PLoS Computational Biology最新文献

{"title":"Robust prediction of drug combination side effects in realistic settings.","authors":"Ruben Jimenez, Alberto Paccanaro","doi":"10.1371/journal.pcbi.1013619","DOIUrl":"10.1371/journal.pcbi.1013619","url":null,"abstract":"<p><p>Side effects caused by drug combinations pose a major challenge in healthcare. Knowledge of these side effects is limited because often they are not detected in clinical trials, which typically involve a restricted number of participants and tested drug combinations. We introduce DCSE (Drug Combinations Side Effects), a novel machine learning method for predicting polypharmacy side effects. DCSE learns latent signatures for drugs, drug pairs, and side effects to predict the probability that a side effect occurs in a given drug combination. We first evaluate its performance in the commonly adopted experimental settings in the literature. However, these rely on balanced testing datasets and sampled negative examples, which do not capture the highly imbalanced and structured set of unknown side effects encountered in practice. Therefore, a key contribution of this paper is the introduction of more realistic experimental settings under prospective evaluations. Here, we attempt to predict side effects reported between 2009 and 2014 after training only on data available prior to that period. These evaluations include warm-start scenarios, in which some side effects are already known for a drug pair, and cold-start scenarios, in which the model predicts side effects for previously uncharacterized drug pairs. Our results indicate that DCSE consistently outperforms state-of-the-art methods, demonstrating its robustness and efficacy in real-world applications.</p>","PeriodicalId":20241,"journal":{"name":"PLoS Computational Biology","volume":"22 10","pages":"e1013619"},"PeriodicalIF":3.6,"publicationDate":"2026-10-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13657403/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149114175","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}

{"title":"Effects of spatial constraints of inhibitory connectivity on the dynamical development of criticality in spiking networks.","authors":"Felix Benjamin Kern, Takahisa Date, Zenas C Chao","doi":"10.1371/journal.pcbi.1014798","DOIUrl":"10.1371/journal.pcbi.1014798","url":null,"abstract":"<p><p>Neural systems are hypothesized to operate near criticality, enhancing their capacity for optimal information processing, transmission and storage capabilities. Criticality has typically been studied in spiking neural networks and related systems organized in random or full connectivity, with the balance of excitation and inhibition being a key determinant of the critical point of the system. However, given that neurons in the brain are spatially distributed, with their distances significantly influencing connectivity and signal timing, it is unclear how the spatial organization of excitatory and inhibitory connectivity influences the network's development towards criticality. Here, we systematically constrain the distance and density of inhibitory connectivity in two-dimensional spiking networks and allow synaptic weights to develop with activity-dependent excitatory and inhibitory plasticity in the presence of a low level of stochastic intrinsic activity. We then investigate the relationship between inhibitory connectivity, synaptic weights, and the resulting network activity during and after development. We find that networks with longer-range inhibitory synapses tend towards more supercritical behavior compared to networks with a similar number of shorter-range inhibitory synapses. We show that this distance dependence is a consequence of weaker long-range synapses after development due to the presence of synaptic delays, which shift most spike pairs outside of the potentiation window of the inhibitory learning rule.</p>","PeriodicalId":20241,"journal":{"name":"PLoS Computational Biology","volume":"22 10","pages":"e1014798"},"PeriodicalIF":3.6,"publicationDate":"2026-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13645236/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149114129","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}

{"title":"Demographic changes and behavioural responses shape vulnerability to infectious disease outbreaks.","authors":"Abbie Evans, William S Hart, Eunok Jung, Kyeongah Nah, Karla Bonic-Babic, Sung-Mok Jung, Robin N Thompson","doi":"10.1371/journal.pcbi.1014319","DOIUrl":"10.1371/journal.pcbi.1014319","url":null,"abstract":"<p><p>Demographic shifts are reshaping population age structures worldwide, with implications for infectious disease dynamics. Since contact patterns, susceptibility and infectiousness often vary by age, the risk that pathogen introductions initiate a substantial outbreak depends on the population's age distribution and associated behavioural characteristics. We develop an age-structured mathematical model to estimate the risk that a single pathogen introduction leads to sustained transmission (the probability of a major outbreak) under long-term demographic transitions, incorporating changes in age-specific contact patterns and behavioural adaptation (specifically, extended workforce participation in an ageing society). Using the Republic of Korea (projected to become the world's oldest population by 2050) as a case study, we show that population ageing may reduce the probability of a major outbreak due to older individuals' lower contact rates. However, this effect is attenuated for pathogens with increasing susceptibility or infectiousness with age, and by factors such as later retirement that increase the number of contacts that older individuals have. These findings demonstrate that, while outbreak risks are affected by demographic changes, they are further modified by associated behavioural responses, highlighting the importance of accounting for demographic and socio-behavioural context when assessing future infectious disease outbreak risks.</p>","PeriodicalId":20241,"journal":{"name":"PLoS Computational Biology","volume":"22 10","pages":"e1014319"},"PeriodicalIF":3.6,"publicationDate":"2026-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13652342/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149114138","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}

{"title":"Mixed updating in structured populations.","authors":"David A Brewster, Yichen Huang, Michael Mitzenmacher, Martin A Nowak","doi":"10.1371/journal.pcbi.1014829","DOIUrl":"10.1371/journal.pcbi.1014829","url":null,"abstract":"<p><p>Evolutionary graph theory (EGT) studies the effect of population structure on evolutionary dynamics. The vertices of the graph represent the N individuals. The edges denote interactions for competitive replacement. Two standard update rules are death-Birth (dB) and Birth-death (Bd). Under dB, an individual is chosen uniformly at random to die, and its neighbors-the individuals on adjacent vertices of the graph- compete to fill the vacancy proportional to their fitness. Under Bd, an individual is chosen for reproduction proportional to fitness, and its offspring replaces a randomly chosen neighbor on an adjacent vertex. Here we study mixed updating between those two scenarios. In each time step, with probability δ the update is dB and with the remaining probability it is Bd. We study fixation probabilities and times as functions of δ under neutral evolution and constant selection. Despite the fact that fixation probabilities and times can be increasing, decreasing, or non-monotonic in δ, we prove that nearly all unweighted undirected graphs have short fixation times and provide an efficient algorithm to estimate their fixation probabilities. Finally, we prove exact formulas for fixation probabilities on cycles, stars, and more complex structures and classify their sensitivities to δ.</p>","PeriodicalId":20241,"journal":{"name":"PLoS Computational Biology","volume":"22 9","pages":"e1014829"},"PeriodicalIF":3.6,"publicationDate":"2026-09-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13645234/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149113943","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}

{"title":"Context-dependent feature modulation shapes human decision policies in approach-avoidance conflicts.","authors":"Sergej A E Golowin, Niall W Duncan, Faizan Shaikh, Christoph W Korn","doi":"10.1371/journal.pcbi.1014793","DOIUrl":"10.1371/journal.pcbi.1014793","url":null,"abstract":"<p><p>Sequential decisions often involve trade-offs between immediate rewards and future risks, particularly in approach-avoidance contexts. While such behavior is commonly framed in terms of optimization principles, it remains unclear how decision processes adapt across contexts. We developed a sequential foraging task in which participants made binary choices under probabilistic reward and predation risk (threat). The design dissociated reward probability from threat and enabled formal comparison of policies based on decision-relevant features (e.g., reward and threat probabilities), a multi-feature policy, and a mathematically optimal policy derived from a fully observable Markov Decision Process. Crucially, the task included two implicitly signaled conditions - approach and avoidance - that differed in how reward and threat information jointly shaped the optimal policy. In both conditions, higher reward probabilities were associated with higher predation risk. However, the balance between these competing factors differed across environments, creating contexts in which the optimal policy either favored approaching or avoiding the higher-threat option. Behavior was analyzed using hierarchical Bayesian models capturing both individual decision features and optimal action values, as well as their modulation by task context. Avoidance contexts selectively altered the weighting of decision features, with reduced reliance on reward probability. In addition, choices showed evidence for increased alignment with optimal state-action values, even after accounting for single feature policies within a shared model. This indicates that behavior more strongly reflected the value structure of the environment under avoidance, consistent with enhanced integration of decision-relevant features. These findings suggest that adaptive behavior in sequential decision-making arises from context-dependent feature reweighting alongside increased alignment with integrated value signals. This provides a parsimonious account of how humans adjust decision-making under threat, without requiring assumptions about discrete strategy shifts.</p>","PeriodicalId":20241,"journal":{"name":"PLoS Computational Biology","volume":"22 9","pages":"e1014793"},"PeriodicalIF":3.6,"publicationDate":"2026-09-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13652344/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149113878","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}

{"title":"GeoEPred: A multimodal structure-aware geometric deep learning framework for Gram-negative bacterial secreted effector prediction with sequence semantics.","authors":"Shouzhen Song, Hua Shi, Hongfeng Wu, Dachen Liu, Yihang Lin, Nor Ashidi Mat Isa, Quan Zou, Leyi Wei","doi":"10.1371/journal.pcbi.1014344","DOIUrl":"10.1371/journal.pcbi.1014344","url":null,"abstract":"<p><p>Accurate prediction of effector proteins secreted by Gram-negative bacteria is important for elucidating bacterial pathogenic mechanisms and developing precise anti-infective strategies. Although existing methods have benefited from the strong sequence feature extraction capacity of pretrained protein language models, reliance on linear sequence information alone often fails to fully capture the three-dimensional conformational signals required for virulence functions. Meanwhile, conventional structure-based methods are limited by the scarcity of experimentally resolved protein structures. To address these challenges, we propose GeoEPred, a multimodal deep learning framework designed for the synergistic modeling of protein sequence and structure to identify Gram-negative bacterial effector proteins. Specifically, the model integrates sequence-contextual embeddings from a pretrained protein language model with three-dimensional structural representations predicted by ESMFold. A feature projection network refines fine-grained sequence signals associated with effector functions, while geometric vector perceptrons characterize inter-residue orientations, distances, and local spatial topology to capture potential structural conformational motifs. To further enable effective cross-modal fusion, we design a cross-modal alignment and feature-tokenized self-attention module. This module enhances consistency between the sequence-semantic and structural-geometric spaces through contrastive learning and models associations between linear functional motifs and spatial conformational patterns at a fine-grained token level. Extensive evaluations on multiple benchmark datasets show that GeoEPred achieves better predictive performance than existing leading models in T3SE, T4SE, and T6SE prediction tasks, while maintaining stable performance in remote homolog recognition scenarios. Moreover, the modular and extensible architecture of GeoEPred demonstrates strong generalization ability and substantial application potential for genome-scale effector protein discovery.</p>","PeriodicalId":20241,"journal":{"name":"PLoS Computational Biology","volume":"22 9","pages":"e1014344"},"PeriodicalIF":3.6,"publicationDate":"2026-09-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13641719/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149113949","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}

{"title":"Granger sensori-behavioral functional taxonomy of neuronal ensemble activity from two-photon calcium imaging data.","authors":"Sahar Khosravi, Nikolas A Francis, Patrick O Kanold, Behtash Babadi","doi":"10.1371/journal.pcbi.1014820","DOIUrl":"10.1371/journal.pcbi.1014820","url":null,"abstract":"<p><p>Understanding how neuronal populations interact to encode and transform sensory information is a fundamental challenge in computational neuroscience. Most existing studies, however, study neural encoding, behavioral readout, and functional connectivity as disjoint problems. Two-photon calcium imaging enables simultaneous recording of large neuronal ensembles in vivo, driven by diverse stimuli and eliciting distinct behaviors. However, extracting directional functional connectivity metrics as well as encoding and readout properties of neurons from such data remains difficult due to indirect and noisy observations of spiking activity, slow temporal dynamics, and the latent interplay between external stimuli and endogenous neural processes. Here, we introduce a unified conceptual and operational modeling and inference framework for directly extracting functional Granger causal (GC) effects between neurons, from external stimuli to neurons, and from neurons to behavior, from two-photon imaging data, in the sense of Granger's temporal predictability. Inspired by the intersection information framework, we also identify neurons that encode features of sensory stimuli that inform behavioral readout. The resulting GC networks together with the taxonomy of functional sensori-behavioral relevance, which we call G-taxonomy, provides a powerful statistical analysis framework, enabled by the integration of several techniques including state-space modeling and inference, variational inference, and point processes. We applied the proposed framework to simulated and experimentally-recorded two-photon imaging from the mouse auditory cortex (A1) during both passive listening and active tone discrimination. Our simulation studies reveal significant improvement of our proposed methodology over existing techniques. Analysis of experimental data from the mouse A1 identifies distinct groups of cells with diverse sensori-behavioral relevance, as well as changes in functional connectivity associated with correct vs. incorrect behavior. In summary, this work provides a principled and data-driven methodology for uncovering directional interactions among the neurons, sensory stimuli, and behavior, all within the same statistical framework, offering new insights into how distributed cortical populations transform sensory inputs into behaviorally relevant representations.</p>","PeriodicalId":20241,"journal":{"name":"PLoS Computational Biology","volume":"22 9","pages":"e1014820"},"PeriodicalIF":3.6,"publicationDate":"2026-09-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13660992/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149114011","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}

{"title":"Modeling decision dynamics disentangles working memory, cognitive control and reinforcement learning and reveals clinical differences.","authors":"Krishn Bera, Alexander Fengler, Megan A Boudewyn, Cameron S Carter, Molly A Erickson, James M Gold, Steven J Luck, J Daniel Ragland, Andrew P Yonelinas, Angus W MacDonald Iii, Deanna M Barch, Michael J Frank","doi":"10.1371/journal.pcbi.1014796","DOIUrl":"10.1371/journal.pcbi.1014796","url":null,"abstract":"<p><p>Various neurocognitive processes contribute to reinforcement learning (RL) and decision making, requiring careful task designs and computational modeling to disentangle them. People rely on capacity-limited working memory (WM) to rapidly and flexibly adapt behavior during learning, together with slower incremental RL processes that support robust long-term retention. The RLWM paradigm was developed to isolate these contributions by manipulating WM demands during learning. Empirical and computational advances support the separability of such processes, but only imperfectly, because a given choice can often equally be attributed to use of WM vs. RL. Here, we show that jointly modeling decision dynamics - accounting not only for choices but also response time distributions, together with hierarchical Bayesian parameter estimation - improves the ability to disentangle separable learning and cognitive control processes. Despite added complexity, models with decision dynamics improved parameter recovery and yielded accurate out-of-sample prediction for choices in a held-out test phase after learning. In contrast, models fit only to choices produced an inflation of RL learning rates and failed the out-of-sample test. Moreover, joint modeling also revealed a novel neurocognitive process by which participants proactively widen decision boundaries to increase response caution under increased WM load. Applying our model to patients with schizophrenia revealed a deficit in proactive control mechanisms as well as slowed incremental RL, which were masked by previous choice-only models, while replicating previously established WM deficits. In summary, we show how joint modeling enables accurate, model-based and mechanism-oriented computational phenotyping of psychopathologies.</p>","PeriodicalId":20241,"journal":{"name":"PLoS Computational Biology","volume":"22 9","pages":"e1014796"},"PeriodicalIF":3.6,"publicationDate":"2026-09-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13660991/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149114040","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}

{"title":"Generative diffusion models for spatiotemporal influenza forecasting.","authors":"Joseph Lemaitre, Justin Lessler","doi":"10.1371/journal.pcbi.1014846","DOIUrl":"10.1371/journal.pcbi.1014846","url":null,"abstract":"<p><p>Forecasting infectious disease incidence can provide important information to guide public health planning, yet it is difficult because epidemic dynamics are complex. Current mechanistic and statistical approaches often struggle to capture multimodal uncertainty or emergent trends. Influpaint adapts denoising diffusion probabilistic models to epidemic forecasting. By encoding influenza seasons as spatiotemporal images in which pixel intensity represents incidence, Influpaint learns a rich distribution of disease dynamics from a hybrid dataset of surveillance and simulated trajectories. Forecasting is formulated as a conditional generation (inpainting) task from partial observations. We show that Influpaint generates realistic, diverse epidemic trajectories and achieves forecast accuracy that is competitive with leading ensemble methods in retrospective evaluation. In real-time evaluation during the 2022-2025 U.S. CDC FluSight challenges, performance improved substantially across seasons, with highly accurate but somewhat overconfident projections in 2024-2025. The best performance was achieved with a training dataset containing 30% surveillance and 70% simulated trajectories. These results show that diffusion models can capture important spatiotemporal structure in influenza dynamics and provide a flexible framework for probabilistic infectious disease forecasting.</p>","PeriodicalId":20241,"journal":{"name":"PLoS Computational Biology","volume":"22 9","pages":"e1014846"},"PeriodicalIF":3.6,"publicationDate":"2026-09-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13649142/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149114009","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}

{"title":"Modelling vasopressin synthesis and storage dynamics during prolonged osmotic challenge and recovery based on activity dependent upregulation of mRNA transcription.","authors":"Duncan J MacGregor","doi":"10.1371/journal.pcbi.1014832","DOIUrl":"10.1371/journal.pcbi.1014832","url":null,"abstract":"<p><p>Hypothalamic vasopressin neurons are neuroendocrine cells which form part of the homeostatic systems that regulate plasma volume and extracellular fluid osmolarity. In response to synaptic inputs encoding osmolarity and changes in plasma volume, they generate complex and dynamically heterogeneous patterns of spikes that in turn trigger secretion of peptide hormone vasopressin from axonal terminals in the posterior pituitary. The thousands of neurons' secretory signals generate a summed plasma vasopressin signal acting at the kidneys to regulate water loss. Vasopressin is synthesised in the neuronal cell bodies, packaged into vesicles, and transported to large stores at the pituitary terminals. Supported by activity-dependent upregulation of synthesis and transport, these stores can maintain a secretion response for several days of elevated osmolarity, experimentally tested by dehydration or salt loading. However, despite upregulated synthesis, stores gradually decline during sustained challenge, followed by a slow recovery. With no evidence of a store encoding feedback signal, previous simple modelling explained these synthesis dynamics based on activity-dependent upregulation of transcription and mRNA content. Here we integrate this idea into a detailed neuronal model, coupling spiking, secretion, and synthesis to simulate a complete neural system, from physiological input to output, and ask how a distributed neuroendocrine system can maintain hormone supply-demand balance over prolonged challenge without direct feedback from distant hormone stores. The model suggests that mRNA acts as a long-timescale memory of neuronal activity, extending temporal integration beyond spike activity and intracellular Ca2+ to coordinate synthesis with sustained demand.</p>","PeriodicalId":20241,"journal":{"name":"PLoS Computational Biology","volume":"22 9","pages":"e1014832"},"PeriodicalIF":3.6,"publicationDate":"2026-09-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13652331/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149114046","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}