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Course Introduction："Self-study IT Network Linux Load Balancing Video Tutorial" mainly implements Linux load balancing by performing script operations on web, lvs and Linux under nagin.

Course Advanced  17581

Course Introduction："Shangxuetang MySQL Video Tutorial" introduces you to the process from installing to using the MySQL database, and introduces the specific operations of each link in detail.

Course Advanced  11291

Course Introduction："Brothers Band Front-end Example Display Video Tutorial" introduces examples of HTML5 and CSS3 technologies to everyone, so that everyone can become more proficient in using HTML5 and CSS3.

Course Introduction：Editor | Radish Skin Understanding protein function and developing molecular therapies requires identifying the cell types in which the protein acts and dissecting the interactions between proteins. However, modeling protein interactions across biological contexts remains challenging for existing algorithms. In the latest study, researchers at Harvard Medical School developed PINNACLE, a geometric deep learning method for generating context-aware protein representations. PINNACLE leverages multi-organ single cell atlases to learn on contextualized protein interaction networks, generating 394,760 protein representations from 156 cell type contexts in 24 tissues. The study is based on the concept of “ContextualAImodelsforsingl

2024-07-26 comment 0  1140

Course Introduction：Editor | KX In life, it's easy to turn on a lamp or adjust the lighting. But systems that achieve similar control of biomolecular functions are complex and poorly understood. In biology, protein functions are turned on and off in complex ways, and allosteric regulation is one of the important biological regulatory mechanisms that is essential for healthy metabolism and cell signaling. But creating allostery in synthetic protein systems has always presented significant challenges. Recently, David Baker's team at the University of Washington designed a protein that can switch between assembly and disassembly reliably and accurately through allosteric control. The researchers used AI to design new proteins that do not exist in nature and designed a variety of dynamic protein arrangements. David Baker said:

2024-08-20 comment 0  676

Course Introduction：1. Importance of protein interactions Proteins are the building blocks of life and participate in almost all biological processes. Understanding how proteins interact is critical to explaining the complexity of cellular function. 2. New method: pairing interacting protein sequences Anne-Florence Bitbol’s team at Ecole Polytechnique Fédérale de Lausanne (EPFL) proposed a method to pair interacting protein sequences. This method exploits the power of protein language models trained on multiple sequence alignments. 3. Method advantages This method performs well for small data sets and can improve the structure prediction of protein complexes through supervised methods. 4. The research results are published as "Pairinginteractingprotein

2024-07-16 comment 0  843

Course Introduction：目录​Language models generalize beyond natural proteinsA high-level programming language for generative protein designDOC: Improving Long Story Coherence With Detailed Outline Contro Scalable Diffusion Models with Transformers Point-E: A System for Gen

2023-04-13 comment 0  1767

Course Introduction：Editor | Radish Skin Protein design and engineering are advancing at an unprecedented pace thanks to advances in deep learning. However, current models cannot naturally account for non-protein entities during the design process. Here, researchers at the Ecole Polytechnique Fédérale de Lausanne (EPFL) in Switzerland propose a deep learning method based entirely on a geometric transformer of atomic coordinates and element names that can predict proteins based on backbone scaffolds with constraints imposed by different molecular environments. sequence. Using this method, researchers can produce highly thermostable, catalytically active enzymes with a high success rate. This is expected to increase the versatility of protein design pipelines to achieve desired functions. This study uses "Context-awaregeometricde

2024-08-05 comment 0  941