arXiv - QuanBio - Quantitative Methods最新文献_第10页

Mapping Cancer Stem Cell Markers Distribution:A Hypergraph Analysis Across Organs 绘制癌症干细胞标记物分布图：跨器官的超图分析

arXiv - QuanBio - Quantitative Methods Pub Date : 2024-07-27 DOI: arxiv-2407.19330

David H. Margarit, Gustavo Paccosi, Marcela V. Reale, Lilia M. Romanelli

{"title":"Mapping Cancer Stem Cell Markers Distribution:A Hypergraph Analysis Across Organs","authors":"David H. Margarit, Gustavo Paccosi, Marcela V. Reale, Lilia M. Romanelli","doi":"arxiv-2407.19330","DOIUrl":"https://doi.org/arxiv-2407.19330","url":null,"abstract":"This study presents an interdisciplinary approach to analyse the distribution\u0000of cancer stem cell markers (CSCMs) across various cancer-affected organs using\u0000hypergraphs. Cancer stem cells (CSCs) play a crucial role in cancer initiation,\u0000progression, and metastasis. By employing hypergraphs, we model the\u0000relationships between CSCM locations and cancerous organs, providing a\u0000comprehensive representation of these interactions. Initially, we utilised an\u0000unweighted incidence matrix and its Markov transition matrices to gain a\u0000dynamic perspective on CSCM distributions. This method allows us to observe how\u0000these markers spread and influence cancer progression in a dynamical context.\u0000By calculating mutual information for each node and hyperedge, our analysis\u0000uncovers complex interaction patterns between CSCMs and organs, highlighting\u0000the critical roles of certain markers in cancer progression and metastasis. Our\u0000approach offers a detailed representation of cancer stem cell networks,\u0000enhancing our understanding of the mechanisms driving cancer heterogeneity and\u0000metastasis. By integrating hypergraph theory with cancer biology, this study\u0000provides valuable insights for developing targeted cancer therapies.","PeriodicalId":501266,"journal":{"name":"arXiv - QuanBio - Quantitative Methods","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-07-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141869979","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Small Molecule Optimization with Large Language Models 利用大语言模型优化小分子结构

arXiv - QuanBio - Quantitative Methods Pub Date : 2024-07-26 DOI: arxiv-2407.18897

Philipp Guevorguian, Menua Bedrosian, Tigran Fahradyan, Gayane Chilingaryan, Hrant Khachatrian, Armen Aghajanyan

引用次数: 0

Mitosis, Cytoskeleton Regulation, and Drug Resistance in Receptor Triple Negative Breast Cancer 受体三阴性乳腺癌的有丝分裂、细胞骨架调节和耐药性

arXiv - QuanBio - Quantitative Methods Pub Date : 2024-07-26 DOI: arxiv-2407.19112

Alexandre Matov

{"title":"Mitosis, Cytoskeleton Regulation, and Drug Resistance in Receptor Triple Negative Breast Cancer","authors":"Alexandre Matov","doi":"arxiv-2407.19112","DOIUrl":"https://doi.org/arxiv-2407.19112","url":null,"abstract":"Methods for personalizing medical treatment are the focal point of\u0000contemporary biomedical research. In cancer care, we can analyze the effects of\u0000therapies at the level of individual cells. Quantitative characterization of\u0000treatment efficacy and evaluation of why some individuals respond to specific\u0000regimens, whereas others do not, requires additional approaches to genetic\u0000sequencing at single time points. Methods for the continuous analysis of\u0000changes in phenotype, such as in vivo and ex vivo morphology and motion\u0000tracking of cellular proteins and organelles, over time-frames spanning the\u0000minute-hour scales, can provide important insights into patient treatment\u0000options. Despite improvements in the diagnosis and therapy of many types of breast\u0000cancer (BC), many aggressive forms, such as receptor triple-negative cancers,\u0000are associated with the worst patient outcomes; though initially effective in\u0000reducing tumor burden for some patients, acquired resistance to cytotoxic\u0000chemotherapy is almost universal, and there is no rationale for identifying\u0000intrinsically drug-resistant and drug-sensitive patient populations before\u0000initiating therapy. During cell division, the receptor triple-negative\u0000MDA-MB-231 mitotic spindles are the largest in comparison to other BC cell\u0000lines. Many of the MDA-MB-231 spindles exhibit rapid lateral twisting during\u0000metaphase, which remains unaffected by knockdown of the oncogene Myc and\u0000treatment with inhibitors of the serine/threonine-protein kinase B-Raf and the\u0000epidermal growth factor receptor (EGFR), alone or in any combination. In this manuscript, we outline a strategy for the selection of the most\u0000optimal tubulin inhibitor based on the ability to affect MT dynamics.","PeriodicalId":501266,"journal":{"name":"arXiv - QuanBio - Quantitative Methods","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-07-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141869978","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Interpreting artificial neural networks to detect genome-wide association signals for complex traits 解读人工神经网络，检测复杂性状的全基因组关联信号

arXiv - QuanBio - Quantitative Methods Pub Date : 2024-07-26 DOI: arxiv-2407.18811

Burak Yelmen, Maris Alver, Estonian Biobank Research Team, Flora Jay, Lili Milani

{"title":"Interpreting artificial neural networks to detect genome-wide association signals for complex traits","authors":"Burak Yelmen, Maris Alver, Estonian Biobank Research Team, Flora Jay, Lili Milani","doi":"arxiv-2407.18811","DOIUrl":"https://doi.org/arxiv-2407.18811","url":null,"abstract":"Investigating the genetic architecture of complex diseases is challenging due\u0000to the highly polygenic and interactive landscape of genetic and environmental\u0000factors. Although genome-wide association studies (GWAS) have identified\u0000thousands of variants for multiple complex phenotypes, conventional statistical\u0000approaches can be limited by simplified assumptions such as linearity and lack\u0000of epistasis models. In this work, we trained artificial neural networks for\u0000predicting complex traits using both simulated and real genotype/phenotype\u0000datasets. We extracted feature importance scores via different post hoc\u0000interpretability methods to identify potentially associated loci (PAL) for the\u0000target phenotype. Simulations we performed with various parameters demonstrated\u0000that associated loci can be detected with good precision using strict selection\u0000criteria, but downstream analyses are required for fine-mapping the exact\u0000variants due to linkage disequilibrium, similarly to conventional GWAS. By\u0000applying our approach to the schizophrenia cohort in the Estonian Biobank, we\u0000were able to detect multiple PAL related to this highly polygenic and heritable\u0000disorder. We also performed enrichment analyses with PAL in genic regions,\u0000which predominantly identified terms associated with brain morphology. With\u0000further improvements in model optimization and confidence measures, artificial\u0000neural networks can enhance the identification of genomic loci associated with\u0000complex diseases, providing a more comprehensive approach for GWAS and serving\u0000as initial screening tools for subsequent functional studies. Keywords: Deep learning, interpretability, genome-wide association studies,\u0000complex diseases","PeriodicalId":501266,"journal":{"name":"arXiv - QuanBio - Quantitative Methods","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-07-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141869981","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Impact of opinion dynamics on recurrent pandemic waves: balancing risk aversion and peer pressure 舆论动态对经常性流行病浪潮的影响：平衡风险规避和同伴压力

arXiv - QuanBio - Quantitative Methods Pub Date : 2024-07-26 DOI: arxiv-2408.00011

Sheryl L. Chang, Quang Dang Nguyen, Carl J. E. Suster, Christina M. Jamerlan, Rebecca J. Rockett, Vitali Sintchenko, Tania C. Sorrell, Alexandra Martiniuk, Mikhail Prokopenko

{"title":"Impact of opinion dynamics on recurrent pandemic waves: balancing risk aversion and peer pressure","authors":"Sheryl L. Chang, Quang Dang Nguyen, Carl J. E. Suster, Christina M. Jamerlan, Rebecca J. Rockett, Vitali Sintchenko, Tania C. Sorrell, Alexandra Martiniuk, Mikhail Prokopenko","doi":"arxiv-2408.00011","DOIUrl":"https://doi.org/arxiv-2408.00011","url":null,"abstract":"Recurrent waves which are often observed during long pandemics typically form\u0000as a result of several interrelated dynamics including public health\u0000interventions, population mobility and behaviour, varying disease\u0000transmissibility due to pathogen mutations, and changes in host immunity due to\u0000recency of vaccination or previous infections. Complex nonlinear dependencies\u0000among these dynamics, including feedback between disease incidence and the\u0000opinion-driven adoption of social distancing behaviour, remain poorly\u0000understood, particularly in scenarios involving heterogeneous population,\u0000partial and waning immunity, and rapidly changing public opinions. This study\u0000addressed this challenge by proposing an opinion dynamics model that accounts\u0000for changes in social distancing behaviour (i.e., whether to adopt social\u0000distancing) by modelling both individual risk perception and peer pressure. The\u0000opinion dynamics model was integrated and validated within a large-scale\u0000agent-based COVID-19 pandemic simulation that modelled the spread of the\u0000Omicron variant of SARS-CoV-2 between December 2021 and June 2022 in Australia.\u0000Our study revealed that the fluctuating adoption of social distancing, shaped\u0000by individual risk aversion and social peer pressure from both household and\u0000workplace environments, may explain the observed pattern of recurrent waves of\u0000infections.","PeriodicalId":501266,"journal":{"name":"arXiv - QuanBio - Quantitative Methods","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-07-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141881709","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

CavDetect: A DBSCAN Algorithm based Novel Cavity Detection Model on Protein Structure CavDetect：基于 DBSCAN 算法的蛋白质结构新空洞检测模型

arXiv - QuanBio - Quantitative Methods Pub Date : 2024-07-25 DOI: arxiv-2407.18317

Swati AdhikariThe University of Burdwan, Parthajit RoyThe University of Burdwan

引用次数: 0

Chemistry-informed Machine Learning Explains Calcium-binding Proteins Fuzzy Shape for Communicating Changes in the Atomic States of Calcium Ions 以化学为基础的机器学习解释钙结合蛋白传递钙离子原子态变化的模糊形状

arXiv - QuanBio - Quantitative Methods Pub Date : 2024-07-24 DOI: arxiv-2407.17017

Pengzhi Zhang, Jules Nde, Yossi Eliaz, Nathaniel Jennings, Piotr Cieplak, Margaret. S. Cheung

{"title":"Chemistry-informed Machine Learning Explains Calcium-binding Proteins Fuzzy Shape for Communicating Changes in the Atomic States of Calcium Ions","authors":"Pengzhi Zhang, Jules Nde, Yossi Eliaz, Nathaniel Jennings, Piotr Cieplak, Margaret. S. Cheung","doi":"arxiv-2407.17017","DOIUrl":"https://doi.org/arxiv-2407.17017","url":null,"abstract":"Proteins' fuzziness are features for communicating changes in cell signaling\u0000instigated by binding with secondary messengers, such as calcium ions,\u0000associated with the coordination of muscle contraction, neurotransmitter\u0000release, and gene expression. Binding with the disordered parts of a protein,\u0000calcium ions must balance their charge states with the shape of calcium-binding\u0000proteins and their versatile pool of partners depending on the circumstances\u0000they transmit, but it is unclear whether the limited experimental data\u0000available can be used to train models to accurately predict the charges of\u0000calcium-binding protein variants. Here, we developed a chemistry-informed,\u0000machine-learning algorithm that implements a game theoretic approach to explain\u0000the output of a machine-learning model without the prerequisite of an\u0000excessively large database for high-performance prediction of atomic charges.\u0000We used the ab initio electronic structure data representing calcium ions and\u0000the structures of the disordered segments of calcium-binding peptides with\u0000surrounding water molecules to train several explainable models. Network theory\u0000was used to extract the topological features of atomic interactions in the\u0000structurally complex data dictated by the coordination chemistry of a calcium\u0000ion, a potent indicator of its charge state in protein. With our designs, we\u0000provided a framework of explainable machine learning model to annotate atomic\u0000charges of calcium ions in calcium-binding proteins with domain knowledge in\u0000response to the chemical changes in an environment based on the limited size of\u0000scientific data in a genome space.","PeriodicalId":501266,"journal":{"name":"arXiv - QuanBio - Quantitative Methods","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-07-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141782985","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Research on Adverse Drug Reaction Prediction Model Combining Knowledge Graph Embedding and Deep Learning 知识图谱嵌入与深度学习相结合的药物不良反应预测模型研究

arXiv - QuanBio - Quantitative Methods Pub Date : 2024-07-23 DOI: arxiv-2407.16715

Yufeng Li, Wenchao Zhao, Bo Dang, Xu Yan, Weimin Wang, Min Gao, Mingxuan Xiao

{"title":"Research on Adverse Drug Reaction Prediction Model Combining Knowledge Graph Embedding and Deep Learning","authors":"Yufeng Li, Wenchao Zhao, Bo Dang, Xu Yan, Weimin Wang, Min Gao, Mingxuan Xiao","doi":"arxiv-2407.16715","DOIUrl":"https://doi.org/arxiv-2407.16715","url":null,"abstract":"In clinical treatment, identifying potential adverse reactions of drugs can\u0000help assist doctors in making medication decisions. In response to the problems\u0000in previous studies that features are high-dimensional and sparse, independent\u0000prediction models need to be constructed for each adverse reaction of drugs,\u0000and the prediction accuracy is low, this paper develops an adverse drug\u0000reaction prediction model based on knowledge graph embedding and deep learning,\u0000which can predict experimental results. Unified prediction of adverse drug\u0000reactions covered. Knowledge graph embedding technology can fuse the associated\u0000information between drugs and alleviate the shortcomings of high-dimensional\u0000sparsity in feature matrices, and the efficient training capabilities of deep\u0000learning can improve the prediction accuracy of the model. This article builds\u0000an adverse drug reaction knowledge graph based on drug feature data; by\u0000analyzing the embedding effect of the knowledge graph under different embedding\u0000strategies, the best embedding strategy is selected to obtain sample vectors;\u0000and then a convolutional neural network model is constructed to predict adverse\u0000reactions. The results show that under the DistMult embedding model and\u0000400-dimensional embedding strategy, the convolutional neural network model has\u0000the best prediction effect; the average accuracy, F_1 score, recall rate and\u0000area under the curve of repeated experiments are better than the methods\u0000reported in the literature. The obtained prediction model has good prediction\u0000accuracy and stability, and can provide an effective reference for later safe\u0000medication guidance.","PeriodicalId":501266,"journal":{"name":"arXiv - QuanBio - Quantitative Methods","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-07-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141782989","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Machine Learning Models for the Identification of Cardiovascular Diseases Using UK Biobank Data 利用英国生物库数据识别心血管疾病的机器学习模型

arXiv - QuanBio - Quantitative Methods Pub Date : 2024-07-23 DOI: arxiv-2407.16721

Sheikh Mohammed Shariful Islam, Moloud Abrar, Teketo Tegegne, Liliana Loranjo, Chandan Karmakar, Md Abdul Awal, Md. Shahadat Hossain, Muhammad Ashad Kabir, Mufti Mahmud, Abbas Khosravi, George Siopis, Jeban C Moses, Ralph Maddison

{"title":"Machine Learning Models for the Identification of Cardiovascular Diseases Using UK Biobank Data","authors":"Sheikh Mohammed Shariful Islam, Moloud Abrar, Teketo Tegegne, Liliana Loranjo, Chandan Karmakar, Md Abdul Awal, Md. Shahadat Hossain, Muhammad Ashad Kabir, Mufti Mahmud, Abbas Khosravi, George Siopis, Jeban C Moses, Ralph Maddison","doi":"arxiv-2407.16721","DOIUrl":"https://doi.org/arxiv-2407.16721","url":null,"abstract":"Machine learning models have the potential to identify cardiovascular\u0000diseases (CVDs) early and accurately in primary healthcare settings, which is\u0000crucial for delivering timely treatment and management. Although\u0000population-based CVD risk models have been used traditionally, these models\u0000often do not consider variations in lifestyles, socioeconomic conditions, or\u0000genetic predispositions. Therefore, we aimed to develop machine learning models\u0000for CVD detection using primary healthcare data, compare the performance of\u0000different models, and identify the best models. We used data from the UK\u0000Biobank study, which included over 500,000 middle-aged participants from\u0000different primary healthcare centers in the UK. Data collected at baseline\u0000(2006--2010) and during imaging visits after 2014 were used in this study.\u0000Baseline characteristics, including sex, age, and the Townsend Deprivation\u0000Index, were included. Participants were classified as having CVD if they\u0000reported at least one of the following conditions: heart attack, angina,\u0000stroke, or high blood pressure. Cardiac imaging data such as electrocardiogram\u0000and echocardiography data, including left ventricular size and function,\u0000cardiac output, and stroke volume, were also used. We used 9 machine learning\u0000models (LSVM, RBFSVM, GP, DT, RF, NN, AdaBoost, NB, and QDA), which are\u0000explainable and easily interpretable. We reported the accuracy, precision,\u0000recall, and F-1 scores; confusion matrices; and area under the curve (AUC)\u0000curves.","PeriodicalId":501266,"journal":{"name":"arXiv - QuanBio - Quantitative Methods","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-07-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141782987","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

A study of animal action segmentation algorithms across supervised, unsupervised, and semi-supervised learning paradigms 跨越监督、无监督和半监督学习范式的动物动作分割算法研究

arXiv - QuanBio - Quantitative Methods Pub Date : 2024-07-23 DOI: arxiv-2407.16727

Ari Blau, Evan S Schaffer, Neeli Mishra, Nathaniel J Miska, The International Brain Laboratory, Liam Paninski, Matthew R Whiteway

{"title":"A study of animal action segmentation algorithms across supervised, unsupervised, and semi-supervised learning paradigms","authors":"Ari Blau, Evan S Schaffer, Neeli Mishra, Nathaniel J Miska, The International Brain Laboratory, Liam Paninski, Matthew R Whiteway","doi":"arxiv-2407.16727","DOIUrl":"https://doi.org/arxiv-2407.16727","url":null,"abstract":"Action segmentation of behavioral videos is the process of labeling each\u0000frame as belonging to one or more discrete classes, and is a crucial component\u0000of many studies that investigate animal behavior. A wide range of algorithms\u0000exist to automatically parse discrete animal behavior, encompassing supervised,\u0000unsupervised, and semi-supervised learning paradigms. These algorithms -- which\u0000include tree-based models, deep neural networks, and graphical models -- differ\u0000widely in their structure and assumptions on the data. Using four datasets\u0000spanning multiple species -- fly, mouse, and human -- we systematically study\u0000how the outputs of these various algorithms align with manually annotated\u0000behaviors of interest. Along the way, we introduce a semi-supervised action\u0000segmentation model that bridges the gap between supervised deep neural networks\u0000and unsupervised graphical models. We find that fully supervised temporal\u0000convolutional networks with the addition of temporal information in the\u0000observations perform the best on our supervised metrics across all datasets.","PeriodicalId":501266,"journal":{"name":"arXiv - QuanBio - Quantitative Methods","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-07-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141782990","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0