pwc-archive/papers-with-abstracts huggingface dataset:
Generalized FGM dependence: Geometrical representation and convex bounds on sums
Authors: Hélène Cossette, Etienne Marceau, Alessandro Mutti, Patrizia Semeraro
Date: 2024-06-15 00:00:00

Building on the one-to-one relationship between generalized FGM copulas and multivariate Bernoulli distributions, we prove that the class of multivariate distributions with generalized FGM copulas is a convex polytope. Therefore, we find sharp bounds in this class for many aggregate risk measures, such as value-at-risk, expected shortfall, and entropic risk measure, by enumerating their values on the extremal points of the convex polytope. This is infeasible in high dimensions. We overcome this limitation by considering the aggregation of identically distributed risks with generalized FGM copula specified by a common parameter $p$. In this case, the analogy with the geometrical structure of the class of Bernoulli distribution allows us to provide sharp analytical bounds for convex risk measures.
Protein-Nucleic Acid Complex Modeling with Frame Averaging Transformer
Authors: Tinglin Huang, Zhenqiao Song, Rex Ying, Wengong Jin
Date: 2024-06-13 00:00:00

Nucleic acid-based drugs like aptamers have recently demonstrated great therapeutic potential. However, experimental platforms for aptamer screening are costly, and the scarcity of labeled data presents a challenge for supervised methods to learn protein-aptamer binding. To this end, we develop an unsupervised learning approach based on the predicted pairwise contact map between a protein and a nucleic acid and demonstrate its effectiveness in protein-aptamer binding prediction. Our model is based on FAFormer, a novel equivariant transformer architecture that seamlessly integrates frame averaging (FA) within each transformer block. This integration allows our model to infuse geometric information into node features while preserving the spatial semantics of coordinates, leading to greater expressive power than standard FA models. Our results show that FAFormer outperforms existing equivariant models in contact map prediction across three protein complex datasets, with over 10% relative improvement. Moreover, we curate five real-world protein-aptamer interaction datasets and show that the contact map predicted by FAFormer serves as a strong binding indicator for aptamer screening.
skandiver: a divergence-based analysis tool for identifying intercellular mobile genetic elements
Authors: Xiaolei Brian Zhang, Grace Oualline, Jim Shaw, Yun William Yu
Date: 2024-06-17 00:00:00

Mobile genetic elements (MGEs) are as ubiquitous in nature as they are varied in type, ranging from viral insertions to transposons to incorporated plasmids. Horizontal transfer of MGEs across bacterial species may also pose a significant threat to global health due to their capability to harbour antibiotic resistance genes. However, despite cheap and rapid whole genome sequencing, the varied nature of MGEs makes it difficult to fully characterize them, and existing methods for detecting MGEs often don't agree on what should count. In this manuscript, we first define and argue in favor of a divergence-based characterization of mobile-genetic elements. Using that paradigm, we present skandiver, a tool designed to efficiently detect MGEs from whole genome assemblies without the need for gene annotation or markers. skandiver determines mobile elements via genome fragmentation, average nucleotide identity (ANI), and divergence time. By building on the scalable skani software for ANI computation, skandiver can query hundreds of complete assemblies against $>$65,000 representative genomes in a few minutes and 19 GB memory, providing scalable and efficient method for elucidating mobile element profiles in incomplete, uncharacterized genomic sequences. For isolated and integrated large plasmids (>10kbp), skandiver's recall was 48\% and 47\%, MobileElementFinder was 59\% and 17\%, and geNomad was 86\% and 32\%, respectively. For isolated large plasmids, skandiver's recall (48\%) is lower than state-of-the-art reference-based methods geNomad (86\%) and MobileElementFinder (59\%). However, skandiver achieves higher recall on integrated plasmids and, unlike other methods, without comparing against a curated database, making skandiver suitable for discovery of novel MGEs. Availability: https://github.com/YoukaiFromAccounting/skandiver
AOC: Analysis of Orthologous Collections -- an application for the characterization of natural selection in protein-coding sequences
Authors: Alexander Lucaci, Sergei Pond
Date: 2024-06-13 00:00:00

Motivation Modern molecular sequence analysis increasingly relies on automated and robust software tools for interpretation, annotation, and biological insight. The Analysis of Orthologous Collections (AOC) application automates the identification of genomic sites and species/lineages influenced by natural selection in coding sequence analysis. AOC quantifies different types of selection: negative, diversifying or directional positive, or differential selection between groups of branches. We include all steps necessary to go from unaligned homologous sequences to complete results and interactive visualizations that are designed to aid in the useful interpretation and contextualization. Results We are motivated by a desire to make evolutionary analyses as simple as possible, and to close the disparity in the literature between genes which draw a significant amount of interest and those that are largely overlooked and underexplored. We believe that such underappreciated and understudied genetic datasets can hold rich biological information and offer substantial insights into the diverse patterns and processes of evolution, especially if domain experts are able to perform the analyses themselves. Availability and implementation A Snakemake [M\"older et al., 2021] application implementation is publicly available on GitHub at https://github.com/aglucaci/AnalysisOfOrthologousCollections and is accompanied by software documentation and a tutorial.
Computing the Frequency Response of Biochemical Networks: A Python module
Authors: Herbert M Sauro
Date: 2024-06-17 00:00:00

In this paper, a set of Python methods is described that can be used to compute the frequency response of an arbitrary biochemical network given any input and output. Models can be provided in standard SBML or Antimony format. The code takes into account any conserved moieties so that this software can be used to also study signaling networks where moiety cycles are common. A utility method is also provided to make it easy to plot standard Bode plots from the generated results. The code also takes into account the possibility that the phase shift could exceed 180 degrees which can result in ugly discontinuities in the Bode plot. In the paper, some of the theory behind the method is provided as well as some commentary on the code and several illustrative examples to show the code in operation. Illustrative examples include linear reaction chains of varying lengths and the effect of negative feedback on the frequency response. Software License: MIT Open Source Availability: The code is available from https://github.com/sys-bio/frequencyResponse.
Reproducibility of predictive networks for mouse visual cortex
Authors: Polina Turishcheva, Max Burg, Fabian H. Sinz, Alexander Ecker
Date: 2024-06-18 00:00:00
Tasks: Clustering

Deep predictive models of neuronal activity have recently enabled several new discoveries about the selectivity and invariance of neurons in the visual cortex. These models learn a shared set of nonlinear basis functions, which are linearly combined via a learned weight vector to represent a neuron's function. Such weight vectors, which can be thought as embeddings of neuronal function, have been proposed to define functional cell types via unsupervised clustering. However, as deep models are usually highly overparameterized, the learning problem is unlikely to have a unique solution, which raises the question if such embeddings can be used in a meaningful way for downstream analysis. In this paper, we investigate how stable neuronal embeddings are with respect to changes in model architecture and initialization. We find that $L_1$ regularization to be an important ingredient for structured embeddings and develop an adaptive regularization that adjusts the strength of regularization per neuron. This regularization improves both predictive performance and how consistently neuronal embeddings cluster across model fits compared to uniform regularization. To overcome overparametrization, we propose an iterative feature pruning strategy which reduces the dimensionality of performance-optimized models by half without loss of performance and improves the consistency of neuronal embeddings with respect to clustering neurons. This result suggests that to achieve an objective taxonomy of cell types or a compact representation of the functional landscape, we need novel architectures or learning techniques that improve identifiability. We will make our code available at publication time.
Mining comorbidities: a brief survey
Authors: Giovanna Maria Dimitri
Date: 2024-06-15 00:00:00
Tasks: Survey

In this manuscript we will present a brief overview of the comorbidity concept. We will start by laying its foundations and its definitions and then describing the role that machine learning can hold in mining and defining it. The purpose of this short survey is to present a brief overview of the definition of comorbidity as a concept, and showing some of the latest applications and potentialities for the application of natural language processing and text mining techniques.
Nursery function rehabilitation projects in port areas can support fish populations but they remain less effective than ensuring compliance to fisheries management
Authors: Etienne Joubert, Charlotte Sève, Stéphanie Mahévas, Adrian Bach, Marc Bouchoucha
Date: 2024-06-13 00:00:00
Tasks: Management

Conservation measures are implemented to support biodiversity in areas that are degraded or under anthropogenic pressure. Over the past decade, numerous projects aimed at rehabilitating a fish nursery function in ports, through the installation of artificial structures, have emerged. While studies conducted on these solutions seem promising on a very local scale (e.g., higher densities of juvenile fish on artificial fish nurseries compared to bare port infrastructures), no evaluation has been undertaken yet to establish their contribution to the renewal of coastal fish populations or their performance compared to other conservation measures such as fishing regulation. Here, we used a coupled model of fish population dynamics and fisheries management, ISIS-fish, to describe the coastal commercial fish population, the white seabream (Diplodus sargus) in the highly artificialized Bay of Toulon. Using ISIS-Fish, we simulated rehabilitation and fisheries management scenarios. We provided the first quantitative assessment of the implementation of artificial structures in ports covering 10% and 100% of the available port area and compared, at population level and fishing fleets level, the quantitative consequences of these rehabilitation measures with fishing control measures leading to strict compliance with minimum catch sizes. The rehabilitation of the nursery function in ports demonstrated a potential to enhance the renewal of fish populations and catches. When the size of projects is small the outcomes they provide remain relatively modest in contrast to the impact of regulatory fishing measures. However, we have demonstrated that combining fishing reduction measures and rehabilitation projects has a synergistic effect on fish populations, resulting in increased populations and catches. This study is the first quantitative assessment of fish nursery rehabilitation projects in port areas, by evaluating their effectiveness in renewing coastal fish populations and fisheries and comparing their outcomes with fishing control measures. Small-scale port-area nursery rehabilitation projects can support fish populations, but are less effective than controlling fisheries.
Fast uncovering of protein sequence diversity from structure
Authors: Luca Alessandro Silva, Barthelemy Meynard-Piganeau, Carlo Lucibello, Christoph Feinauer
Date: 2024-06-17 00:00:00
Tasks: Diversity, Protein Design

We present InvMSAFold, an inverse folding method for generating protein sequences that is optimized for diversity and speed. For a given structure, InvMSAFold generates the parameters of a probability distribution over the space of sequences with pairwise interactions, capturing the amino acid covariances observed in Multiple Sequence Alignments (MSA) of homologous proteins. This allows for the efficient generation of highly diverse protein sequences while preserving structural and functional integrity. We show that this increased diversity in sampled sequences translates into greater variability in biochemical properties, highlighting the exciting potential of our method for applications such as protein design. The orders of magnitude improvement in sampling speed compared to existing methods unlocks new possibilities for high-throughput virtual screening.
Increasing certainty in systems biology models using Bayesian multimodel inference
Authors: Nathaniel Linden-Santangeli, Jin Zhang, Boris Kramer, Padmini Rangamani
Date: 2024-06-17 00:00:00
Tasks: Model Selection

Mathematical models are indispensable to the system biology toolkit for studying the structure and behavior of intracellular signaling networks. A common approach to modeling is to develop a system of equations that encode the known biology using approximations and simplifying assumptions. As a result, the same signaling pathway can be represented by multiple models, each with its set of underlying assumptions, which opens up challenges for model selection and decreases certainty in model predictions. Here, we use Bayesian multimodel inference to develop a framework to increase certainty in systems biology models. Using models of the extracellular regulated kinase (ERK) pathway, we first show that multimodel inference increases predictive certainty and yields predictors that are robust to changes in the set of available models. We then show that predictions made with multimodel inference are robust to data uncertainties introduced by decreasing the measurement duration and reducing the sample size. Finally, we use multimodel inference to identify a new model to explain experimentally measured sub-cellular location-specific ERK activity dynamics. In summary, our framework highlights multimodel inference as a disciplined approach to increasing the certainty of intracellular signaling activity predictions.
Improved absolute abundance estimates from spatial count data with simulation and microfossil case studies
Authors: Chris Mays, Marcos Amores, Anthony Mays
Date: 2024-06-16 00:00:00

Many fundamental parameters of biological systems -- eg. productivity, population sizes and biomass -- are most effectively expressed in absolute terms. In contrast to proportional data (eg. percentages), absolute values provide standardised metrics on the functioning of biological entities (eg. organisism, species, ecosystems). These are particularly valuable when comparing assemblages across time and space. Since it is almost always impractical to count entire populations, estimates of population abundances require a sampling method that is both accurate and precise. Such absolute abundance estimates typically entail more "sampling effort" (data collection time) than proportional data. Here we refined a method of absolute abundance estimates -- the "exotic marker technique" -- by producing a variant that is more efficient without losing accuracy. This new method, the "field-of-view subsampling method" (FOVS method) is based on area subsampling, from which large samples can be quickly extrapolated. Two case studies of the exotic marker technique were employed: 1, computer simulations; and 2, an observational "real world" data set of terrestrial organic microfossils from the Permian- and Triassic-aged rock strata of southeastern Australia, spiked with marker grains of known quantity and variance. We compared the FOVS method against the traditional "linear method" method using three metrics: 1, concentration (specimens/gram of sediment); 2, precision and 3, data collection effort. In almost all cases, the FOVS method delivers higher precision than the linear method, with equivalent effort, and our computer simulations suggest that the FOVS method more accurately estimates the true error for large target-to-marker ratios. Since we predict that these conditions are typically common, we recommend the new FOVS method in almost every "real world" case.
Horizon-wise Learning Paradigm Promotes Gene Splicing Identification
Authors: Qi-Jie Li, Qian Sun, Shao-Qun Zhang
Date: 2024-06-15 00:00:00

Identifying gene splicing is a core and significant task confronted in modern collaboration between artificial intelligence and bioinformatics. Past decades have witnessed great efforts on this concern, such as the bio-plausible splicing pattern AT-CG and the famous SpliceAI. In this paper, we propose a novel framework for the task of gene splicing identification, named Horizon-wise Gene Splicing Identification (H-GSI). The proposed H-GSI follows the horizon-wise identification paradigm and comprises four components: the pre-processing procedure transforming string data into tensors, the sliding window technique handling long sequences, the SeqLab model, and the predictor. In contrast to existing studies that process gene information with a truncated fixed-length sequence, H-GSI employs a horizon-wise identification paradigm in which all positions in a sequence are predicted with only one forward computation, improving accuracy and efficiency. The experiments conducted on the real-world Human dataset show that our proposed H-GSI outperforms SpliceAI and achieves the best accuracy of 97.20\%. The source code is available from this link.
Multimodal Radiomics Model for Predicting Gold Nanoparticles Accumulation in Mouse Tumors
Authors: Jiajia Tang, Jie Zhang, Jiulou Zhang, Yuxia Tang, Hao Ni, Shouju Wang
Date: 2024-06-14 00:00:00
Tasks: Diagnostic

Background: Nanoparticles can accumulate in solid tumors, serving as diagnostic or therapeutic agents for cancer. Clinical translation is challenging due to low accumulation in tumors and heterogeneity between tumor types and individuals. Tools to identify this heterogeneity and predict nanoparticle accumulation are needed. Advanced imaging techniques combined with radiomics and AI may offer a solution. Methods: 183 mice were used to create seven subcutaneous tumor models, with three sizes (15nm, 40nm, 70nm) of gold nanoparticles injected via the tail vein. Accumulation was measured using ICP-OES. Data were divided into training and test sets (7:3). Tumors were categorized into high and low uptake groups based on the median value of the training set. Before injection, multimodal imaging data (CT, B-mode ultrasound, SWE, CEUS) were acquired, and radiomics features extracted. LASSO and RFE algorithms built a radiomics signature. This, along with tumor type and mean values from CT and SWE, constructed the best model using SVM. For each tumor in the test set, the radiomics signature predicted gold nanoparticle uptake. Model performance was evaluated by AUC. Results: Significant variability in gold nanoparticle accumulation was observed among tumors (P < 0.001). The median accumulation in the training set was 3.37% ID/g. Nanoparticle size was not a main determinant of uptake (P > 0.05). The composite model based on radiomics signature outperformed the basic model in both training (AUC 0.93 vs. 0.68) and testing (0.78 vs. 0.61) datasets. Conclusion: The composite model identifies tumor heterogeneity and predicts high uptake of gold nanoparticles, improving patient stratification and supporting nanomedicine's clinical application.
A Calibration Approach for Elasticity Estimation with Medical Tools
Authors: Sarah Grube, Maximilian Neidhardt, Anna-Katarina Herrmann, Johanna Sprenger, Kian Abdolazizi, Sarah Latus, Christian J. Cyron, Alexander Schlaefer
Date: 2024-06-14 00:00:00

Soft tissue elasticity is directly related to different stages of diseases and can be used for tissue identification during minimally invasive procedures. By palpating a tissue with a robot in a minimally invasive fashion force-displacement curves can be acquired. However, force-displacement curves strongly depend on the tool geometry which is often complex in the case of medical tools. Hence, a tool calibration procedure is desired to directly map force-displacement curves to the corresponding tissue elasticity.We present an experimental setup for calibrating medical tools with a robot. First, we propose to estimate the elasticity of gelatin phantoms by spherical indentation with a state-of-the-art contact model. We estimate force-displacement curves for different gelatin elasticities and temperatures. Our experiments demonstrate that gelatin elasticity is highly dependent on temperature, which can lead to an elasticity offset if not considered. Second, we propose to use a more complex material model, e.g., a neural network, that can be trained with the determined elasticities. Considering the temperature of the gelatin sample we can represent different elasticities per phantom and thereby increase our training data.We report elasticity values ranging from 10 to 40 kPa for a 10% gelatin phantom, depending on temperature.
Fees in AMMs: A quantitative study
Authors: Abe Alexander, Lars Fritz
Date: 2024-06-18 00:00:00

In the ever evolving landscape of decentralized finance automated market makers (AMMs) play a key role: they provide a market place for trading assets in a decentralized manner. For so-called bluechip pairs, arbitrage activity provides a major part of the revenue generation of AMMs but also a major source of loss due to the so-called 'informed orderflow'. Finding ways to minimize those losses while still keeping uninformed trading activity alive is a major problem in the field. In this paper we will investigate the mechanics of said arbitrage and try to understand how AMMs can maximize the revenue creation or in other words minimize the losses. To that end, we model the dynamics of arbitrage activity for a concrete implementation of a pool and study its sensitivity to the choice of fee aiming to maximize the revenue for the AMM. We identify dynamical fees that mimic the directionality of the price due to asymmetric fee choices as a promising avenue to mitigate losses to toxic flow. This work is based on and extends a recent article by some of the authors.