arXiv - QuanBio - Quantitative Methods最新文献_第3页

Modelling the effect of antibody depletion on dose-response behavior for common immunostaining protocols 模拟抗体耗竭对常见免疫染色方案剂量反应行为的影响

arXiv - QuanBio - Quantitative Methods Pub Date : 2024-09-10 DOI: arxiv-2409.06895

Dominik Tschimmel, Steffen Waldherr, Tim Hucho

{"title":"Modelling the effect of antibody depletion on dose-response behavior for common immunostaining protocols","authors":"Dominik Tschimmel, Steffen Waldherr, Tim Hucho","doi":"arxiv-2409.06895","DOIUrl":"https://doi.org/arxiv-2409.06895","url":null,"abstract":"Dose-response curves of immunostaining experiments are commonly described as\u0000Langmuir isotherm. However, for common immunostaining protocols the equilibrium\u0000assumption is violated and the dose-response behavior is governed by antibody\u0000accumulation. If bound antibodies are replenished, i.e. the concentration of\u0000unbound antibodies is constant, the accumulation model can easily be solved\u0000analytically. Yet, in many experimental setups the overall amount of antibodies\u0000is fixed such that antibody binding reduces the concentration of free\u0000antibodies. Solving the accumulation model for this case is more difficult and\u0000seems to be impossible if the epitopes are heterogeneous. In this paper, we\u0000solve the accumulation model with antibody depletion analytically for the\u0000simple case of identical epitopes. We derive inequalities between the\u0000depletion-free accumulation model, the accumulation model and the Langmuir\u0000isotherm. This allows us to characterize the antibody depletion effect. We\u0000generalize the problem to heterogeneous epitopes, where we prove the existence\u0000and uniqueness of a solution that behaves as expected by the experimental\u0000setting. With these properties we derive bounds for the resulting\u0000multi-epitope-class accumulation model and investigate the depletion effect in\u0000the case of heterogeneous epitopes.","PeriodicalId":501266,"journal":{"name":"arXiv - QuanBio - Quantitative Methods","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-09-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142213266","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

ProteinBench: A Holistic Evaluation of Protein Foundation Models ProteinBench：蛋白质基础模型的整体评估

arXiv - QuanBio - Quantitative Methods Pub Date : 2024-09-10 DOI: arxiv-2409.06744

Fei Ye, Zaixiang Zheng, Dongyu Xue, Yuning Shen, Lihao Wang, Yiming Ma, Yan Wang, Xinyou Wang, Xiangxin Zhou, Quanquan Gu

{"title":"ProteinBench: A Holistic Evaluation of Protein Foundation Models","authors":"Fei Ye, Zaixiang Zheng, Dongyu Xue, Yuning Shen, Lihao Wang, Yiming Ma, Yan Wang, Xinyou Wang, Xiangxin Zhou, Quanquan Gu","doi":"arxiv-2409.06744","DOIUrl":"https://doi.org/arxiv-2409.06744","url":null,"abstract":"Recent years have witnessed a surge in the development of protein foundation\u0000models, significantly improving performance in protein prediction and\u0000generative tasks ranging from 3D structure prediction and protein design to\u0000conformational dynamics. However, the capabilities and limitations associated\u0000with these models remain poorly understood due to the absence of a unified\u0000evaluation framework. To fill this gap, we introduce ProteinBench, a holistic\u0000evaluation framework designed to enhance the transparency of protein foundation\u0000models. Our approach consists of three key components: (i) A taxonomic\u0000classification of tasks that broadly encompass the main challenges in the\u0000protein domain, based on the relationships between different protein\u0000modalities; (ii) A multi-metric evaluation approach that assesses performance\u0000across four key dimensions: quality, novelty, diversity, and robustness; and\u0000(iii) In-depth analyses from various user objectives, providing a holistic view\u0000of model performance. Our comprehensive evaluation of protein foundation models\u0000reveals several key findings that shed light on their current capabilities and\u0000limitations. To promote transparency and facilitate further research, we\u0000release the evaluation dataset, code, and a public leaderboard publicly for\u0000further analysis and a general modular toolkit. We intend for ProteinBench to\u0000be a living benchmark for establishing a standardized, in-depth evaluation\u0000framework for protein foundation models, driving their development and\u0000application while fostering collaboration within the field.","PeriodicalId":501266,"journal":{"name":"arXiv - QuanBio - Quantitative Methods","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-09-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142213267","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

DeepFM-Crispr: Prediction of CRISPR On-Target Effects via Deep Learning DeepFM-Crispr：通过深度学习预测 CRISPR 靶向效应

arXiv - QuanBio - Quantitative Methods Pub Date : 2024-09-09 DOI: arxiv-2409.05938

Condy Bao, Fuxiao Liu

{"title":"DeepFM-Crispr: Prediction of CRISPR On-Target Effects via Deep Learning","authors":"Condy Bao, Fuxiao Liu","doi":"arxiv-2409.05938","DOIUrl":"https://doi.org/arxiv-2409.05938","url":null,"abstract":"Since the advent of CRISPR-Cas9, a groundbreaking gene-editing technology\u0000that enables precise genomic modifications via a short RNA guide sequence,\u0000there has been a marked increase in the accessibility and application of this\u0000technology across various fields. The success of CRISPR-Cas9 has spurred\u0000further investment and led to the discovery of additional CRISPR systems,\u0000including CRISPR-Cas13. Distinct from Cas9, which targets DNA, Cas13 targets\u0000RNA, offering unique advantages for gene modulation. We focus on Cas13d, a\u0000variant known for its collateral activity where it non-specifically cleaves\u0000adjacent RNA molecules upon activation, a feature critical to its function. We\u0000introduce DeepFM-Crispr, a novel deep learning model developed to predict the\u0000on-target efficiency and evaluate the off-target effects of Cas13d. This model\u0000harnesses a large language model to generate comprehensive representations rich\u0000in evolutionary and structural data, thereby enhancing predictions of RNA\u0000secondary structures and overall sgRNA efficacy. A transformer-based\u0000architecture processes these inputs to produce a predictive efficacy score.\u0000Comparative experiments show that DeepFM-Crispr not only surpasses traditional\u0000models but also outperforms recent state-of-the-art deep learning methods in\u0000terms of prediction accuracy and reliability.","PeriodicalId":501266,"journal":{"name":"arXiv - QuanBio - Quantitative Methods","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-09-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142213272","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

signDNE: A python package for ariaDNE and its sign-oriented extension signDNE：ariaDNE 及其面向符号扩展的 python 软件包

arXiv - QuanBio - Quantitative Methods Pub Date : 2024-09-09 DOI: arxiv-2409.05549

Felix Risbro Hjerrild, Shan Shan, Doug M Boyer, Ingrid Daubechies

{"title":"signDNE: A python package for ariaDNE and its sign-oriented extension","authors":"Felix Risbro Hjerrild, Shan Shan, Doug M Boyer, Ingrid Daubechies","doi":"arxiv-2409.05549","DOIUrl":"https://doi.org/arxiv-2409.05549","url":null,"abstract":"A key challenge in evolutionary biology is to develop robust computational\u0000tools that can accurately analyze shape variations across diverse anatomical\u0000structures. The Dirichlet Normal Energy (DNE) is a shape complexity metric that\u0000addresses this by summarizing the local curvature of surfaces, particularly\u0000aiding the analytical studies and providing insights into evolutionary and\u0000functional adaptations. Building on the DNE concept, we introduce a\u0000Python-based implementation, designed to compute both the original DNE and a\u0000newly developed sign-oriented DNE metric. This Python package includes a\u0000user-friendly command line interface (CLI) and built-in visualization tools to\u0000facilitate the interpretation of the surface's local curvature properties. The\u0000addition of signDNE, which integrates the convexity and concavity of surfaces,\u0000enhances the tool's ability to identify fine-scale features across a broad\u0000range of biological structures. We validate the robustness of our method by\u0000comparing its performance with standard implementations on a dataset of\u0000triangular meshes with varying discrete representations. Additionally, we\u0000demonstrate its potential applications through visualization of the local\u0000curvature field (i.e., local curvature value over the surface) on various\u0000biological specimens, showing how it effectively captures complex biological\u0000features. In this paper, we offer a brief overview of the Python CLI for ease\u0000of use. Alongside the Python implementation, we have also updated the original\u0000MATLAB package to ensure consistent and accurate DNE computation across\u0000platforms. These improvements enhance the tool's flexibility, reduce\u0000sensitivity to sampling density and mesh quality, and support a more accurate\u0000interpretation of biological surface topography.","PeriodicalId":501266,"journal":{"name":"arXiv - QuanBio - Quantitative Methods","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-09-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142213327","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

CRADLE-VAE: Enhancing Single-Cell Gene Perturbation Modeling with Counterfactual Reasoning-based Artifact Disentanglement CRADLE-VAE：利用基于反事实推理的伪差解除增强单细胞基因扰动建模

arXiv - QuanBio - Quantitative Methods Pub Date : 2024-09-09 DOI: arxiv-2409.05484

Seungheun Baek, Soyon Park, Yan Ting Chok, Junhyun Lee, Jueon Park, Mogan Gim, Jaewoo Kang

{"title":"CRADLE-VAE: Enhancing Single-Cell Gene Perturbation Modeling with Counterfactual Reasoning-based Artifact Disentanglement","authors":"Seungheun Baek, Soyon Park, Yan Ting Chok, Junhyun Lee, Jueon Park, Mogan Gim, Jaewoo Kang","doi":"arxiv-2409.05484","DOIUrl":"https://doi.org/arxiv-2409.05484","url":null,"abstract":"Predicting cellular responses to various perturbations is a critical focus in\u0000drug discovery and personalized therapeutics, with deep learning models playing\u0000a significant role in this endeavor. Single-cell datasets contain technical\u0000artifacts that may hinder the predictability of such models, which poses\u0000quality control issues highly regarded in this area. To address this, we\u0000propose CRADLE-VAE, a causal generative framework tailored for single-cell gene\u0000perturbation modeling, enhanced with counterfactual reasoning-based artifact\u0000disentanglement. Throughout training, CRADLE-VAE models the underlying latent\u0000distribution of technical artifacts and perturbation effects present in\u0000single-cell datasets. It employs counterfactual reasoning to effectively\u0000disentangle such artifacts by modulating the latent basal spaces and learns\u0000robust features for generating cellular response data with improved quality.\u0000Experimental results demonstrate that this approach improves not only treatment\u0000effect estimation performance but also generative quality as well. The\u0000CRADLE-VAE codebase is publicly available at\u0000https://github.com/dmis-lab/CRADLE-VAE.","PeriodicalId":501266,"journal":{"name":"arXiv - QuanBio - Quantitative Methods","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-09-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142213285","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Understanding how chromatin folding and enzyme competition affect rugged epigenetic landscapes 了解染色质折叠和酶竞争如何影响崎岖的表观遗传景观

arXiv - QuanBio - Quantitative Methods Pub Date : 2024-09-09 DOI: arxiv-2409.06116

Daria Stepanova, Meritxell Brunet Guasch, Helen M. Byrne, Tomás Alarcón

{"title":"Understanding how chromatin folding and enzyme competition affect rugged epigenetic landscapes","authors":"Daria Stepanova, Meritxell Brunet Guasch, Helen M. Byrne, Tomás Alarcón","doi":"arxiv-2409.06116","DOIUrl":"https://doi.org/arxiv-2409.06116","url":null,"abstract":"Epigenetics plays a key role in cellular differentiation and maintaining cell\u0000identity, enabling cells to regulate their genetic activity without altering\u0000the DNA sequence. Epigenetic regulation occurs within the context of\u0000hierarchically folded chromatin, yet the interplay between the dynamics of\u0000epigenetic modifications and chromatin architecture remains poorly understood.\u0000In addition, it remains unclear what mechanisms drive the formation of rugged\u0000epigenetic patterns, characterised by alternating genomic regions enriched in\u0000activating and repressive marks. In this study, we focus on post-translational\u0000modifications of histone H3 tails, particularly H3K27me3, H3K4me3, and H3K27ac.\u0000We introduce a mesoscopic stochastic model that incorporates chromatin\u0000architecture and competition of histone-modifying enzymes into the dynamics of\u0000epigenetic modifications in small genomic loci comprising several nucleosomes.\u0000Our approach enables us to investigate the mechanisms by which epigenetic\u0000patterns form on larger scales of chromatin organisation, such as loops and\u0000domains. Through bifurcation analysis and stochastic simulations, we\u0000demonstrate that the model can reproduce uniform chromatin states (open,\u0000closed, and bivalent) and generate previously unexplored rugged profiles. Our\u0000results suggest that enzyme competition and chromatin conformations with\u0000high-frequency interactions between distant genomic loci can drive the\u0000emergence of rugged epigenetic landscapes. Additionally, we hypothesise that\u0000bivalent chromatin can act as an intermediate state, facilitating transitions\u0000between uniform and rugged landscapes. This work offers a powerful mathematical\u0000framework for understanding the dynamic interactions between chromatin\u0000architecture and epigenetic regulation, providing new insights into the\u0000formation of complex epigenetic patterns.","PeriodicalId":501266,"journal":{"name":"arXiv - QuanBio - Quantitative Methods","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-09-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142213383","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Hierarchical novel class discovery for single-cell transcriptomic profiles 单细胞转录组图谱的分级新类别发现

arXiv - QuanBio - Quantitative Methods Pub Date : 2024-09-09 DOI: arxiv-2409.05937

Malek Senoussi, Thierry Artières, Paul Villoutreix

引用次数: 0

Bayesian estimation of transmission networks for infectious diseases 传染病传播网络的贝叶斯估计

arXiv - QuanBio - Quantitative Methods Pub Date : 2024-09-08 DOI: arxiv-2409.05245

Jianing Xu, Huimin Hu, Gregory Ellison, Lili Yu, Christopher Whalen, Liang Liu

{"title":"Bayesian estimation of transmission networks for infectious diseases","authors":"Jianing Xu, Huimin Hu, Gregory Ellison, Lili Yu, Christopher Whalen, Liang Liu","doi":"arxiv-2409.05245","DOIUrl":"https://doi.org/arxiv-2409.05245","url":null,"abstract":"Reconstructing transmission networks is essential for identifying key factors\u0000like superspreaders and high-risk locations, which are critical for developing\u0000effective pandemic prevention strategies. In this study, we developed a\u0000Bayesian framework that integrates genomic and temporal data to reconstruct\u0000transmission networks for infectious diseases. The Bayesian transmission model\u0000accounts for the latent period and differentiates between symptom onset and\u0000actual infection time, enhancing the accuracy of transmission dynamics and\u0000epidemiological models. Additionally, the model allows for the transmission of\u0000multiple pathogen lineages, reflecting the complexity of real-world\u0000transmission events more accurately than models that assume a single lineage\u0000transmission. Simulation results show that the Bayesian model reliably\u0000estimates both the model parameters and the transmission network. Moreover,\u0000hypothesis testing effectively identifies direct transmission events. This\u0000approach highlights the crucial role of genetic data in reconstructing\u0000transmission networks and understanding the origins and transmission dynamics\u0000of infectious diseases.","PeriodicalId":501266,"journal":{"name":"arXiv - QuanBio - Quantitative Methods","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-09-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142213273","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Microsimulation Estimates of Decision Uncertainty and Value of Information Are Biased but Consistent 微观模拟对决策不确定性和信息价值的估计有偏差但一致

arXiv - QuanBio - Quantitative Methods Pub Date : 2024-09-08 DOI: arxiv-2409.05183

Jeremy D. Goldhaber-Fiebert, Hawre Jalal, Fernando Alarid Escudero

{"title":"Microsimulation Estimates of Decision Uncertainty and Value of Information Are Biased but Consistent","authors":"Jeremy D. Goldhaber-Fiebert, Hawre Jalal, Fernando Alarid Escudero","doi":"arxiv-2409.05183","DOIUrl":"https://doi.org/arxiv-2409.05183","url":null,"abstract":"Individual-level state-transition microsimulations (iSTMs) have proliferated\u0000for economic evaluations in place of cohort state transition models (cSTMs).\u0000Probabilistic economic evaluations quantify decision uncertainty and value of\u0000information (VOI). Prior studies show that iSTMs provide unbiased estimates of\u0000expected incremental net monetary benefits (EINMB), but statistical properties\u0000of their estimates of decision uncertainty and VOI are uncharacterized. We\u0000compare such iSTMs-produced estimates to corresponding cSTMs. For a\u00002-alternative decision and normally distributed incremental costs and benefits,\u0000we derive analytical expressions for the probability of being cost-effective\u0000and the expected value of perfect information (EVPI) for cSTMs and iSTMs,\u0000accounting for correlations in incremental outcomes at the population and\u0000individual levels. Numerical simulations illustrate our findings and explore\u0000relaxation of normality assumptions or having >2 decision alternatives. iSTM\u0000estimates of decision uncertainty and VOI are biased but asymptotically\u0000consistent (i.e., bias->0 as number of microsimulated individuals->infinity).\u0000Decision uncertainty depends on one tail of the INMB distribution (e.g.,\u0000P(INMB<=0)) which depends on estimated variance (larger with iSTMs given\u0000first-order noise). While iSTMs overestimate EVPI, their direction of bias for\u0000the probability of being cost-effective is ambiguous. Bias is larger when\u0000uncertainties in incremental costs and effects are negatively correlated. While\u0000more samples at the population uncertainty level are interchangeable with more\u0000microsimulations for estimating EINMB, minimizing iSTM bias in estimating\u0000decision uncertainty and VOI depends on sufficient microsimulations. Analysts\u0000should account for this when allocating their computational budgets and, at\u0000minimum, characterize such bias in their reported results.","PeriodicalId":501266,"journal":{"name":"arXiv - QuanBio - Quantitative Methods","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-09-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142213282","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Efficient Training of Transformers for Molecule Property Prediction on Small-scale Datasets 在小规模数据集上高效训练用于分子性质预测的变换器

arXiv - QuanBio - Quantitative Methods Pub Date : 2024-09-07 DOI: arxiv-2409.04909

Shivesh Prakash

引用次数: 0