Synergistic epistasis among cancer drivers can rescue early tumors from the accumulation of deleterious passengers
by Carla Alejandre, Jorge Calle-Espinosa, Jaime Iranzo
Epistasis among driver mutations is pervasive and explains relevant features of cancer, such as differential therapy response and convergence towards well-characterized molecular subtypes. Furthermore, a growing body of evidence suggests that tumor development could be hampered by the accumulation of slightly deleterious passenger mutations. In this work, we combined empirical epistasis networks, computer simulations, and mathematical models to explore how synergistic interactions among driver mutations affect cancer progression under the burden of slightly deleteriou...
Source: PLoS Computational Biology - April 30, 2024 Category: Biology Authors: Carla Alejandre Source Type: research
UNNT: A novel Utility for comparing Neural Net and Tree-based models
by Vineeth Gutta, Satish Ranganathan Ganakammal, Sara Jones, Matthew Beyers, Sunita Chandrasekaran
The use of deep learning (DL) is steadily gaining traction in scientific challenges such as cancer research. Advances in enhanced data generation, machine learning algorithms, and compute infrastructure have led to an acceleration in the use of deep learning in various domains of cancer research such as drug response problems. In our study, we explored tree-based models to improve the accuracy of a single drug response model and demonstrate that tree-based models such as XGBoost (eXtreme Gradient Boosting) have advantages ov...
Source: PLoS Computational Biology - April 29, 2024 Category: Biology Authors: Vineeth Gutta Source Type: research
Bayesian workflow for time-varying transmission in stratified compartmental infectious disease transmission models
We present a Bayesian workflow for such models, including four features: (1) an adjustment for incomplete case ascertainment, (2) an adequate sampling distribution of laboratory-confirmed cases, (3) a flexible, time-varying transmission rate, and (4) a stratification by age group. Within the workflow, we benchmarked the performance of various implementations of two of these features (2 and 3). For the second feature, we used SARS-CoV-2 data from the canton of Geneva (Switzerland) and found that a quasi-Poisson distribution is the most suitable sampling distribution for describing the overdispersion in the observed laborato...
Source: PLoS Computational Biology - April 29, 2024 Category: Biology Authors: Judith A. Bouman Source Type: research
Ensemble learning and ground-truth validation of synaptic connectivity inferred from spike trains
by Christian Donner, Julian Bartram, Philipp Hornauer, Taehoon Kim, Damian Roqueiro, Andreas Hierlemann, Guillaume Obozinski, Manuel Schr öter
Probing the architecture of neuronal circuits and the principles that underlie their functional organization remains an important challenge of modern neurosciences. This holds true, in particular, for the inference of neuronal connectivity from large-scale extracellular recordings. Despite the popularity of this approach and a number of elaborate methods to reconstruct networks, the degree to which synaptic connections can be reconstructed from spike-train recordings alone remains...
Source: PLoS Computational Biology - April 29, 2024 Category: Biology Authors: Christian Donner Source Type: research
Recurrent neural networks that learn multi-step visual routines with reinforcement learning
We report that networks learn elemental operations, such as contour grouping and visual search, and execute sequences of operat ions, solely based on the characteristics of the visual stimuli and the reward structure of a task. After training was completed, the activity of the units of the neural network elicited by behaviorally relevant image items was stronger than that elicited by irrelevant ones, just as has been observe d in the visual cortex of monkeys solving the same tasks. Relevant information that needed to be exchanged between subroutines was maintained as a focus of enhanced activity and passed on to the subseq...
Source: PLoS Computational Biology - April 29, 2024 Category: Biology Authors: Sami Mollard Source Type: research
Informing policy via dynamic models: Cholera in Haiti
by Jesse Wheeler, AnnaElaine Rosengart, Zhuoxun Jiang, Kevin Tan, Noah Treutle, Edward L. Ionides
Public health decisions must be made about when and how to implement interventions to control an infectious disease epidemic. These decisions should be informed by data on the epidemic as well as current understanding about the transmission dynamics. Such decisions can be posed as statistical questions about scientifically motivated dynamic models. Thus, we encounter the methodological task of building credible, data-informed decisions based on stochastic, partially observed, nonlinear dynamic models. This necessitates addres...
Source: PLoS Computational Biology - April 29, 2024 Category: Biology Authors: Jesse Wheeler Source Type: research
Wagers for work: Decomposing the costs of cognitive effort
by Sarah L. Master, Clayton E. Curtis, Peter Dayan
Some aspects of cognition are more taxing than others. Accordingly, many people will avoid cognitively demanding tasks in favor of simpler alternatives. Which components of these tasks are costly, and how much, remains unknown. Here, we use a novel task design in which subjects request wages for completing cognitive tasks and a computational modeling procedure that decomposes their wages into the costs driving them. Using working memory as a test case, our approach revealed that gating new information into memory and protecting against interference are costly. Critically,...
Source: PLoS Computational Biology - April 29, 2024 Category: Biology Authors: Sarah L. Master Source Type: research
A novel hypergraph model for identifying and prioritizing personalized drivers in cancer
by Naiqian Zhang, Fubin Ma, Dong Guo, Yuxuan Pang, Chenye Wang, Yusen Zhang, Xiaoqi Zheng, Mingyi Wang
Cancer development is driven by an accumulation of a small number of driver genetic mutations that confer the selective growth advantage to the cell, while most passenger mutations do not contribute to tumor progression. The identification of these driver genes responsible for tumorigenesis is a crucial step in designing effective cancer treatments. Although many computational methods have been developed with this purpose, the majority of existing methods solely provided a single driver gene list for the entire cohort of...
Source: PLoS Computational Biology - April 29, 2024 Category: Biology Authors: Naiqian Zhang Source Type: research
Reconstruction Set Test (RESET): A computationally efficient method for single sample gene set testing based on randomized reduced rank reconstruction error
by H. Robert Frost
We have developed a new, and analytically novel, single sample gene set testing method called Reconstruction Set Test (RESET). RESET quantifies gene set importance based on the ability of set genes to reconstruct values for all measured genes. RESET is realized using a computationally efficient randomized reduced rank reconstruction algorithm (available via the RESET R package on CRAN) that can effectively detect patterns of differential abundance and differential correlation for self-contained and competitive scenarios. As demonstrated using real and simulated scRNA-seq data, RESET provides superior pe...
Source: PLoS Computational Biology - April 29, 2024 Category: Biology Authors: H. Robert Frost Source Type: research
Learning spatio-temporal patterns with Neural Cellular Automata
by Alex D. Richardson, Tibor Antal, Richard A. Blythe, Linus J. Schumacher
Neural Cellular Automata (NCA) are a powerful combination of machine learning and mechanistic modelling. We train NCA to learn complex dynamics from time series of images and PDE trajectories. Our method is designed to identify underlying local rules that govern large scale dynamic emergent behaviours. Previous work on NCA focuses on learning rules that give stationary emergent structures. We extend NCA to capture both transient and stable structures within the same system, as well as learning rules that capture the dynamics of Turing pattern forma...
Source: PLoS Computational Biology - April 26, 2024 Category: Biology Authors: Alex D. Richardson Source Type: research
Bayesian inference of structured latent spaces from neural population activity with the orthogonal stochastic linear mixing model
by Rui Meng, Kristofer E. Bouchard
The brain produces diverse functions, from perceiving sounds to producing arm reaches, through the collective activity of populations of many neurons. Determining if and how the features of these exogenous variables (e.g., sound frequency, reach angle) are reflected in population neural activity is important for understanding how the brain operates. Often, high-dimensional neural population activity is confined to low-dimensional latent spaces. However, many current methods fail to extract latent spaces that are clearly structured by exogenous variables. This has contributed to a debate ...
Source: PLoS Computational Biology - April 26, 2024 Category: Biology Authors: Rui Meng Source Type: research
Spatial transcriptome-guided multi-scale framework connects < i > P < /i > . < i > aeruginosa < /i > metabolic states to oxidative stress biofilm microenvironment
by Tracy J. Kuper, Mohammad Mazharul Islam, Shayn M. Peirce-Cottler, Jason A. Papin, Roseanne M Ford
With the generation of spatially resolved transcriptomics of microbial biofilms, computational tools can be used to integrate this data to elucidate the multi-scale mechanisms controlling heterogeneous biofilm metabolism. This work presents a Multi-scale model of Metabolism In Cellular Systems (MiMICS) which is a computational framework that couples a genome-scale metabolic network reconstruction (GENRE) with Hybrid Automata Library (HAL), an existing agent-based model and reaction-diffusion model platform. A key feature o...
Source: PLoS Computational Biology - April 26, 2024 Category: Biology Authors: Tracy J. Kuper Source Type: research
Human decision making balances reward maximization and policy compression
In this study, we present behavioral evidence that humans prefer simpler policies, as predicted by a capacity-limited reinforcement learning model. Across a set of tasks, we find that people exploit structure in the relationships between states, actions, and rewards to “compress” their policies. In particular, compressed policies are systematically biased towards actions with high marginal probability, thereby discarding some state information. This bias is greater when there is redundancy in the reward-maximizing action policy across states, and increases wit h memory load. These results could not be explained qualita...
Source: PLoS Computational Biology - April 26, 2024 Category: Biology Authors: Lucy Lai Source Type: research
Combined multiplex panel test results are a poor estimate of disease prevalence without adjustment for test error
We present novel statistical methods that adjust for bias and quantify uncertainty in prevalence estimates from panel tests, and use simulations to test these methods. As multiplex testing becomes more commonly used for screening in routine clinical practice, accumulation of test error due to the combination of large numbers of test results needs to be identified and corrected for. (Source: PLoS Computational Biology)
Source: PLoS Computational Biology - April 26, 2024 Category: Biology Authors: Robert Challen Source Type: research
Method for cycle detection in sparse, irregularly sampled, long-term neuro-behavioral timeseries: Basis pursuit denoising with polynomial detrending of long-term, inter-ictal epileptiform activity
by Irena Balzekas, Joshua Trzasko, Grace Yu, Thomas J. Richner, Filip Mivalt, Vladimir Sladky, Nicholas M. Gregg, Jamie Van Gompel, Kai Miller, Paul E. Croarkin, Vaclav Kremen, Gregory A. Worrell
Numerous physiological processes are cyclical, but sampling these processes densely enough to perform frequency decomposition and subsequent analyses can be challenging. Mathematical approaches for decomposition and reconstruction of sparsely and irregularly sampled signals are well established but have been under-utilized in physiological applications. We developed a basis pursuit denoising with polynomial detrending (BPWP) mode...
Source: PLoS Computational Biology - April 25, 2024 Category: Biology Authors: Irena Balzekas Source Type: research
