Protein folding is the intricate process by which a linear sequence of amino
acids self-assembles into a unique three-dimensional structure. Protein folding
kinetics is the study of pathways and time-dependent mechanisms a protein
undergoes when it folds. Understanding protein kinetics is essential as a
protein needs to fold correctly for it to perform its biological functions
optimally, and a misfolded protein can sometimes be contorted into shapes that
are not ideal for a cellular environment giving rise to many degenerative,
neuro-degenerative disorders and amyloid diseases. Monitoring at-risk
individuals and detecting protein discrepancies in a protein's folding kinetics
at the early stages could majorly result in public health benefits, as
preventive measures can be taken. This research proposes an efficient pipeline
for predicting protein folding kinetics with high accuracy and low memory
footprint. The deployed machine learning (ML) model outperformed the
state-of-the-art ML models by 4.8% in terms of accuracy while consuming 327x
lesser memory and being 7.3% faster.

通过使用机器学习模型，本研究提出了一种高准确性、低内存占用的有效方法来预测蛋白质的折叠动力学，相较于现有模型提高了 4.8% 的准确性，内存占用减少了 327 倍，速度提升了 7.3%。

蛋白质折叠动力学预测的端到端优化管线

End-to-End Optimized Pipeline for Prediction of Protein Folding Kinetics

Understanding protein interactions and pathway knowledge is crucial for
unraveling the complexities of living systems and investigating the underlying
mechanisms of biological functions and complex diseases. While existing
databases provide curated biological data from literature and other sources,
they are often incomplete and their maintenance is labor-intensive,
necessitating alternative approaches. In this study, we propose to harness the
capabilities of large language models to address these issues by automatically
extracting such knowledge from the relevant scientific literature. Toward this
goal, in this work, we investigate the effectiveness of different large
language models in tasks that involve recognizing protein interactions,
pathways, and gene regulatory relations. We thoroughly evaluate the performance
of various models, highlight the significant findings, and discuss both the
future opportunities and the remaining challenges associated with this
approach. The code and data are available at:
this https URL

利用大型语言模型自动从相关科学文献中提取知识，研究识别蛋白质相互作用、通路和基因调控关系的任务效果，并评估不同模型的性能，讨论了该方法的未来机遇和挑战。

大语言模型在提取分子相互作用和通路知识方面的比较性能评估

Comparative Performance Evaluation of Large Language Models for  Extracting Molecular Interactions and Pathway Knowledge

In a dynamic social or biological environment, the interactions between the
actors can undergo large and systematic changes. In this paper we propose a
model-based approach to analyze what we will refer to as the dynamic tomography
of such time-evolving networks. Our approach offers an intuitive but powerful
tool to infer the semantic underpinnings of each actor, such as its social
roles or biological functions, underlying the observed network topologies. Our
model builds on earlier work on a mixed membership stochastic blockmodel for
static networks, and the state-space model for tracking object trajectory. It
overcomes a major limitation of many current network inference techniques,
which assume that each actor plays a unique and invariant role that accounts
for all its interactions with other actors; instead, our method models the role
of each actor as a time-evolving mixed membership vector that allows actors to
behave differently over time and carry out different roles/functions when
interacting with different peers, which is closer to reality. We present an
efficient algorithm for approximate inference and learning using our model; and
we applied our model to analyze a social network between monks (i.e., the
Sampson's network), a dynamic email communication network between the Enron
employees, and a rewiring gene interaction network of fruit fly collected
during its full life cycle. In all cases, our model reveals interesting
patterns of the dynamic roles of the actors.

本文提出了一种基于模型的方法来分析动态时间变化的网络中的演员，即动态层析。与其他网络推断技术不同，我们的方法将每个演员的角色建模为一个时间演化的混合成员向量，以更贴近现实。我们应用该模型分析了僧侣之间的社交网络，Enron 员工之间的动态电子邮件通信网络和果蝇在其完整生命周期期间重新布线的基因相互作用网络，揭示了演员动态角色的有趣模式。