Patterns最新文献_第10页

Reducing overconfident errors in molecular property classification using Posterior Network 利用后验网络减少分子特性分类中的过度自信误差

IF 6.5

Patterns Pub Date : 2024-05-08 DOI: 10.1016/j.patter.2024.100991

Zhehuan Fan, Jie Yu, Xiang Zhang, Yijie Chen, Shihui Sun, Yuanyuan Zhang, Mingan Chen, Fu Xiao, Wenyong Wu, Xutong Li, Mingyue Zheng, Xiaomin Luo, Dingyan Wang

{"title":"Reducing overconfident errors in molecular property classification using Posterior Network","authors":"Zhehuan Fan, Jie Yu, Xiang Zhang, Yijie Chen, Shihui Sun, Yuanyuan Zhang, Mingan Chen, Fu Xiao, Wenyong Wu, Xutong Li, Mingyue Zheng, Xiaomin Luo, Dingyan Wang","doi":"10.1016/j.patter.2024.100991","DOIUrl":"https://doi.org/10.1016/j.patter.2024.100991","url":null,"abstract":"Deep-learning-based classification models are increasingly used for predicting molecular properties in drug development. However, traditional classification models using the Softmax function often give overconfident mispredictions for out-of-distribution samples, highlighting a critical lack of accurate uncertainty estimation. Such limitations can result in substantial costs and should be avoided during drug development. Inspired by advances in evidential deep learning and Posterior Network, we replaced the Softmax function with a normalizing flow to enhance the uncertainty estimation ability of the model in molecular property classification. The proposed strategy was evaluated across diverse scenarios, including simulated experiments based on a synthetic dataset, ADMET predictions, and ligand-based virtual screening. The results demonstrate that compared with the vanilla model, the proposed strategy effectively alleviates the problem of giving overconfident but incorrect predictions. Our findings support the promising application of evidential deep learning in drug development and offer a valuable framework for further research.","PeriodicalId":36242,"journal":{"name":"Patterns","volume":"29 1","pages":""},"PeriodicalIF":6.5,"publicationDate":"2024-05-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140933408","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

Active sensing with predictive coding and uncertainty minimization 带有预测编码和不确定性最小化功能的主动传感

IF 6.5

Patterns Pub Date : 2024-05-03 DOI: 10.1016/j.patter.2024.100983

Abdelrahman Sharafeldin, Nabil Imam, Hannah Choi

引用次数: 0

IF 6.5

Patterns Pub Date : 2024-05-02 DOI: 10.1016/j.patter.2024.100987

Kelly Rootes-Murdy, Sandeep Panta, Ross Kelly, Javier Romero, Yann Quidé, Murray J. Cairns, Carmel Loughland, Vaughan J. Carr, Stanley V. Catts, Assen Jablensky, Melissa J. Green, Frans Henskens, Dylan Kiltschewskij, Patricia T. Michie, Bryan Mowry, Christos Pantelis, Paul E. Rasser, William R. Reay, Ulrich Schall, Rodney J. Scott, Vince D. Calhoun

{"title":"Cortical similarities in psychiatric and mood disorders identified in federated VBM analysis via COINSTAC","authors":"Kelly Rootes-Murdy, Sandeep Panta, Ross Kelly, Javier Romero, Yann Quidé, Murray J. Cairns, Carmel Loughland, Vaughan J. Carr, Stanley V. Catts, Assen Jablensky, Melissa J. Green, Frans Henskens, Dylan Kiltschewskij, Patricia T. Michie, Bryan Mowry, Christos Pantelis, Paul E. Rasser, William R. Reay, Ulrich Schall, Rodney J. Scott, Vince D. Calhoun","doi":"10.1016/j.patter.2024.100987","DOIUrl":"https://doi.org/10.1016/j.patter.2024.100987","url":null,"abstract":"Structural neuroimaging studies have identified a combination of shared and disorder-specific patterns of gray matter (GM) deficits across psychiatric disorders. Pooling large data allows for examination of a possible common neuroanatomical basis that may identify a certain vulnerability for mental illness. Large-scale collaborative research is already facilitated by data repositories, institutionally supported databases, and data archives. However, these data-sharing methodologies can suffer from significant barriers. Federated approaches augment these approaches by enabling access or more sophisticated, shareable and scaled-up analyses of large-scale data. We examined GM alterations using Collaborative Informatics and Neuroimaging Suite Toolkit for Anonymous Computation, an open-source, decentralized analysis application. Through federated analysis of eight sites, we identified significant overlap in the GM patterns (n = 4,102) of individuals with schizophrenia, major depressive disorder, and autism spectrum disorder. These results show cortical and subcortical regions that may indicate a shared vulnerability to psychiatric disorders.","PeriodicalId":36242,"journal":{"name":"Patterns","volume":"9 1","pages":""},"PeriodicalIF":6.5,"publicationDate":"2024-05-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140832823","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

MUSTANG: Multi-sample spatial transcriptomics data analysis with cross-sample transcriptional similarity guidance MUSTANG：利用跨样本转录相似性指导进行多样本空间转录组学数据分析

IF 6.5

Patterns Pub Date : 2024-05-02 DOI: 10.1016/j.patter.2024.100986

Seyednami Niyakan, Jianting Sheng, Yuliang Cao, Xiang Zhang, Zhan Xu, Ling Wu, Stephen T.C. Wong, Xiaoning Qian

引用次数: 0

A generalized AI system for human embryo selection covering the entire IVF cycle via multi-modal contrastive learning 通过多模态对比学习，建立覆盖整个试管婴儿周期的人类胚胎选择通用人工智能系统

IF 6.5

Patterns Pub Date : 2024-05-02 DOI: 10.1016/j.patter.2024.100985

Guangyu Wang, Kai Wang, Yuanxu Gao, Longbin Chen, Tianrun Gao, Yuanlin Ma, Zeyu Jiang, Guoxing Yang, Fajin Feng, Shuoping Zhang, Yifan Gu, Guangdong Liu, Lei Chen, Li-Shuang Ma, Ye Sang, Yanwen Xu, Ge Lin, Xiaohong Liu

{"title":"A generalized AI system for human embryo selection covering the entire IVF cycle via multi-modal contrastive learning","authors":"Guangyu Wang, Kai Wang, Yuanxu Gao, Longbin Chen, Tianrun Gao, Yuanlin Ma, Zeyu Jiang, Guoxing Yang, Fajin Feng, Shuoping Zhang, Yifan Gu, Guangdong Liu, Lei Chen, Li-Shuang Ma, Ye Sang, Yanwen Xu, Ge Lin, Xiaohong Liu","doi":"10.1016/j.patter.2024.100985","DOIUrl":"https://doi.org/10.1016/j.patter.2024.100985","url":null,"abstract":"In vitro fertilization (IVF) has revolutionized infertility treatment, benefiting millions of couples worldwide. However, current clinical practices for embryo selection rely heavily on visual inspection of morphology, which is highly variable and experience dependent. Here, we propose a comprehensive artificial intelligence (AI) system that can interpret embryo-developmental knowledge encoded in vast unlabeled multi-modal datasets and provide personalized embryo selection. This AI platform consists of a transformer-based network backbone named IVFormer and a self-supervised learning framework, VTCLR (visual-temporal contrastive learning of representations), for training multi-modal embryo representations pre-trained on large and unlabeled data. When evaluated on clinical scenarios covering the entire IVF cycle, our pre-trained AI model demonstrates accurate and reliable performance on euploidy ranking and live-birth occurrence prediction. For AI vs. physician for euploidy ranking, our model achieved superior performance across all score categories. The results demonstrate the potential of the AI system as a non-invasive, efficient, and cost-effective tool to improve embryo selection and IVF outcomes.","PeriodicalId":36242,"journal":{"name":"Patterns","volume":"75 1","pages":""},"PeriodicalIF":6.5,"publicationDate":"2024-05-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140832827","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

CURE: A deep learning framework pre-trained on large-scale patient data for treatment effect estimation CURE：在大规模患者数据上预先训练的深度学习框架，用于估计治疗效果

IF 6.5

Patterns Pub Date : 2024-05-01 DOI: 10.1016/j.patter.2024.100973

Ruoqi Liu, Pin-Yu Chen, Ping Zhang

引用次数: 0

PYPE: A pipeline for phenome-wide association and Mendelian randomization in investigator-driven biobank scale analysis PYPE：研究者驱动的生物库规模分析中的全表型关联和孟德尔随机化管道

IF 6.5

Patterns Pub Date : 2024-05-01 DOI: 10.1016/j.patter.2024.100982

Taykhoom Dalal, Chirag J. Patel

引用次数: 0

The landscape of biomedical research 生物医学研究的前景

IF 6.5

Patterns Pub Date : 2024-04-09 DOI: 10.1016/j.patter.2024.100968

Rita González-Márquez, Luca Schmidt, Benjamin M. Schmidt, Philipp Berens, Dmitry Kobak

引用次数: 0

Improving antibody language models with native pairing 利用母语配对改进抗体语言模型

IF 6.5

Patterns Pub Date : 2024-04-04 DOI: 10.1016/j.patter.2024.100967

Sarah M. Burbach, Bryan Briney

引用次数: 0

Enhancing molecular design efficiency: Uniting language models and generative networks with genetic algorithms 提高分子设计效率：将语言模型和生成网络与遗传算法相结合

IF 6.5

Patterns Pub Date : 2024-03-14 DOI: 10.1016/j.patter.2024.100947

Debsindhu Bhowmik, Pei Zhang, Zachary Fox, Stephan Irle, John Gounley

{"title":"Enhancing molecular design efficiency: Uniting language models and generative networks with genetic algorithms","authors":"Debsindhu Bhowmik, Pei Zhang, Zachary Fox, Stephan Irle, John Gounley","doi":"10.1016/j.patter.2024.100947","DOIUrl":"https://doi.org/10.1016/j.patter.2024.100947","url":null,"abstract":"This study examines the effectiveness of generative models in drug discovery, material science, and polymer science, aiming to overcome constraints associated with traditional inverse design methods relying on heuristic rules. Generative models generate synthetic data resembling real data, enabling deep learning model training without extensive labeled datasets. They prove valuable in creating virtual libraries of molecules for material science and facilitating drug discovery by generating molecules with specific properties. While generative adversarial networks (GANs) are explored for these purposes, mode collapse restricts their efficacy, limiting novel structure variability. To address this, we introduce a masked language model (LM) inspired by natural language processing. Although LMs alone can have inherent limitations, we propose a hybrid architecture combining LMs and GANs to efficiently generate new molecules, demonstrating superior performance over standalone masked LMs, particularly for smaller population sizes. This hybrid LM-GAN architecture enhances efficiency in optimizing properties and generating novel samples.","PeriodicalId":36242,"journal":{"name":"Patterns","volume":"2 1","pages":""},"PeriodicalIF":6.5,"publicationDate":"2024-03-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140169430","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