PLoS Computational Biology最新文献_第10页

Ten simple rules for effectively assessing lab environments. 有效评估实验室环境的十条简单规则。

IF 3.8 2区生物学

PLoS Computational Biology Pub Date : 2025-06-24 eCollection Date: 2025-06-01 DOI: 10.1371/journal.pcbi.1013223

Kristen M Naegle, Jeffrey J Saucerman, Julie Leonard-Duke, Michael Rariden, Yonathan Tamrat Aberra

引用次数: 0

MitoTracer facilitates the identification of informative mitochondrial mutations for precise lineage reconstruction. MitoTracer有助于鉴定信息丰富的线粒体突变，用于精确的谱系重建。

IF 3.8 2区生物学

PLoS Computational Biology Pub Date : 2025-06-23 eCollection Date: 2025-06-01 DOI: 10.1371/journal.pcbi.1013090

Xuexin Yu, Jing Hu, Yuhao Tan, Mingyao Pan, Hongyi Zhang, Bo Li

{"title":"MitoTracer facilitates the identification of informative mitochondrial mutations for precise lineage reconstruction.","authors":"Xuexin Yu, Jing Hu, Yuhao Tan, Mingyao Pan, Hongyi Zhang, Bo Li","doi":"10.1371/journal.pcbi.1013090","DOIUrl":"10.1371/journal.pcbi.1013090","url":null,"abstract":"Mitochondrial (MT) mutations serve as natural genetic markers for inferring clonal relationships using single cell sequencing data. However, the fundamental challenge of MT mutation-based lineage tracing is automated identification of informative MT mutations. Here, we introduced an open-source computational algorithm called \"MitoTracer\", which accurately identified clonally informative MT mutations and inferred evolutionary lineage from scRNA-seq or scATAC-seq samples. We benchmarked MitoTracer using the ground-truth experimental lineage sequencing data and demonstrated its superior performance over the existing methods measured by high sensitivity and specificity. MitoTracer is compatible with multiple single cell sequencing platforms. Its application to a cancer evolution dataset revealed the genes related to primary BRAF-inhibitor resistance from scRNA-seq data of BRAF-mutated cancer cells. Overall, our work provided a valuable tool for capturing real informative MT mutations and tracing the lineages among cells.","PeriodicalId":20241,"journal":{"name":"PLoS Computational Biology","volume":"21 6","pages":"e1013090"},"PeriodicalIF":3.8,"publicationDate":"2025-06-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12184895/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144476373","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Synthetic method of analogues for emerging infectious disease forecasting. 新发传染病预测类似物的合成方法。

IF 3.6 2区生物学

PLoS Computational Biology Pub Date : 2025-06-23 eCollection Date: 2025-06-01 DOI: 10.1371/journal.pcbi.1013203

Alexander C Murph, G Casey Gibson, Elizabeth B Amona, Lauren J Beesley, Lauren A Castro, Sara Y Del Valle, Dave Osthus

{"title":"Synthetic method of analogues for emerging infectious disease forecasting.","authors":"Alexander C Murph, G Casey Gibson, Elizabeth B Amona, Lauren J Beesley, Lauren A Castro, Sara Y Del Valle, Dave Osthus","doi":"10.1371/journal.pcbi.1013203","DOIUrl":"10.1371/journal.pcbi.1013203","url":null,"abstract":"The Method of Analogues (MOA) has gained popularity in the past decade for infectious disease forecasting due to its non-parametric nature. In MOA, the local behavior observed in a time series is matched to the local behaviors of several historical time series. The known values that directly follow the historical time series that best match the observed time series are used to calculate a forecast. This non-parametric approach leverages historical trends to produce forecasts without extensive parameterization, making it highly adaptable. However, MOA is limited in scenarios where historical data is sparse. This limitation was particularly evident during the early stages of the COVID-19 pandemic, where the emerging global epidemic had little-to-no historical data. In this work, we propose a new method inspired by MOA, called the Synthetic Method of Analogues (sMOA). sMOA replaces historical disease data with a library of synthetic data that describe a broad range of possible disease trends. This model circumvents the need to estimate explicit parameter values by instead matching segments of ongoing time series data to a comprehensive library of synthetically generated segments of time series data. We demonstrate that sMOA has competitive performance with state-of-the-art infectious disease forecasting models, out-performing 78% of models from the COVID-19 Forecasting Hub in terms of averaged Mean Absolute Error and 76% of models from the COVID-19 Forecasting Hub in terms of averaged Weighted Interval Score. Additionally, we introduce a novel uncertainty quantification methodology designed for the onset of emerging epidemics. Developing versatile approaches that do not rely on historical data and can maintain high accuracy in the face of novel pandemics is critical for enhancing public health decision-making and strengthening preparedness for future outbreaks.","PeriodicalId":20241,"journal":{"name":"PLoS Computational Biology","volume":"21 6","pages":"e1013203"},"PeriodicalIF":3.6,"publicationDate":"2025-06-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12303386/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144476374","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Lateralised memory networks may explain the use of higher-order visual features in navigating insects. 侧向记忆网络可以解释昆虫导航时使用的高阶视觉特征。

IF 3.8 2区生物学

PLoS Computational Biology Pub Date : 2025-06-23 eCollection Date: 2025-06-01 DOI: 10.1371/journal.pcbi.1012670

Giulio Filippi, James Knight, Andrew Philippides, Paul Graham

{"title":"Lateralised memory networks may explain the use of higher-order visual features in navigating insects.","authors":"Giulio Filippi, James Knight, Andrew Philippides, Paul Graham","doi":"10.1371/journal.pcbi.1012670","DOIUrl":"10.1371/journal.pcbi.1012670","url":null,"abstract":"Many insects use memories of their visual environment to adaptively drive spatial behaviours. In ants, visual memories are fundamental for navigation, whereby foragers follow long visually guided routes to foraging sites and return to the location of their nest. Whilst we understand the basic visual pathway to the memory centres (Optic Lobes to Mushroom Bodies) involved in the storage of visual information, it is still largely unknown what type of representation of visual scenes underpins view-based navigation in ants. Several experimental studies have suggested ants use \"higher-order\" visual information - that is features extracted across the whole extent of a visual scene - which raises the question as to how these features might be computed. One such experimental study showed that ants can use the proportion of a shape experienced left of their visual centre to learn and recapitulate a route, a feature referred to as \"fractional position of mass\" (FPM). In this work, we use a simple model constrained by the known neuroanatomy and information processing properties of the Mushroom Bodies to explore whether the apparent use of the FPM could be a resulting factor of the bilateral organisation of the insect brain, all the whilst assuming a simple \"retinotopic\" view representation. We demonstrate that such bilaterally organised memory models can implicitly encode the FPM learned during training. We find that balancing the \"quality\" of the memory match across left and right hemispheres allows a trained model to retrieve the FPM defined direction, even when the model is tested with novel shapes, as demonstrated by ants. The result is shown to be largely independent of model parameter values, therefore suggesting that some aspects of higher-order processing of a visual scene may be emergent from the structure of the neural circuits, rather than computed in discrete processing modules.","PeriodicalId":20241,"journal":{"name":"PLoS Computational Biology","volume":"21 6","pages":"e1012670"},"PeriodicalIF":3.8,"publicationDate":"2025-06-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12225813/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144476372","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Impact of symmetry in local learning rules on predictive neural representations and generalization in spatial navigation. 局部学习规则对称性对空间导航预测神经表征和泛化的影响。

IF 3.8 2区生物学

PLoS Computational Biology Pub Date : 2025-06-23 eCollection Date: 2025-06-01 DOI: 10.1371/journal.pcbi.1013056

Janis Keck, Caswell Barry, Christian F Doeller, Jürgen Jost

{"title":"Impact of symmetry in local learning rules on predictive neural representations and generalization in spatial navigation.","authors":"Janis Keck, Caswell Barry, Christian F Doeller, Jürgen Jost","doi":"10.1371/journal.pcbi.1013056","DOIUrl":"10.1371/journal.pcbi.1013056","url":null,"abstract":"In spatial cognition, the Successor Representation (SR) from reinforcement learning provides a compelling candidate of how predictive representations are used to encode space. In particular, hippocampal place cells are hypothesized to encode the SR. Here, we investigate how varying the temporal symmetry in learning rules influences those representations. To this end, we use a simple local learning rule which can be made insensitive to the temporal order. We analytically find that a symmetric learning rule results in a successor representation under a symmetrized version of the experienced transition structure. We then apply this rule to a two-layer neural network model loosely resembling hippocampal subfields CA3 - with a symmetric learning rule and recurrent weights - and CA1 - with an asymmetric learning rule and no recurrent weights. Here, when exposed repeatedly to a linear track, neurons in our model in CA3 show less shift of the centre of mass than those in CA1, in line with existing empirical findings. Investigating the functional benefits of such symmetry, we employ a simple reinforcement learning agent which may learn symmetric or classical successor representations. Here, we find that using a symmetric learning rule yields representations which afford better generalization, when the agent is probed to navigate to a new target without relearning the SR. This effect is reversed when the state space is not symmetric anymore. Thus, our results hint at a potential benefit of the inductive bias afforded by symmetric learning rules in areas employed in spatial navigation, where there naturally is a symmetry in the state space.","PeriodicalId":20241,"journal":{"name":"PLoS Computational Biology","volume":"21 6","pages":"e1013056"},"PeriodicalIF":3.8,"publicationDate":"2025-06-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12184951/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144476371","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Computations that sustain neural feature selectivity across processing stages. 在处理阶段维持神经特征选择性的计算。

IF 3.8 2区生物学

PLoS Computational Biology Pub Date : 2025-06-20 eCollection Date: 2025-06-01 DOI: 10.1371/journal.pcbi.1013075

Ryan J Rowekamp, Tatyana O Sharpee

{"title":"Computations that sustain neural feature selectivity across processing stages.","authors":"Ryan J Rowekamp, Tatyana O Sharpee","doi":"10.1371/journal.pcbi.1013075","DOIUrl":"10.1371/journal.pcbi.1013075","url":null,"abstract":"Biological visual systems are celebrated for their ability to reliably and precisely recognize objects. However, the specific neural mechanisms responsible for this capability remain largely elusive. In this study, we investigate neural responses in the visual areas V1, V2, and V4 of the brain to natural stimuli using a framework that includes quadratic computations in order to capture local recurrent interactions, both excitatory and suppressive. We find that these quadratic computations and specific coordination between their elements strongly increase both the predictive power of the model and the neural selectivity to natural stimuli. Particularly important were (i) coordination between excitatory and suppressive features to represent mutually exclusive hypotheses regarding incoming stimuli, such as orthogonal orientations or opposing motion directions in area V4, (ii) balance in the contribution of excitatory and suppressive components and its maintenance at similar levels across stages of processing, and (iii) refinement of feature selectivity between stages, with earlier stages representing broader category of inputs. Overall, this work describes how the brain could use multiple nonlinear mechanisms to increase selectivity of neural responses to natural stimuli.","PeriodicalId":20241,"journal":{"name":"PLoS Computational Biology","volume":"21 6","pages":"e1013075"},"PeriodicalIF":3.8,"publicationDate":"2025-06-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12180665/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144336886","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Predicting neuronal firing from calcium imaging using a control theoretic approach. 用控制理论方法预测钙成像中的神经元放电。

IF 3.8 2区生物学

PLoS Computational Biology Pub Date : 2025-06-19 eCollection Date: 2025-06-01 DOI: 10.1371/journal.pcbi.1012603

Nicholas A Rondoni, Fan Lu, Daniel B Turner-Evans, Marcella Gomez

{"title":"Predicting neuronal firing from calcium imaging using a control theoretic approach.","authors":"Nicholas A Rondoni, Fan Lu, Daniel B Turner-Evans, Marcella Gomez","doi":"10.1371/journal.pcbi.1012603","DOIUrl":"10.1371/journal.pcbi.1012603","url":null,"abstract":"Calcium imaging techniques, such as two-photon imaging, have become a powerful tool to explore the functions of neurons and the connectivity of their circuitry. Frequently, fluorescent calcium indicators are taken as a direct measure of neuronal activity. These indicators, however, are slow relative to behavior, obscuring functional relationships between an animal's movements and the true neuronal activity. As a consequence, the firing rate of a neuron is a more meaningful metric. Converting calcium imaging data to the firing of a neuron is nontrivial. Most state-of-the-art methods depend largely on non-mechanistic modeling frameworks such as neural networks, which do not illuminate the underlying chemical exchanges within the neuron, require significant data to be trained on, and cannot be implemented in real-time. Leveraging modeling frameworks from chemical reaction networks (CRN) coupled with a control theoretic approach, a new algorithm is presented leveraging a fully deterministic ordinary differential equation (ODE) model. This framework utilizes model predictive control (MPC) to challenge state-of-the-art correlation scores while retaining interpretability. Furthermore, these computations can be done in real time, thus, enabling online experimentation informed by neuronal firing rates. To demonstrate the use cases of this architecture, it is tested on ground truth datasets courtesy of the spikefinder challenge. Finally, we propose potential applications of the model for guiding experimental design.","PeriodicalId":20241,"journal":{"name":"PLoS Computational Biology","volume":"21 6","pages":"e1012603"},"PeriodicalIF":3.8,"publicationDate":"2025-06-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12194039/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144333783","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

MVHGCN: Predicting circRNA-disease associations with multi-view heterogeneous graph convolutional neural networks. MVHGCN：用多视图异构图卷积神经网络预测circrna与疾病的关联。

IF 3.8 2区生物学

PLoS Computational Biology Pub Date : 2025-06-19 eCollection Date: 2025-06-01 DOI: 10.1371/journal.pcbi.1013225

Yan Miao, Xuan Tang, Chunyu Wang, Zhenyuan Sun, Guohua Wang, Shan Huang

{"title":"MVHGCN: Predicting circRNA-disease associations with multi-view heterogeneous graph convolutional neural networks.","authors":"Yan Miao, Xuan Tang, Chunyu Wang, Zhenyuan Sun, Guohua Wang, Shan Huang","doi":"10.1371/journal.pcbi.1013225","DOIUrl":"10.1371/journal.pcbi.1013225","url":null,"abstract":"Circular RNA, a class of RNA molecules gaining widespread attentions, has been widely recognized as a potential biomarker for many diseases. In recent years, significant progress has been made in the study of the associations between circRNA and diseases. However, traditional experimental methods are often inefficient and costly, making computational models an effective alternative. Nevertheless, existing computational methods still face challenges such as data sparsity and the difficulty of confirming negative samples, which limits the accuracy of predictions. To address these challenges, a novel computational method, namely MVHGCN, is proposed based on multi-view and graph convolutional networks to predict potential associations between circRNA and diseases. MVHGCN first constructs a heterogeneous graph and generates feature descriptors by integrating multiple databases. Then it extracts different connection views of circRNA and diseases through meta-paths, maximizing the utilization of known association information, and aggregates deep feature information through graph convolutional networks. Finally, a MLP is used to predict the association scores. The experimental results show that MVHGCN significantly outperforms existing methods on benchmark datasets by 5-fold cross-validation. This research provides an effective new approach to studying the associations between circRNAs and diseases, capable of alleviating the problem of data sparsity and accurately identifying potential associations.","PeriodicalId":20241,"journal":{"name":"PLoS Computational Biology","volume":"21 6","pages":"e1013225"},"PeriodicalIF":3.8,"publicationDate":"2025-06-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12225982/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144333782","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Correction: A disordered encounter complex is central to the yeast Abp1p SH3 domain binding pathway. 更正：一个无序的相遇复合体是酵母Abp1p SH3结构域结合途径的核心。

IF 3.8 2区生物学

PLoS Computational Biology Pub Date : 2025-06-18 eCollection Date: 2025-06-01 DOI: 10.1371/journal.pcbi.1013201

Gabriella J Gerlach, Rachel Carrock, Robyn Stix, Elliott J Stollar, K Aurelia Ball

引用次数: 0

Embodied decisions as active inference. 作为主动推理的具体化决策。

IF 3.8 2区生物学

PLoS Computational Biology Pub Date : 2025-06-18 eCollection Date: 2025-06-01 DOI: 10.1371/journal.pcbi.1013180

Matteo Priorelli, Ivilin Peev Stoianov, Giovanni Pezzulo

{"title":"Embodied decisions as active inference.","authors":"Matteo Priorelli, Ivilin Peev Stoianov, Giovanni Pezzulo","doi":"10.1371/journal.pcbi.1013180","DOIUrl":"10.1371/journal.pcbi.1013180","url":null,"abstract":"Decision-making is often conceptualized as a serial process, during which sensory evidence is accumulated for the choice alternatives until a certain threshold is reached, at which point a decision is made and an action is executed. This decide-then-act perspective has successfully explained various facets of perceptual and economic decisions in the laboratory, in which action dynamics are usually irrelevant to the choice. However, living organisms often face another class of decisions-called embodied decisions-that require selecting between potential courses of actions to be executed timely in a dynamic environment, e.g., for a lion, deciding which gazelle to chase and how fast to do so. Studies of embodied decisions reveal two aspects of goal-directed behavior in stark contrast to the serial view. First, that decision and action processes can unfold in parallel; second, that action-related components, such as the motor costs associated with selecting a particular choice alternative or required to \"change mind\" between choice alternatives, exert a feedback effect on the decision taken. Here, we show that these signatures of embodied decisions emerge naturally in active inference-a framework that simultaneously optimizes perception and action, according to the same (free energy minimization) imperative. We show that optimizing embodied choices requires a continuous feedback loop between motor planning (where beliefs about choice alternatives guide action dynamics) and motor inference (where action dynamics finesse beliefs about choice alternatives). Furthermore, our active inference simulations reveal the normative character of embodied decisions in ecological settings - namely, achieving an effective balance between a high accuracy and a low risk of missing valid opportunities.","PeriodicalId":20241,"journal":{"name":"PLoS Computational Biology","volume":"21 6","pages":"e1013180"},"PeriodicalIF":3.8,"publicationDate":"2025-06-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12201680/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144326784","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0