Multimodal Learning for Mapping Genotype–Phenotype Dynamics

Information Science, Computer Science, Artificial Intelligence, Medicine, Life Sciences, Cell Biology, Biochemistry, Bioengineering, Genetics,
multimodal learning   genotype-phenotype relationship   single-cell RNA sequencing   machine learning   cellular heterogeneity  

Multimodal Learning Reveals Genotype–Phenotype Dynamics Background The complex relationship between genotype and phenotype has long been a central question in biology. Genotype refers to the genetic information of an organism, while phenotype is the manifestation of this genetic information in a specific environment. Although Wilhelm Johannsen intr...

Deep Bayesian Active Learning Using In-Memory Computing Hardware

Chemistry, Nanoscience, Engineering, Electrical Engineering, Information Science, Computer Science, Semiconductors and Information Devices, Artificial Intelligence,
in-memory computing   deep Bayesian learning   uncertainty capture   robot skill learning   efficient learning  

With the rapid development of artificial intelligence (AI) technologies, deep learning has made significant progress in complex tasks. However, the success of deep learning largely relies on massive amounts of labeled data, and the data labeling process is not only time-consuming and labor-intensive but also requires specialized domain knowledge, m...

Approaching Coupled-Cluster Accuracy for Molecular Electronic Structures with Multi-Task Learning

Chemistry, Computational Chemistry, Materials Science, Information Science, Artificial Intelligence,
multi-task learning   coupled-cluster methods   molecular electronic structure   quantum chemistry   machine learning  

Machine Learning Boosts Quantum Chemistry: Predicting Molecular Electronic Structures Approaching Coupled-Cluster Accuracy Academic Background In physics, chemistry, and materials science, computational methods are key tools for uncovering the mechanisms behind diverse physical phenomena and accelerating materials design. However, quantum chemistry...

A Spatiotemporal Style Transfer Algorithm for Dynamic Visual Stimulus Generation

Information Science, Computer Science, Artificial Intelligence, Medicine, Neurology,
dynamic visual stimulus   style transfer   deep learning   vision science   spatiotemporal features   artificial vision   neuroscience  

Research Report on the Spatiotemporal Style Transfer Algorithm for Dynamic Visual Stimulus Generation Academic Background The encoding and processing of visual information has been a significant focus in the fields of neuroscience and vision science. With the rapid development of deep learning techniques, investigating the similarities between arti...

A Simulated Annealing Algorithm for Randomizing Weighted Networks

Information Science, Computer Science, Artificial Intelligence, Medicine, Neurology,
weighted networks   simulated annealing   connectomics   network randomization   brain network organization   strength sequence   rewiring algorithms  

Research on Weighted Network Randomization Based on Simulated Annealing Algorithm Background Introduction In the field of neuroscience, connectomics is an important branch for studying the structure and function of brain neural networks. With the development of modern imaging technologies, researchers are able to acquire a wealth of biologically me...

A Scalable Framework for Learning the Geometry-Dependent Solution Operators of Partial Differential Equations

Mathematics, Information Science, Computer Science, Artificial Intelligence,
partial differential equations   geometric mapping   neural operator   artificial intelligence   computational efficiency  

Introduction In recent years, solving partial differential equations (PDEs) using numerical methods has played a significant role in various fields such as engineering and medicine. These methods have shown remarkable effectiveness in applications like topology and design optimization as well as clinical prognostication. However, the high computati...

Comprehensive Prediction and Analysis of Human Protein Essentiality Based on a Pretrained Large Language Model

Information Science, Artificial Intelligence, Medicine, Medical Laboratory Science, Life Sciences, Bioengineering,
pretrained model   protein essentiality   deep learning   biomedicine   biomarkers   cancer prognosis   microproteins  

Comprehensive Prediction and Analysis of Human Protein Essentiality Based on a Pretrained Large Language Model Academic Background Human Essential Proteins (HEPs) are crucial for individual survival and development. However, experimental methods for identifying HEPs are often costly, time-consuming, and labor-intensive. Additionally, existing compu...

A Deep Learning Approach for Rational Ligand Generation with Toxicity Control

Chemistry, Medicinal Chemistry, Information Science, Artificial Intelligence, Life Sciences, Biochemistry,
deep learning   ligand generation   toxicity control   drug design   computational model   block-based generation   molecular optimization  

Latest Research on Deep Learning Applied to Target Protein Ligand Generation: Proposal and Validation of the DeepBlock Framework Background and Research Problem In the drug discovery process, finding ligand molecules that bind to specific proteins has always been a core objective. However, current virtual screening methods are often limited by the ...

Spin-Symmetry-Enforced Solution of the Many-Body Schrödinger Equation with a Deep Neural Network

Physics, Quantum Physics, Chemistry, Computational Chemistry, Information Science, Artificial Intelligence,
deep neural networks   Schrödinger equation   spin symmetry   quantum states   photochemistry   multi-reference systems   electron correlation  

Research on Deep Learning Framework for Spin-Symmetry-Enforced Solutions to the Many-Body Schrödinger Equation: A Groundbreaking Achievement In the fields of quantum physics and quantum chemistry, the description of many-body electron systems has always been an important yet highly challenging topic. Accurately characterizing strong electron-electr...

Predicting Crystals Formation from Amorphous Precursors Using Deep Learning Potentials

Materials Science, Inorganic Non-Metallic Materials, Information Science, Computer Science, Artificial Intelligence,
deep learning   amorphous precursors   crystal formation   non-crystalline phases   polymorphs   materials science   energy landscape  

Predicting the Emergence of Crystals from Amorphous Precursors: Deep Learning Empowers Breakthroughs in Materials Science Background Introduction The process of crystallization from amorphous materials holds significant importance in both natural and laboratory settings. This phenomenon is widespread in various processes ranging from geological to ...