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quantitative biology methods

GeneTech 知识引擎共收录 1417 篇相关科研实体，横跨 30 个垂直领域。数据来自 OpenAlex、arXiv、Crossref、PubMed、Europe PMC 等公开学术源，全部可溯源到原文。

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代表性文献（按质量分排序，前 60 条）

• Fifteen challenges for generative AI applications to cell biology.
  2026 · AI 驱动科学发现 · Generative AI (Gen-AI) has shown a remarkable impact in several biological research areas, from protein folding and de novo design to pathogenic mutation prediction. However, it remains unclear whether these molecular-le…
• Label-Free Impedance-Activated Acoustic Sorting of Cells From Structural to Molecular Scale in Microfluidics.
  2026 · AI 驱动科学发现 · Label-free, noninvasive, and high-throughput analysis and sorting of cells are essential capabilities in modern biomedicine, particularly for investigating cellular heterogeneity, functional phenotyping, and precision me…
• Precision Application of Nitrogen by Root Dipping Application as Starter Solution in Chilli (Capsicum annuum L.)
  2026 · 智慧农业 · To meet the nutritional demand of the vegetable crops, it is essential to supply nutrients throughout their growth period. Supply of nutrients at initial growth stages of plant leads to improved root development thus ult…
• Foundation models and AI agents in oncology drug discovery
  2026 · AI 驱动科学发现 · Oncology drug discovery remains limited by high attrition, slow experimental iteration and weak translation from preclinical models to clinical benefit. This review examines how AI is restructuring that process through t…
• The comparison between the Pt-Rh thermodynamic database from machine learning interatomic potential and first-principles calculations
  2026 · AI 驱动科学发现 · Platinum–rhodium alloys are one of the prominent alloys used in high-temperature and high-corrosion environments. Pt and Rh maintain a single solid-solution phase up to high temperatures. To develop and design a part for…
• Hierarchical transfer learning: an agile and equitable strategy for machine-learning interatomic models
  2026 · AI 驱动科学发现 · Machine-learned interatomic models are growing in popularity due to their ability to afford near quantum-accurate predictions for complex phenomena with orders-of-magnitude greater computational efficiency. However, thes…
• Enhancing potato yield estimation using vegetative indices, SAR imagery, and terrain data
  2025 · 智慧农业 · Having an accurate potato yield estimation is of the upmost importance to help improve the agronomic management of plots. The aim of this study was to analyse the relationship between synthetic aperture radar (SAR) image…
• Automatic phenotyping using exhaustive projection pursuit
  2025 · AI 驱动科学发现 · Abstract One of the most common objectives in the analysis of flow cytometry data is the identification and delineation of phenotypes, distinct populations of cells with shared characteristics in the measurement dimensio…
• Transferable Machine Learning Interatomic Potential for Pd-Catalyzed Cross-Coupling Reactions
  2025 · AI 驱动科学发现 · Finding efficient substrate-catalyst combinations for palladium-catalyzed cross-coupling reactions remains a critical challenge in synthetic chemistry, with broad implications for pharmaceutical and materials manufacturi…
• The end of protein structure prediction: Improving prediction accuracy in chimeric proteins by windowed multiple sequence alignment
  2024 · AI 驱动科学发现 · Abstract AlphaFold2 has predicted the structures of almost every known protein. A simple means to create proteins beyond those found in nature, is by unnaturally fusing together two known proteins. Here we demonstrate th…
• Conditioned Protein Structure Prediction
  2023 · AI 驱动科学发现 · Abstract Deep learning based protein structure prediction has facilitated major breakthroughs in biological sciences. However, current methods struggle with alternative conformation prediction and offer limited integrati…
• Understanding Complex Socio-Environmental Systems with Spatial Agent-Based Models
  2024 · AI Agent 生态 · Our increasingly connected world is faced with complex socio-environmental problems (e.g., biodiversity loss, climate change, and food insecurity). Tackling these problems requires cross- disciplinary approaches that exa…
• Error propagation in agricultural models
  2024 · 智慧农业 · Spatially distributed agricultural models rely for their operation on a number of value surfaces representing their various input parameters. Each of these surfaces will contain some error, due in part to the sampling an…
• Optimal treatment placement for on-farm experiments: pseudo-Bayesian optimal designs with a linear response plateau model
  2024 · 智慧农业 · On-farm experiments are increasingly being used as their costs have decreased with technological advances in collecting, storing, and processing geospatial data. A question that has not been well addressed is what spatia…
• Improving Protein Secondary Structure Prediction by Deep Language Models and Transformer Networks
  2024 · AI 驱动科学发现 · Protein secondary structure prediction is useful for many applications. It can be considered a language translation problem, that is, translating a sequence of 20 different amino acids into a sequence of secondary struct…
• Cerebra: a computationally efficient framework for accurate protein structure prediction
  2024 · AI 驱动科学发现 · Update A newer Cerebra release with updated model checkpoints and the CAMEO 2025 test set is available at: Cerebra v2 model checkpoints and CAMEO 2025 test set | Zenodo Please use the new Zenodo record for the latest mod…
• GPS-based auto-guidance test program development
  2023 · 智慧农业 · Auto-guidance based on Global Positioning System (GPS) navigation is a rapidly expanding technology in modern agriculture. Because of many discrepancies in testing methods, there is a need to establish a standardized tes…
• Protein Structure Prediction
  2023 · AI 驱动科学发现 · This chapter concentrates on protein structure prediction programs. It provides strong foundational knowledge of protein structures and the Protein Data Bank. The chapter reviews how secondary and tertiary structures of …
• Approaches to Evaluating Human–AI Interaction
  2026 · AI 安全与对齐 · Abstract How can human–AI interaction be evaluated? This chapter provides a high-level overview of three main approaches to evaluating human–AI interaction: empirical, analytical, and model based. Empirical methods are b…
• Protein Structure Prediction
  2026 · AI 驱动科学发现 · This chapter presents experimental and computational approaches to determining protein structures, focusing on secondary and tertiary levels. It describes how researchers use X-ray crystallography, NMR spectroscopy, and …
• Bio-Information Dimension Prediction
  2026 · AI 驱动科学发现 · This paper introduces a novel dimension prediction model based on integrated biological genome and protein structure data. The model aims to predict key molecular interaction and biological process dimensions, offering a…
• Bio-Information Dimension Prediction
  2026 · AI 驱动科学发现 · This paper introduces a novel dimension prediction model based on integrated biological genome and protein structure data. The model aims to predict key molecular interaction and biological process dimensions, offering a…
• Pangenome-scale annotation of mycobacteriophages for dissecting phage–host interactions based on a Sequence Clustering and Structural Homology analysis strategy
  2026 · AI 驱动科学发现 · This record contains data related to the article "Pangenome-scale annotation of mycobacteriophages for dissecting phage–host interactions based on a Sequence Clustering and Structural Homology analysis strategy." Supplem…
• Pangenome-scale annotation of mycobacteriophages for dissecting phage–host interactions based on a Sequence Clustering and Structural Homology analysis strategy
  2026 · AI 驱动科学发现 · This record contains data related to the article "Pangenome-scale annotation of mycobacteriophages for dissecting phage–host interactions based on a Sequence Clustering and Structural Homology analysis strategy." Supplem…
• Graph Neural Networks for Protein Structure Prediction
  2026 · AI 驱动科学发现 · Protein structure prediction remains a grand challenge in computational biology. Traditional methods often struggle to accurately capture the intricate relationships within a protein sequence, leading to suboptimal struc…
• Graph Neural Networks for Protein Structure Prediction
  2026 · AI 驱动科学发现 · Protein structure prediction remains a grand challenge in computational biology. Traditional methods often struggle to accurately capture the intricate relationships within a protein sequence, leading to suboptimal struc…
• Topology-Driven Computational Framework
  2026 · AI 驱动科学发现 · This paper introduces a novel computational framework, the Topology-Driven Computational Framework (TDCF), designed to efficiently model and simulate complex biological systems, particularly focusing on gene expression a…
• Topology-Driven Computational Framework
  2026 · AI 驱动科学发现 · This paper introduces a novel computational framework, the Topology-Driven Computational Framework (TDCF), designed to efficiently model and simulate complex biological systems, particularly focusing on gene expression a…
• 399 Generative AI in data analysis for animal science.
  2025 · AI 驱动科学发现 · Abstract The near ubiquity of large language models (LLM) and AI have made it difficult to escape the its use in every aspect of life. The animal sciences has recently accelerated the use of new technologies including AI…
• Advanced machine learning method for prediction of protein secondary structure
  2025 · AI 驱动科学发现 · A new method based on Markov process to encode the protein sequences has been introduced. With this simple method, input vectors that contain the essential features of protein sequence can be extracted and efficiently us…
• Workflow for Protein Structure Prediction, Refinement, and Validation v1
  2025 · AI 驱动科学发现 · This protocol provides a reproducible computational workflow for protein structure prediction using a combination of freely available software, open-source pipelines, and specialized protein structure prediction services…
• AlphaFold2 for Protein Structure Prediction: Best Practices and Critical Analyses.
  2024 · AI 驱动科学发现 · AlphaFold2 (AF2) has emerged in recent years as a groundbreaking innovation that has revolutionized several scientific fields, in particular structural biology, drug design, and the elucidation of disease mechanisms. Man…
• TANA: efficient approach for predicting protein functions by transferring annotation via alignment networks
  2020 · AI 安全与对齐 · Abstract Background: One of the challenges of the post-genomic era is to provide accurate function annotations for orphan and unannotated protein sequences. With the recent availability of huge protein-protein interactio…
• TANA: efficient approach for predicting protein functions by transferring annotation via alignment networks
  2020 · AI 安全与对齐 · Abstract Background: One of the challenges of the post-genomic era is to provide accurate function annotations for orphan and unannotated protein sequences. With the recent availability of huge PPIs networks for many mod…
• Mathematical model of broadly reactive plasma cell production
  2020 · AI 驱动科学发现 · Abstract Strain-specific plasma cells are capable of producing neutralizing antibodies that are essential for clearance of challenging pathogens. These neutralizing antibodies also function as a main defense against dise…
• PairK: Pairwise k-mer alignment for quantifying protein motif conservation in disordered regions
  2024 · AI 安全与对齐 · ABSTRACT Protein-protein interactions are often mediated by a modular peptide recognition domain binding to a short linear motif (SLiM) in the disordered region of another protein. The ability to predict domain-SLiM inte…
• Advanced Production Diagnostics and Optimized Drilling Strategy: Unlocking the True Potential of Production Data Using Physics and Machine Learning
  2024 · AI 驱动科学发现 · Abstract Reliable well survival statistics forecast for shale wells is crucial for investment decisions, optimized drilling rates, and energy security policies. At present, the industry lacks a standard analytical or dat…
• Structure prediction of alternative protein conformations
  2024 · AI 驱动科学发现 · Abstract Proteins are dynamic molecules whose movements result in different conformations with different functions. Neural networks such as AlphaFold2 can predict the structure of single-chain proteins with conformations…
• Use of geographic information systems (GIS) in crop protection warning service
  2023 · 智慧农业 · One of the important aims of the Governmental Crop Protection Services (GCPS) in Germany is to reduce spraying intensity and to guarantee an environmentally friendly and economical crop protection strategy. ZEPP is the c…
• Prediction of transmembrane protein topology
  2023 · AI 驱动科学发现 · Abstract Membrane proteins fulfil many critical cellular functions, and make up a large fraction of all proteins. In fact, it has recently been estimated that as many as 35–-40% of all yeast genes may code for integral m…
• Protein-protein interaction prediction via structure-based deep learning
  2023 · AI 驱动科学发现 · Abstract Protein-protein interactions (PPIs) play an essential role in life activities. Many machine learning algorithms based on protein sequence information have been developed to predict PPIs. However, these models ha…
• Protein-DNA interaction prediction and protein structure modeling by machine learning
  2023 · AI 驱动科学发现 · [EMBARGOED UNTIL 8/1/2023] Proteins are large, complex molecules that perform most essential functions within organisms. In this work, we mainly focus on two important aspects that determine their functional properties: …
• Structure prediction of alternative protein conformations
  2023 · AI 驱动科学发现 · Abstract Proteins are dynamic molecules whose movements result in different conformations with different functions. Neural networks such as AlphaFold2 can predict the structure of single-chain proteins in the conformatio…
• Protein Structure Prediction
  2023 · AI 驱动科学发现 · The primary resource for protein structure is PDB. This database stores the coordinates of protein structures that have been solved either using X-rays or NMR. There exists a huge literature on techniques for predicting …
• The prediction challenge
  2023 · AI 驱动科学发现 · Abstract Genome projects will soon be producing hundreds of kilobases of raw sequence a day. A major effort in biocomputing will be the analysis of sequence data in terms of functional and structural characterization. Ho…
• Group-theoretical high-order rotational invariants for structural representations: Application to linearized machine learning interatomic potential
  2019 · AI 驱动科学发现 · Many rotational invariants for crystal structure representations have been used to describe the structure-property relationship by machine learning. The machine learning interatomic potential (MLIP) is one of the applica…
• Advances in Protein Super-Secondary Structure Prediction and Application to Protein Structure Prediction
  2019 · AI 驱动科学发现 · Due to the advancement in various sequencing technologies, the gap between the number of protein sequences and the number of experimental protein structures is ever increasing. Community-wide initiatives like CASP have r…
• On Analyzing Self-Driving Networks
  2018 · AI 驱动科学发现 · Along with recent networking advances (such as software-defined networks, network functions virtualization, and programmable data planes), the networking field, in a bid to construct highly optimized self-driving and sel…
• Estimation of rice protein content before harvest using ground-based hyperspectral imaging and region of interest analysis
  2017 · 智慧农业 · Protein content, which represents rice taste quality, must be estimated in order to create a harvesting plan as well as next year’s basal dressing fertilizer application plan. Ground-based hyperspectral imaging with high…
• The GOR Method of Protein Secondary Structure Prediction and Its Application as a Protein Aggregation Prediction Tool
  2016 · AI 驱动科学发现 · The GOR method of protein secondary structure prediction is described. The original method was published by Garnier, Osguthorpe, and Robson in 1978 and was one of the first successful methods to predict protein secondary…
• Diversifying TS using GA in Multi-agent System for Solving Flexible Job Shop Problem
  2015 · AI Agent 生态 · No doubt, the flexible job shop problem (FJSP) has an important significance in both fields of production management and combinatorial optimization. For this reason, FJSP continues to attract the interests of researchers…
• NEURAL-NETWORK-BASED GENETIC ALGORITHM FOR OPTIMAL KITCHEN FAUCET STYLES
  2015 · AI 安全与对齐 · Artificial neural networks (ANNs) are the models of choice in many data classification tasks.In this study, ANN classification models were used to explore user perceptions about kitchen faucet styles and investigate the …
• DOCK/PIERR: Web Server for Structure Prediction of Protein–Protein Complexes
  2014 · AI 驱动科学发现 · In protein docking we aim to find the structure of the complex formed when two proteins interact. Protein-protein interactions are crucial for cell function. Here we discuss the usage of DOCK/PIERR. In DOCK/PIERR, a unif…
• The MULTICOM Protein Tertiary Structure Prediction System
  2014 · AI 驱动科学发现 · With the expansion of genomics and proteomics data aided by the rapid progress of next-generation sequencing technologies, computational prediction of protein three-dimensional structure is an essential part of modern st…
• Relating Protein Structure and Function Through a Bijection and Its Implications on Protein Structure Prediction
  2012 · AI 驱动科学发现 · Proteins are studied by measuring different properties, typically the chemical structure and biochemical activity. Given that these measurements are done on the same protein molecule, they must be related. Despite the fa…
• A dynamic grain flow model for a mass flow yield sensor on a combine
  2011 · 智慧农业 · A model is developed to describe the flow of grain through a clean grain elevator system on a combine in order to facilitate accurate mass flow rate estimation. The relationship between mass flow rate and impact force de…
• Protein Structure Prediction by Protein Threading
  2010 · AI 驱动科学发现 · The seminal work of Bowie, Lüthy, and Eisenberg (Bowie et al., 1991) on “the inverse protein folding problem” laid the foundation of protein structure prediction by protein threading. By using simple measures for fitness…
• Methodology for the use of DSSAT models for precision agriculture decision support
  2008 · 智慧农业 · A prototype decision support system (DSS) called Apollo was developed to assist researchers in using the Decision Support System for Agrotechnology Transfer (DSSAT) crop growth models to analyze precision farming dataset…
• Protein Structure Prediction Using Threading
  2008 · AI 驱动科学发现 · This chapter discusses the protocol for computational protein structure prediction by protein threading. First, we present a general procedure and summarize some typical ideas for each step of protein threading. Then, we…
• Scoring Functions for De Novo Protein Structure Prediction Revisited
  2008 · AI 驱动科学发现 · De novo protein structure prediction methods attempt to predict tertiary structures from sequences based on general principles that govern protein folding energetics and/or statistical tendencies of conformational featur…

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