pwc-archive/papers-with-abstracts huggingface dataset:
Fast and scalable Wasserstein-1 neural optimal transport solver for single-cell perturbation prediction
Authors: Yanshuo Chen, Zhengmian Hu, Wei Chen, Heng Huang
Date: 2024-11-01 00:00:00

\textbf{Motivation:} Predicting single-cell perturbation responses requires mapping between two unpaired single-cell data distributions. Optimal transport (OT) theory provides a principled framework for constructing such mappings by minimizing transport cost. Recently, Wasserstein-2 ($W_2$) neural optimal transport solvers (\textit{e.g.}, CellOT) have been employed for this prediction task. However, $W_2$ OT relies on the general Kantorovich dual formulation, which involves optimizing over two conjugate functions, leading to a complex min-max optimization problem that converges slowly. \\ \textbf{Results:} To address these challenges, we propose a novel solver based on the Wasserstein-1 ($W_1$) dual formulation. Unlike $W_2$, the $W_1$ dual simplifies the optimization to a maximization problem over a single 1-Lipschitz function, thus eliminating the need for time-consuming min-max optimization. While solving the $W_1$ dual only reveals the transport direction and does not directly provide a unique optimal transport map, we incorporate an additional step using adversarial training to determine an appropriate transport step size, effectively recovering the transport map. Our experiments demonstrate that the proposed $W_1$ neural optimal transport solver can mimic the $W_2$ OT solvers in finding a unique and ``monotonic" map on 2D datasets. Moreover, the $W_1$ OT solver achieves performance on par with or surpasses $W_2$ OT solvers on real single-cell perturbation datasets. Furthermore, we show that $W_1$ OT solver achieves $25 \sim 45\times$ speedup, scales better on high dimensional transportation task, and can be directly applied on single-cell RNA-seq dataset with highly variable genes. \\ \textbf{Availability and Implementation:} Our implementation and experiments are open-sourced at https://github.com/poseidonchan/w1ot.
eDOC: Explainable Decoding Out-of-domain Cell Types with Evidential Learning
Authors: Chaochen Wu, Meiyun Zuo, Lei Xie
Date: 2024-10-30 00:00:00
Tasks: Driver Identification

Single-cell RNA-seq (scRNA-seq) technology is a powerful tool for unraveling the complexity of biological systems. One of essential and fundamental tasks in scRNA-seq data analysis is Cell Type Annotation (CTA). In spite of tremendous efforts in developing machine learning methods for this problem, several challenges remains. They include identifying Out-of-Domain (OOD) cell types, quantifying the uncertainty of unseen cell type annotations, and determining interpretable cell type-specific gene drivers for an OOD case. OOD cell types are often associated with therapeutic responses and disease origins, making them critical for precision medicine and early disease diagnosis. Additionally, scRNA-seq data contains tens thousands of gene expressions. Pinpointing gene drivers underlying CTA can provide deep insight into gene regulatory mechanisms and serve as disease biomarkers. In this study, we develop a new method, eDOC, to address aforementioned challenges. eDOC leverages a transformer architecture with evidential learning to annotate In-Domain (IND) and OOD cell types as well as to highlight genes that contribute both IND cells and OOD cells in a single cell resolution. Rigorous experiments demonstrate that eDOC significantly improves the efficiency and effectiveness of OOD cell type and gene driver identification compared to other state-of-the-art methods. Our findings suggest that eDOC may provide new insights into single-cell biology.
LLaMo: Large Language Model-based Molecular Graph Assistant
Authors: Jinyoung Park, Minseong Bae, Dohwan Ko, Hyunwoo J. Kim
Date: 2024-10-31 00:00:00
Tasks: Instruction Following, IUPAC Name Prediction, Language Modeling, Language Modelling, Large Language Model, model, Molecular description generation, Property Prediction

Large Language Models (LLMs) have demonstrated remarkable generalization and instruction-following capabilities with instruction tuning. The advancements in LLMs and instruction tuning have led to the development of Large Vision-Language Models (LVLMs). However, the competency of the LLMs and instruction tuning have been less explored in the molecular domain. Thus, we propose LLaMo: Large Language Model-based Molecular graph assistant, which is an end-to-end trained large molecular graph-language model. To bridge the discrepancy between the language and graph modalities, we present the multi-level graph projector that transforms graph representations into graph tokens by abstracting the output representations of each GNN layer and motif representations with the cross-attention mechanism. We also introduce machine-generated molecular graph instruction data to instruction-tune the large molecular graph-language model for general-purpose molecule and language understanding. Our extensive experiments demonstrate that LLaMo shows the best performance on diverse tasks, such as molecular description generation, property prediction, and IUPAC name prediction. The code of LLaMo is available at https://github.com/mlvlab/LLaMo.
Correlation of Correlation Networks: High-Order Interactions in the Topology of Brain Networks
Authors: Qiang Li, Jingyu Liu, Vince D. Calhoun
Date: 2024-11-01 00:00:00

To understand collective network behavior in the complex human brain, pairwise correlation networks alone are insufficient for capturing the high-order interactions that extend beyond pairwise interactions and play a crucial role in brain network dynamics. These interactions often reveal intricate relationships among multiple brain networks, significantly influencing cognitive processes. In this study, we explored the correlation of correlation networks and topological network analysis with resting-state fMRI to gain deeper insights into these higher-order interactions and their impact on the topology of brain networks, ultimately enhancing our understanding of brain function. We observed that the correlation of correlation networks highlighted network connections while preserving the topological structure of correlation networks. Our findings suggested that the correlation of correlation networks surpassed traditional correlation networks, showcasing considerable potential for applications in various areas of network science. Moreover, after applying topological network analysis to the correlation of correlation networks, we observed that some high-order interaction hubs predominantly occurred in primary and high-level cognitive areas, such as the visual and fronto-parietal regions. These high-order hubs played a crucial role in information integration within the human brain.
The Dynamics of Triple Interactions in Resting fMRI: Insights into Psychotic Disorders
Authors: Qiang Li, Vince D. Calhoun, Armin Iraji
Date: 2024-11-01 00:00:00
Tasks: Functional Connectivity

The human brain dynamically integrated and configured information to adapt to the environment. To capture these changes over time, dynamic second-order functional connectivity was typically used to capture transient brain patterns. However, dynamic second-order functional connectivity typically ignored interactions beyond pairwise relationships. To address this limitation, we utilized dynamic triple interactions to investigate multiscale network interactions in the brain. In this study, we evaluated a resting-state fMRI dataset that included individuals with psychotic disorders (PD). We first estimated dynamic triple interactions using resting-state fMRI. After clustering, we estimated cohort-specific and cohort-common states for controls (CN), schizophrenia (SZ), and schizoaffective disorder (SAD). From the cohort-specific states, we observed significant triple interactions, particularly among visual, subcortical, and somatomotor networks, as well as temporal and higher cognitive networks in SZ. In SAD, key interactions involved temporal networks in the initial state and somatomotor networks in subsequent states. From the cohort-common states, we observed that high-cognitive networks were primarily involved in SZ and SAD compared to CN. Furthermore, the most significant differences between SZ and SAD also existed in high-cognitive networks. In summary, we studied PD using dynamic triple interaction, the first time such an approach has been used to study PD. Our findings highlighted the significant potential of dynamic high-order functional connectivity, paving the way for new avenues in the study of the healthy and disordered human brain.
Modelling Alzheimer's Protein Dynamics: A Data-Driven Integration of Stochastic Methods, Machine Learning and Connectome Insights
Authors: Alec MacIver, Hina Shaheen
Date: 2024-11-04 00:00:00
Tasks: Bayesian Inference

Alzheimer's disease (AD) is a complex neurodegenerative disorder characterized by the progressive accumulation of misfolded proteins, leading to cognitive decline. This study presents a novel stochastic modelling approach to simulate the propagation of these proteins within the brain. We employ a network diffusion model utilizing the Laplacian matrix derived from MRI data provided by the Human Connectome Project (https://braingraph.org/cms/). The deterministic model is extended by incorporating stochastic differential equations (SDEs) to account for inherent uncertainties in disease progression. Introducing stochastic components into the model allows for a more realistic simulation of the disease due to the multi-factorial nature of AD. By simulation, the model captures the variability in misfolded protein concentration across brain regions over time. Bayesian inference is a statistical method that uses prior beliefs and given data to model a posterior distribution for relevant parameter values. This allows us to better understand the impact of noise and external factors on AD progression. Deterministic results suggest that AD progresses at different speeds within each lobe of the brain, moreover, the frontal takes the longest to reach a perfect disease state. We find that in the presence of noise, the model never reaches a perfect disease state and the later years of AD are more unpredictable than earlier on in the disease. These results highlight the importance of integrating stochastic elements into deterministic models to achieve more realistic simulations, providing valuable insights for future studies on the dynamics of neurodegenerative diseases.
Why Does the Cortex Have Such a Vast Storage Capacity?
Authors: Hui Wei, Surun Yang, Yangwang Li
Date: 2024-11-02 00:00:00
Tasks: Form

The capacity of long-term memory seems to be extremely large, capable of storing information spanning almost a lifetime. Why does it have such a vast capacity? Why are some memories so enduring? What is the actual physical form of long-term memory? In the movie Inside Out, it is depicted as individual orbs containing information. Is that really the case? Simply explaining this by saying that the cortex has many neurons, numerous neural connections, and complex electrochemical activity between them is not sufficient to answer these fundamental questions. We need to uncover the theory hidden behind these phenomena.In essence, a neural network is equivalent to a very large directed graph, with a massive number of nodes and directed connections. This paper posits that the physical form of long-term memory is a connected subgraph within this complex directed graph. This subgraph is capable of linking together the disparate fragments of the same event, spread across different sensory cortices, to form associations. This provides a physical realization of the engram theory. The robustness of the connected subgraph and the resources it consumes can explain various memory behaviors.Based on anatomical, brain imaging and electrophysiological evidence, this paper constructs a probabilistic connectivity model and uses theorems from graph theory to prove the ease of constructing connected subgraphs. Finally, it explains why the potential capacity for memory is immense.
Beyond networks, towards adaptive systems
Authors: Luiz Pessoa
Date: 2024-11-06 00:00:00

Despite their widespread utility across domains, basic network models face fundamental limitations when applied to complex biological systems, particularly in neuroscience. This paper critically examines these limitations and explores potential extensions and alternative frameworks better suited to capture the adaptive nature of biological systems. Key challenges include: the need to account for time-varying connections and adaptive topologies; the difficulty in representing multilevel systems with cross-level interactions; the inadequacy of fixed state spaces for systems that continuously expand their range of possible states; and the challenge of modeling deep history dependence and open-world interactions. While some limitations can be partially addressed through extensions like multilayer networks and time-varying connections, I argue that biological systems exhibit radical context dependence and open-endedness that resist conventional mathematical formalization. Using examples primarily from neuroscience, I demonstrate how even sophisticated approaches incorporating dynamics and context sensitivity fall short in fully capturing biological complexity. Moving forward requires developing frameworks that explicitly account for adaptive reconfiguration, embrace open-ended state spaces, and draw inspiration from concepts like Kauffman's "adjacent possible." The path ahead demands mathematical and computational approaches that are fundamentally more dynamic and flexible than traditional network models.
Curiosity-Driven Science: The in Situ Jungle Biomechanics Lab in the Amazon Rainforest
Authors: S. David Stupski, Laura Casas Ferrer, Jacob S. Harrison, Justina Jackson, Carolina Paucarhuanca Mansilla, Loribeth Maricielo Bolo Livano, Avaneesh Narla, Chew Chai, Elizabeth Clark, Nami Ha, Jaime Quispe Nina, Ethan Wold, Johana Reyes-Quinteros, Geoffrey Gallice, Saad Bhamla
Date: 2024-11-05 00:00:00

Field work is an essential component not just for organismal biology, but also for the expanding umbrella of disciplines which have turned their attention towards living systems. Observing organisms in naturalistic contexts is a critical component of discovery; however, conducting field research can be a massive barrier for scientists who do not have experience working with organisms in a naturalistic context under challenging field conditions. Here we propose 8 critical steps for organizing and executing interdisciplinary curiosity-driven field research, drawing on the insights from The in Situ Jungle Biomechanics Lab (JBL). The JBL program is a field research course that helps early-career scientists gain experience in organizing and conducting interdisciplinary field research. JBL uses a curiosity-driven approach to field science education by encouraging early-career researchers to explore scientific questions in the Peruvian Amazon with a non-prescriptive approach to research output. We achieve an inclusive research space by bringing scientists from across disciplines together, with local communities to collaborate and spark new questions and ideas. To stoke curiosity, the JBL imparts a naturalist tradition set forth by organismal biologists of the 20th century who have extolled the merits of observing the natural world as a form of scientific exploration.
Evolutionary features in a minimal physical system: directionality, diversity, selection, growth, inheritance, and adaptation
Authors: Guy Bunin, Olivier Rivoire
Date: 2024-11-06 00:00:00
Tasks: Diversity

We present a simple physical model that recapitulates several features of biological evolution, despite being based only on thermally-driven attachment and detachment of elementary building blocks. Through its dynamics, this model samples a large and diverse array of non-equilibrium steady states, both within and between independent trajectories. These dynamics exhibit directionality with a quantity that increases in time, selection and preferential spatial expansion of particular states, as well as inheritance in the form of correlated compositions between successive states, and environment-dependent adaptation. The model challenges common conceptions regarding the requirements for life-like properties: it does not involve separate mechanisms for metabolism, replication and compartmentalization, stores and transmits digital information without template replication or assembly of large molecules, exhibits selection both without and with reproduction, and undergoes growth without autocatalysis. As the model is based on generic physical principles, it is amenable to various experimental implementations.
A Temporal Playbook for Multiple Wave Dengue Pandemic from Latin America and Asia to Italy
Authors: Alessandra D'Alise, Davide Iacobacci, Francesco Sannino
Date: 2024-11-06 00:00:00

We show that the epidemiological Renormalization Group (eRG) framework is a useful and minimal tool to effectively describe the temporal evolution of the Dengue multi-wave pandemics. We test the framework on the Dengue history of several countries located in both Latin America and Asia. We also observe a strong correlation between the total number of infected individuals and the changes in the local temperature. Our results further support the expectation that global warming is bound to increase the cases of Dengue worldwide. We then move to investigate, via the eRG, the recent outbreak in Fano, Italy and offer our projections.
The co-evolution of direct, indirect and generalized reciprocity
Authors: Saptarshi Pal, Christian Hilbe, Nikoleta E Glynatsi
Date: 2024-11-05 00:00:00

People often engage in costly cooperation, especially in repeated interactions. When deciding whether to cooperate, individuals typically take into account how others have acted in the past. For instance, when one person is deciding whether to cooperate with another, they may consider how they were treated by the other party (direct reciprocity), how the other party treated others (indirect reciprocity), or how they themselves were treated by others in general (generalized reciprocity). Given these different approaches, it is unclear which strategy, or more specifically which mode of reciprocity, individuals will prefer. This study introduces a model where individuals decide how much weight to give each type of information when choosing to cooperate. Through equilibrium analysis, we find that all three modes of reciprocity can be sustained when individuals have sufficiently frequent interactions. However, the existence of such equilibria does not guarantee that individuals will learn to use them. Simulations show that when individuals mainly imitate others, generalized reciprocity often hinders cooperation, leading to defection even under conditions favorable to cooperation. In contrast, when individuals explore new strategies during learning, stable cooperation emerges through direct reciprocity. This study highlights the importance of studying all forms of reciprocity in unison.
Phenotypic heterogeneity in temporally fluctuating environments
Authors: Alexander P Browning, Sara Hamis
Date: 2024-11-05 00:00:00

Many biological systems regulate phenotypic heterogeneity as a fitness-maximising strategy in uncertain and dynamic environments. Analysis of such strategies is typically confined both to a discrete set of environmental conditions, and to a discrete (often binary) set of phenotypes specialised to each condition. In this work, we extend theory on both fronts to encapsulate both a discrete and continuous spectrum of phenotypes arising in response to two broad classes of environmental efluctuations that drive changes in the phenotype-dependent growth rates; specifically, stochastic environments that are temporally uncorrelated (specifically, white-noise processes) and correlated (specifically, Poisson and Ornstein-Uhlenbeck processes). For tractability, we restrict analysis to an exponential growth model, and consider biologically relevant simplifications that pertain to the relative timescale of phenotype switching. These assumptions yield a series of analytical and semi-analytical expressions that reveal environments in which both discrete and continuous phenotypic heterogeneity is evolutionary advantageous.
How oscillations in SIRS epidemic models are affected by the distribution of immunity times
Authors: Daniel Henrik Nevermann, Claudius Gros
Date: 2024-11-04 00:00:00

Models for resident infectious diseases, like the SIRS model, may settle into an endemic state with constant numbers of susceptible ($S$), infected ($I$) and recovered ($R$) individuals, where recovered individuals attain a temporary immunity to reinfection. For many infectious pathogens, infection dynamics may also show periodic outbreaks corresponding to a limit cycle in phase space. One way to reproduce oscillations in SIRS models is to include a non-exponential dwell-time distribution in the recovered state. Here, we study a SIRS model with a step-function-like kernel for the immunity time, mapping out the model's full phase diagram. Using the kernel series framework, we are able to identify the onset of periodic outbreaks when successively broadening the step-width. We further investigate the shape of the outbreaks, finding that broader steps cause more sinusoidal oscillations while more uniform immunity time distributions are related to sharper outbreaks occurring after extended periods of low infection activity. Our main results concern recovery distributions characterized by a single dominant timescale. We also consider recovery distributions with two timescales, which may be observed when two or more distinct recovery processes co-exist. Surprisingly, two qualitatively different limit cycles are found to be stable in this case, with only one of the two limit cycles emerging via a standard supercritical Hopf bifurcation.
Independence in Integrated Population Models
Authors: Frédéric Barraquand
Date: 2024-11-04 00:00:00

Integrated population models (IPMs) combine multiple ecological data types such as capture-mark-recapture histories, reproduction surveys, and population counts into a single statistical framework. In such models, each data type is generated by a probabilistic submodel, and an assumption of independence between the different data types is usually made. The fact that the same biological individuals can contribute to multiple data types has been perceived as affecting their independence, and several studies have even investigated IPM robustness in this scenario. However, what matters from a statistical perspective is probabilistic independence: the joint probability of observing all data is equal to the product of the likelihoods of the various datasets. Contrary to a widespread perception, probabilistic non-independence does not automatically result from collecting data on the same physical individuals. Conversely, while there can be good reasons for non-independence of IPM submodels arising from sharing of individuals between data types, these relations do not seem to be included in IPMs whose robustness is being investigated. Furthermore, conditional rather than true independence is sometimes assumed. In this conceptual paper, I survey the various independence concepts used in IPMs, try to make sense of them by getting back to first principles in toy models, and show that it is possible to obtain probabilistic independence (or near-independence) despite two or three data types collected on the same set of biological individuals. I then revisit recommendations pertaining to component data collection and IPM robustness checks, and provide some suggestions to bridge the current gap between individual-level IPMs and their population-level approximations using composite likelihoods.