pwc-archive/papers-with-abstracts huggingface dataset:
How cells stay together; a mechanism for maintenance of a robust cluster explored by local and nonlocal continuum models
Authors: Andreas Buttenschön, Shona Sinclair, Leah Edelstein-Keshet
Date: 2024-06-05 00:00:00
Tasks: Clustering

Formation of organs and specialized tissues in embryonic development requires migration of cells to specific targets. In some instances, such cells migrate as a robust cluster. We here explore a recent local approximation of nonlocal continuum models by Falc\'o, Baker, and Carrillo (2023). We apply their theoretical results by specifying biologically-based cell-cell interactions, showing how such cell communication results in an effective attraction-repulsion Morse potential. We then explore the clustering instability, the existence and size of the cluster, and its stability. We also extend their work by investigating the accuracy of the local approximation relative to the full nonlocal model.
Modal Analysis of Cellular Dynamics in the Morphospace in Epithelial-Mesenchymal Transition
Authors: Akash Chandra Das, Debanga Raj Neog, Biplab Bose
Date: 2024-06-03 00:00:00

During epithelial-mesenchymal transition (EMT), epithelial cells change their morphology, disperse, and gain mesenchymal-like characteristics. Usually, cells are categorized into discrete cell types or states based on gene expression and other cellular features. Subsequently, EMT is investigated as a dynamical process where cells jump from one discrete state to another. In the current work, we moved away from this idea of discrete state transition and investigated EMT dynamics in a continuous phenotypic space. We used morphology to define the phenotype of a cell. We used the data from quantitative image analysis of MDA-MB-468 cells undergoing EGF-induced EMT. We defined the morphological state space or 'morphospace' using the morphological features extracted through image analysis. During EMT, as the morphology changed, the distribution of cells in the morphospace also changed. However, this morphospace had a very high dimension. We reduced it to a 2-dimensional "reduced morphospace" and investigated the temporal change in the spatial distribution of cells in this reduced space. We used proper orthogonal decomposition to find dominant dynamical features of this spatio-temporal data. The modal analysis detected key features of EMT in this experimental system - reversible transition, distinct paths of phenotypic transition during induction and reversal of EMT, and enhanced diversity of cells during reversal of EMT. We also provide some intuitive physical meaning of the spatial modes and connect them to the key molecular event during EMT.
DYNA: Disease-Specific Language Model for Variant Pathogenicity
Authors: Huixin Zhan, Zijun Zhang
Date: 2024-05-31 00:00:00
Tasks: Language Modeling, Language Modelling, model, Specificity

Clinical variant classification of pathogenic versus benign genetic variants remains a challenge in clinical genetics. Recently, the proposition of genomic foundation models has improved the generic variant effect prediction (VEP) accuracy via weakly-supervised or unsupervised training. However, these VEPs are not disease-specific, limiting their adaptation at the point of care. To address this problem, we propose DYNA: Disease-specificity fine-tuning via a Siamese neural network broadly applicable to all genomic foundation models for more effective variant effect predictions in disease-specific contexts. We evaluate DYNA in two distinct disease-relevant tasks. For coding VEPs, we focus on various cardiovascular diseases, where gene-disease relationships of loss-of-function vs. gain-of-function dictate disease-specific VEP. For non-coding VEPs, we apply DYNA to an essential post-transcriptional regulatory axis of RNA splicing, the most common non-coding pathogenic mechanism in established clinical VEP guidelines. In both cases, DYNA fine-tunes various pre-trained genomic foundation models on small, rare variant sets. The DYNA fine-tuned models show superior performance in the held-out rare variant testing set and are further replicated in large, clinically-relevant variant annotations in ClinVAR. Thus, DYNA offers a potent disease-specific variant effect prediction method, excelling in intra-gene generalization and generalization to unseen genetic variants, making it particularly valuable for disease associations and clinical applicability.
GET: A Generative EEG Transformer for Continuous Context-Based Neural Signals
Authors: Omair Ali, Muhammad Saif-ur-Rehman, Marita Metzler, Tobias Glasmachers, Ioannis Iossifidis, Christian Klaes
Date: 2024-06-05 00:00:00
Tasks: Brain Computer Interface, Data Augmentation, EEG, Motor Imagery

Generating continuous electroencephalography (EEG) signals through advanced artificial neural networks presents a novel opportunity to enhance brain-computer interface (BCI) technology. This capability has the potential to significantly enhance applications ranging from simulating dynamic brain activity and data augmentation to improving real-time epilepsy detection and BCI inference. By harnessing generative transformer neural networks, specifically designed for EEG signal generation, we can revolutionize the interpretation and interaction with neural data. Generative AI has demonstrated significant success across various domains, from natural language processing (NLP) and computer vision to content creation in visual arts and music. It distinguishes itself by using large-scale datasets to construct context windows during pre-training, a technique that has proven particularly effective in NLP, where models are fine-tuned for specific downstream tasks after extensive foundational training. However, the application of generative AI in the field of BCIs, particularly through the development of continuous, context-rich neural signal generators, has been limited. To address this, we introduce the Generative EEG Transformer (GET), a model leveraging transformer architecture tailored for EEG data. The GET model is pre-trained on diverse EEG datasets, including motor imagery and alpha wave datasets, enabling it to produce high-fidelity neural signals that maintain contextual integrity. Our empirical findings indicate that GET not only faithfully reproduces the frequency spectrum of the training data and input prompts but also robustly generates continuous neural signals. By adopting the successful training strategies of the NLP domain for BCIs, the GET sets a new standard for the development and application of neural signal generation technologies.
Vagus nerve stimulation: Laying the groundwork for predictive network-based computer models
Authors: John F. Ingham, Frances Hutchings, Paolo Zuliani, Yujiang Wang, Sadegh Soudjani, Peter N. Taylor
Date: 2024-06-04 00:00:00

Vagus Nerve Stimulation (VNS) is an established palliative treatment for drug resistant epilepsy. While effective for many patients, its mechanism of action is incompletely understood. Predicting individuals' response, or optimum stimulation parameters, is challenging. Computational modelling has informed other problems in epilepsy but, to our knowledge, has not been applied to VNS. We started with an established, four-population neural mass model (NMM), capable of reproducing the seizure-like dynamics of a thalamocortical circuit. We extended this to include 18 further neural populations, representing nine other brain regions relevant to VNS, with connectivity based on existing literature. We modelled stimulated afferent vagal fibres as projecting to the nucleus tractus solitarius (NTS), which receives input from the vagus nerve in vivo. Bifurcation analysis of a deterministic version of the model showed higher background NTS input made the model monostable at a fixed point (FP), representing normal activity, while lower inputs produce bistability between the FP and a limit cycle (LC), representing the seizure state. Adding noise produced transitions between seizure and normal states. This stochastic model spent decreasing time in the seizure state with increasing background NTS input, until seizures were abolished, consistent with the deterministic model. Simulated VNS stimulation, modelled as a 30 Hz square wave, was summed with the background input to the NTS and was found to reduce total seizure duration in a dose-dependent manner, similar to expectations in vivo. We have successfully produced an in silico model of VNS in epilepsy, capturing behaviour seen in vivo. This may aid understanding therapeutic mechanisms of VNS in epilepsy and provides a starting point to (i) determine which patients might respond best to VNS, and (ii) optimise individuals' treatments.
Reinforcement Learning as a Robotics-Inspired Framework for Insect Navigation: From Spatial Representations to Neural Implementation
Authors: Stephan Lochner, Daniel Honerkamp, Abhinav Valada, Andrew D. Straw
Date: 2024-06-03 00:00:00
Tasks: Reinforcement Learning (RL), Robot Navigation

Bees are among the master navigators of the insect world. Despite impressive advances in robot navigation research, the performance of these insects is still unrivaled by any artificial system in terms of training efficiency and generalization capabilities, particularly considering the limited computational capacity. On the other hand, computational principles underlying these extraordinary feats are still only partially understood. The theoretical framework of reinforcement learning (RL) provides an ideal focal point to bring the two fields together for mutual benefit. In particular, we analyze and compare representations of space in robot and insect navigation models through the lens of RL, as the efficiency of insect navigation is likely rooted in an efficient and robust internal representation, linking retinotopic (egocentric) visual input with the geometry of the environment. While RL has long been at the core of robot navigation research, current computational theories of insect navigation are not commonly formulated within this framework, but largely as an associative learning process implemented in the insect brain, especially in the mushroom body (MB). Here we propose specific hypothetical components of the MB circuit that would enable the implementation of a certain class of relatively simple RL algorithms, capable of integrating distinct components of a navigation task, reminiscent of hierarchical RL models used in robot navigation. We discuss how current models of insect and robot navigation are exploring representations beyond classical, complete map-like representations, with spatial information being embedded in the respective latent representations to varying degrees.
Pattern Formation in a Spiking Neural-Field of Renewal Neurons
Authors: Gregory Dumont, Carmen Oana Tarniceriu
Date: 2024-06-03 00:00:00

Elucidating the neurophysiological mechanisms underlying neural pattern formation remains an outstanding challenge in Computational Neuroscience. In this paper, we address the issue of understanding the emergence of neural patterns by considering a network of renewal neurons, a well-established class of spiking cells. Taking the thermodynamics limit, the network's dynamics can be accurately represented by a partial differential equation coupled with a nonlocal differential equation. The stationary state of the nonlocal system is determined, and a perturbation analysis is performed to analytically characterize the conditions for the occurrence of Turing instabilities. Considering neural network parameters such as the synaptic coupling and the external drive, we numerically obtain the bifurcation line that separates the asynchronous regime from the emergence of patterns. Our theoretical findings provide a new and insightful perspective on the emergence of Turing patterns in spiking neural networks. In the long term, our formalism will enable the study of neural patterns while maintaining the connections between microscopic cellular properties, network coupling, and the emergence of Turing instabilities.
Brain Morphology Normative modelling platform for abnormality and Centile estimation: Brain MoNoCle
Authors: Bethany Little, Nida Alyas, Alexander Surtees, Gavin P Winston, John S Duncan, David A Cousins, John-Paul Taylor, Peter Taylor, Karoline Leiberg, Yujiang Wang
Date: 2024-06-03 00:00:00

Normative models of brain structure estimate the effects of covariates such as age and sex using large samples of healthy controls. These models can then be applied to e.g. smaller clinical cohorts to distinguish disease effects from other covariates. However, these advanced statistical modelling approaches can be difficult to access, and processing large healthy cohorts is computationally demanding. Thus, accessible platforms with pre-trained normative models are needed. We present such a platform for brain morphology analysis as an open-source web application https://cnnplab.shinyapps.io/BrainMoNoCle/, with six key features: (i) user-friendly web interface, (ii) individual and group outputs, (iii) multi-site analysis, (iv) regional and whole-brain analysis, (v) integration with existing tools, and (vi) featuring multiple morphology metrics. Using a diverse sample of 3,276 healthy controls across 21 sites, we pre-trained normative models on various metrics. We validated the models with a small sample of individuals with bipolar disorder, showing outputs that aligned closely with existing literature only after applying our normative modelling. Using a cohort of people with temporal lobe epilepsy, we showed that individual-level abnormalities were in line with seizure lateralisation. Finally, with the ability to investigate multiple morphology measures in the same framework, we found that biological covariates are better explained in specific morphology measures, and for applications, only some measures are sensitive to the disease process. Our platform offers a comprehensive framework to analyse brain morphology in clinical and research settings. Validations confirm the superiority of normative models and the advantage of investigating a range of brain morphology metrics together.
NeuroAssist: Enhancing Cognitive-Computer Synergy with Adaptive AI and Advanced Neural Decoding for Efficient EEG Signal Classification
Authors: Eeshan G. Dandamudi
Date: 2024-05-17 00:00:00
Tasks: Brain Computer Interface, EEG, EEG Signal Classification, Motor Imagery

Traditional methods of controlling prosthetics frequently encounter difficulties regarding flexibility and responsiveness, which can substantially impact people with varying cognitive and physical abilities. Advancements in computational neuroscience and machine learning (ML) have recently led to the development of highly advanced brain-computer interface (BCI) systems that may be customized to meet individual requirements. To address these issues, we propose NeuroAssist, a sophisticated method for analyzing EEG data that merges state-of-the-art BCI technology with adaptable artificial intelligence (AI) algorithms. NeuroAssist's hybrid neural network design efficiently overcomes the constraints of conventional EEG data processing. Our methodology combines a Natural Language Processing (NLP) BERT model to extract complex features from numerical EEG data and utilizes LSTM networks to handle temporal dynamics. In addition, we integrate spiking neural networks (SNNs) and deep Q-networks (DQN) to improve decision-making and flexibility. Our preprocessing method classifies motor imagery (MI) one-versus-the-rest using a common spatial pattern (CSP) while preserving EEG temporal characteristics. The hybrid architecture of NeuroAssist serves as the DQN's Q-network, enabling continuous feedback-based improvement and adaptability. This enables it to acquire optimal actions through trial and error. This experimental analysis has been conducted on the GigaScience and BCI-competition-IV-2a datasets, which have shown exceptional effectiveness in categorizing MI-EEG signals, obtaining an impressive classification accuracy of 99.17%. NeuroAssist offers a crucial approach to current assistive technology by potentially enhancing the speed and versatility of BCI systems.
Reducing phenotype-structured PDE models of cancer evolution to systems of ODEs: a generalised moment dynamics approach
Authors: Chiara Villa, Philip K Maini, Alexander P Browning, Adrianne L Jenner, Sara Hamis, Tyler Cassidy
Date: 2024-06-03 00:00:00

Intratumour phenotypic heterogeneity is nowadays understood to play a critical role in disease progression and treatment failure. Accordingly, there has been increasing interest in the development of mathematical models capable of capturing its role in cancer cell adaptation. This can be systematically achieved by means of models comprising phenotype-structured nonlocal partial differential equations, tracking the evolution of the phenotypic density distribution of the cell population, which may be compared to gene and protein expression distributions obtained experimentally. Nevertheless, given the high analytical and computational cost of solving these models, much is to be gained from reducing them to systems of ordinary differential equations for the moments of the distribution. We propose a generalised method of model-reduction, relying on the use of a moment generating function, Taylor series expansion and truncation closure, to reduce a nonlocal reaction-advection-diffusion equation, with general phenotypic drift and proliferation rate functions, to a system of moment equations up to arbitrary order. Our method extends previous results in the literature, which we address via two examples, by removing any \textit{a priori} assumption on the shape of the distribution, and provides a flexible framework for mathematical modellers to account for the role of phenotypic heterogeneity in cancer adaptive dynamics, in a simpler mathematical framework.
Identification of PK-PD Insulin Models using Experimental GIR Data
Authors: Kirstine Sylvest Freil, Liv Olivia Fritzen, Dimitri Boiroux, Tinna B. Aradottir, John Bagterp Jørgensen
Date: 2024-06-05 00:00:00

We present a method to estimate parameters in pharmacokinetic (PK) and pharmacodynamic (PD) models for glucose insulin dynamics in humans. The method combines 1) experimental glucose infusion rate (GIR) data from glucose clamp studies and 2) a PK-PD model to estimate parameters such that the model fits the data. Assuming that the glucose clamp is perfect, we do not need to know the details of the controller in the clamp, and the GIR can be computed directly from the PK-PD model. To illustrate the procedure, we use the glucoregulatory model developed by Hovorka and modify it to have a smooth non-negative endogeneous glucose production (EGP) term. We estimate PK-PD parameters for rapid-acting insulin analogs (Fiasp and NovoRapid). We use these PK-PD parameters to illustrate GIR for insulin analogs with 30% and 50% faster absorption time than currently available rapid-acting insulin analogs. We discuss the role of system identification using GIR data from glucose clamp studies and how such identified models can be used in automated insulin dosing (AID) systems with ultra rapid-acting insulin.
Modeling PROTAC Degradation Activity with Machine Learning
Authors: Stefano Ribes, Eva Nittinger, Christian Tyrchan, Rocío Mercado
Date: 2024-06-04 00:00:00
Tasks: Binary Classification, Drug Design

PROTACs are a promising therapeutic modality that harnesses the cell's built-in degradation machinery to degrade specific proteins. Despite their potential, developing new PROTACs is challenging and requires significant domain expertise, time, and cost. Meanwhile, machine learning has transformed drug design and development. In this work, we present a strategy for curating open-source PROTAC data and an open-source deep learning tool for predicting the degradation activity of novel PROTAC molecules. The curated dataset incorporates important information such as $pDC_{50}$, $D_{max}$, E3 ligase type, POI amino acid sequence, and experimental cell type. Our model architecture leverages learned embeddings from pretrained machine learning models, in particular for encoding protein sequences and cell type information. We assessed the quality of the curated data and the generalization ability of our model architecture against new PROTACs and targets via three tailored studies, which we recommend other researchers to use in evaluating their degradation activity models. In each study, three models predict protein degradation in a majority vote setting, reaching a top test accuracy of 80.8% and 0.865 ROC AUC, and a test accuracy of 62.3% and 0.604 ROC AUC when generalizing to novel protein targets. Our results are not only comparable to state-of-the-art models for protein degradation prediction, but also part of an open-source implementation which is easily reproducible and less computationally complex than existing approaches.
Matrix dissimilarities based on differences in moments and sparsity
Authors: Li Tuobang
Date: 2024-06-04 00:00:00

Generating a dissimilarity matrix is typically the first step in big data analysis. Although numerous methods exist, such as Euclidean distance, Minkowski distance, Manhattan distance, Bray Curtis dissimilarity, Jaccard similarity and Dice dissimilarity, it remains unclear which factors drive dissimilarity between groups. In this paper, we introduce an approach based on differences in moments and sparsity. We show that this method can delineate the key factors underlying group differences. For example, in biology, mean dissimilarity indicates differences driven by up down regulated gene expressions, standard deviation dissimilarity reflects the heterogeneity of response to treatment, and sparsity dissimilarity corresponds to differences prompted by the activation silence of genes. Through extensive reanalysis of genome, transcriptome, proteome, metabolome, immune profiling, microbiome, and social science datasets, we demonstrate insights not captured in previous studies. For instance, it shows that the sparsity dissimilarity is as effective as the mean dissimilarity in predicting the alleviation effects of a COVID 19 drug, suggesting that sparsity dissimilarity is highly meaningful.
MoFormer: Multi-objective Antimicrobial Peptide Generation Based on Conditional Transformer Joint Multi-modal Fusion Descriptor
Authors: Li Wang, Xiangzheng Fu, Jiahao Yang, Xinyi Zhang, Xiucai Ye, Yiping Liu, Tetsuya Sakurai, Xiangxiang Zeng
Date: 2024-06-03 00:00:00
Tasks: Drug Discovery

Deep learning holds a big promise for optimizing existing peptides with more desirable properties, a critical step towards accelerating new drug discovery. Despite the recent emergence of several optimized Antimicrobial peptides(AMP) generation methods, multi-objective optimizations remain still quite challenging for the idealism-realism tradeoff. Here, we establish a multi-objective AMP synthesis pipeline (MoFormer) for the simultaneous optimization of multi-attributes of AMPs. MoFormer improves the desired attributes of AMP sequences in a highly structured latent space, guided by conditional constraints and fine-grained multi-descriptor.We show that MoFormer outperforms existing methods in the generation task of enhanced antimicrobial activity and minimal hemolysis. We also utilize a Pareto-based non-dominated sorting algorithm and proxies based on large model fine-tuning to hierarchically rank the candidates. We demonstrate substantial property improvement using MoFormer from two perspectives: (1) employing molecular simulations and scoring interactions among amino acids to decipher the structure and functionality of AMPs; (2) visualizing latent space to examine the qualities and distribution features, verifying an effective means to facilitate multi-objective optimization AMPs with design constraints