaction as well as develop personalized treatment alternatives. Traditional methods in computational biology, such as molecular dynamics simulations, are useful but have challenges in their scalability and cost of computation. This study focuses on the use of deep learning algorithms, Graph Neural Networks (GNNs), Attention Mechanisms, and Physics-Informed Neural Networks (PINNs) for the prediction and optimization of drug-lipid interactions in liposomal formulations. These models are much more advanced, can handle complex datasets with simplified models, and recognize complicated interaction patterns while adhering to the necessary physics involved in the problem. We highlight the practicality of these models in predicting encapsulation efficiency, drug release kinetics, and developing controlled drug delivery systems for cancer treatment through several case studies. Also, the application of transfer learning and meta-learning improves model transferability in different drug-lipid matrices, which is a step towards personalized medicine. Our results highlight that the combination of deep learning with experimental and clinical evidence enhances predictive performance and expands scope, thereby facilitating the formulation of more exact and individualized treatment modalities. Such an interdisciplinary approach can greatly improve treatment efficacy and expand the horizons of precision medicine in the field of nanomedicine.
 
 
Keywords: Liposomal drug delivery, Deep Learning models, Drug-Lipid interactions, Physics-Informed Neural Networks (PINNs), Encapsulation efficiency, Personalized medicine, Nanomedicine.
 
 Journal:https://link.springer.com/journal/11831
If: 9.9
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@Raminmousa
@Paper4money
@Machine_learn

AISafetyLab: A Comprehensive Framework for AI Safety Evaluation and Improvement

🖥 Github: https://github.com/thu-coai/AISafetyLab

📕 Paper: https://arxiv.org/abs/2502.16776v1

🌟 Dataset: https://paperswithcode.com/dataset/gptfuzzer

@Machine_learn

From System 1 to System 2: A Survey of Reasoning Large Language Models

24 Feb 2025 · Zhong-Zhi Li, Duzhen Zhang, Ming-Liang Zhang, Jiaxin Zhang, Zengyan Liu, Yuxuan Yao, Haotian Xu, Junhao Zheng, Pei-Jie Wang, Xiuyi Chen, Yingying Zhang, Fei Yin, Jiahua Dong, Zhijiang Guo, Le Song, Cheng-Lin Liu ·

Achieving human-level intelligence requires refining the transition from the fast, intuitive System 1 to the slower, more deliberate System 2 reasoning. While System 1 excels in quick, heuristic decisions, System 2 relies on logical reasoning for more accurate judgments and reduced biases. Foundational Large Language Models (LLMs) excel at fast decision-making but lack the depth for complex reasoning, as they have not yet fully embraced the step-by-step analysis characteristic of true System 2 thinking. Recently, reasoning LLMs like OpenAI's o1/o3 and DeepSeek's R1 have demonstrated expert-level performance in fields such as mathematics and coding, closely mimicking the deliberate reasoning of System 2 and showcasing human-like cognitive abilities. This survey begins with a brief overview of the progress in foundational LLMs and the early development of System 2 technologies, exploring how their combination has paved the way for reasoning LLMs. Next, we discuss how to construct reasoning #LLMs, analyzing their features, the core methods enabling advanced reasoning, and the evolution of various reasoning LLMs. Additionally, we provide an overview of reasoning benchmarks, offering an in-depth comparison of the performance of representative reasoning LLMs. Finally, we explore promising directions for advancing reasoning LLMs and maintain a real-time \href{https://github.com/zzli2022/Awesome-Slow-Reason-System}{GitHub Repository} to track the latest developments. We hope this survey will serve as a valuable resource to inspire innovation and drive progress in this rapidly evolving field.

Paper: https://arxiv.org/pdf/2502.17419v1.pdf

Code: https://github.com/zzli2022/awesome-slow-reason-system

Datasets: GSM8K - MedQA - MathVista - GPQA - MMLU-Pro - PGPS9K

💠https://www.tg-me.com/deep_learning_proj

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Title: Wavelet transform and deep average model for price and illiquidity prediction cryptocurrencies using high-dimensional features

🔸🔸🔸🔸🔸🔸🔸🔸
abstarct: Cryptocurrencies are alternative payment methods that are created using encrypted algorithms. Encryption technologies mean that cryptocurrencies act as both a currency and a virtual accounting system. The global crypto market value is \$2.9 trillion. Hence, it requires high investment requirements. One of the challenging issues in cryptocurrencies is illiquidity. Due to behavioural chaos in the market, some currencies have severe dumps and pumps, which cause concerns for investors. This paper deals with price prediction and illiquidity prediction (converting one asset to another while maintaining its value). The proposed Wavelet Deep average model uses a combination of Wavelet transform and average deep learning models for the final prediction. This model uses the hash rate information of currencies as the main inputs. Then, it achieves the selection of a subset of features using a Random Forest(RF). The selected features are designed by a Wavelet and are considered as the input to the deep network. Four currencies, BTC, Dogecoin, Ethereum(ETH), and Bitcoin Cash(BCH), were considered for model evaluation. In Bitcoin prediction, the lowest MAE for price prediction and illiquidity was achieved, which was 1.19 and 1.49, respectively. Also, the proposed model achieved MAE of 1.99, 3.69, and 2.99 for the illiquidity of three currencies Dogecoin, ETH, and BCH. The implementation codes are available in https://github.com/Ramin1Mousa/.

Journal:
Neural computing and application (springer)

@Raminmousa