Artificial intelligence in the life sciences最新文献

{"title":"A supervised machine learning workflow for the reduction of highly dimensional biological data","authors":"Linnea K. Andersen ,&nbsp;Benjamin J. Reading","doi":"10.1016/j.ailsci.2023.100090","DOIUrl":"https://doi.org/10.1016/j.ailsci.2023.100090","url":null,"abstract":"<div><p>Recent technological advancements have revolutionized research capabilities across the biological sciences by enabling the collection of large data that provides a broader picture of systems from the cellular to ecosystem level at a more refined resolution. The rapid rate of generating these data has exacerbated bottlenecks in study design and data analysis approaches, especially as conventional methods that incorporate traditional statistical tests and assumptions are not suitable or sufficient for highly dimensional data (i.e., more than 1,000 variables). The application of machine learning techniques in large data analysis is one promising solution that is increasingly popular. However, limitations in expertise such that the results from machine learning models can be interpreted to gain meaningful biological insight pose a great challenge. To address this challenge, a user-friendly machine learning workflow that can be applied to a wide variety of data types to reduce these large data to those variables (attributes) most determinant of experimental and/or observed conditions is provided, as well as a general overview of data analysis and machine learning approaches and considerations thereof. The workflow presented here has been beta-tested with great success and is recommended to be incorporated into analysis pipelines of large data as a standardized approach to reduce data dimensionality. Moreover, the workflow is flexible, and the underlying concepts and steps can be modified to best suit user needs, objectives, and study parameters.</p></div>","PeriodicalId":72304,"journal":{"name":"Artificial intelligence in the life sciences","volume":"5 ","pages":"Article 100090"},"PeriodicalIF":0.0,"publicationDate":"2024-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S266731852300034X/pdfft?md5=c41b31c74fb0a867fbb87db01c8f6190&pid=1-s2.0-S266731852300034X-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138739061","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Rationalism in the face of GPT hypes: Benchmarking the output of large language models against human expert-curated biomedical knowledge graphs","authors":"Negin Sadat Babaiha ,&nbsp;Sathvik Guru Rao ,&nbsp;Jürgen Klein ,&nbsp;Bruce Schultz ,&nbsp;Marc Jacobs ,&nbsp;Martin Hofmann-Apitius","doi":"10.1016/j.ailsci.2024.100095","DOIUrl":"https://doi.org/10.1016/j.ailsci.2024.100095","url":null,"abstract":"<div><p>Biomedical knowledge graphs (KGs) hold valuable information regarding biomedical entities such as genes, diseases, biological processes, and drugs. KGs have been successfully employed in challenging biomedical areas such as the identification of pathophysiology mechanisms or drug repurposing. The creation of high-quality KGs typically requires labor-intensive multi-database integration or substantial human expert curation, both of which take time and contribute to the workload of data processing and annotation. Therefore, the use of automatic systems for KG building and maintenance is a prerequisite for the wide uptake and utilization of KGs. Technologies supporting the automated generation and updating of KGs typically make use of Natural Language Processing (NLP), which is optimized for extracting implicit triples described in relevant biomedical text sources. At the core of this challenge is how to improve the accuracy and coverage of the information extraction module by utilizing different models and tools. The emergence of pre-trained large language models (LLMs), such as ChatGPT which has grown in popularity dramatically, has revolutionized the field of NLP, making them a potential candidate to be used in text-based graph creation as well. So far, no previous work has investigated the power of LLMs on the generation of cause-and-effect networks and KGs encoded in Biological Expression Language (BEL). In this paper, we present initial studies towards one-shot BEL relation extraction using two different versions of the Generative Pre-trained Transformer (GPT) models and evaluate its performance by comparing the extracted results to a highly accurate, manually curated BEL KG curated by domain experts.</p></div>","PeriodicalId":72304,"journal":{"name":"Artificial intelligence in the life sciences","volume":"5 ","pages":"Article 100095"},"PeriodicalIF":0.0,"publicationDate":"2024-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2667318524000023/pdfft?md5=9137dd2a207653e4d13cb5b99ca17d48&pid=1-s2.0-S2667318524000023-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139710160","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Origins and progression of the polypharmacology concept in drug discovery","authors":"Jürgen Bajorath","doi":"10.1016/j.ailsci.2024.100094","DOIUrl":"https://doi.org/10.1016/j.ailsci.2024.100094","url":null,"abstract":"","PeriodicalId":72304,"journal":{"name":"Artificial intelligence in the life sciences","volume":"5 ","pages":"Article 100094"},"PeriodicalIF":0.0,"publicationDate":"2024-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2667318524000011/pdfft?md5=ef2f5411ede3a24f3429765640c3360c&pid=1-s2.0-S2667318524000011-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139107191","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Data and code availability requirements in open science and consequences for different research environments","authors":"Jürgen Bajorath","doi":"10.1016/j.ailsci.2023.100085","DOIUrl":"10.1016/j.ailsci.2023.100085","url":null,"abstract":"","PeriodicalId":72304,"journal":{"name":"Artificial intelligence in the life sciences","volume":"4 ","pages":"Article 100085"},"PeriodicalIF":0.0,"publicationDate":"2023-12-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"47255704","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}

{"title":"Modeling and survival exploration of breast carcinoma: A statistical, maximum likelihood estimation, and artificial neural network perspective","authors":"Anum Shafiq ,&nbsp;Andaç Batur Çolak ,&nbsp;Tabassum Naz Sindhu ,&nbsp;Showkat Ahmad Lone ,&nbsp;Tahani A. Abushal","doi":"10.1016/j.ailsci.2023.100082","DOIUrl":"10.1016/j.ailsci.2023.100082","url":null,"abstract":"<div><p>The core objective of this research is to describe the behavior of the distribution using the MLE method to estimate its parameters, as well as to determine the optimal Artificial Neural Network method by comparing it to the maximum likelihood estimation method and applying it to real data for breast cancer patients to determine survival, risk, and other survival study functions of the log-logistic distribution. The parameters were defined in the input layer of the artificial neural network developed for the purpose of survival analysis and reliability function, hazard rate function, probability density function, reserved hazard rate function, Mills ratio, Odd function and CHR values were obtained in the output layer. The findings show that risk function increases with the increase in the time of infection and then decreases for a group of breast cancer patients under study, which corresponds to the theoretical properties of this according to the practical conclusions. The examination of survival analysis reveals that practical conclusions correspond to the theoretical properties of log-logistic distribution. Artificial neural networks have proven to be one of the ideal tools that can be used to predict various vital parameters, especially survival of cancer patients, with their high predictive capabilities.</p></div>","PeriodicalId":72304,"journal":{"name":"Artificial intelligence in the life sciences","volume":"4 ","pages":"Article 100082"},"PeriodicalIF":0.0,"publicationDate":"2023-12-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"41536157","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}

{"title":"Experimental Uncertainty in Training Data for Protein-Ligand Binding Affinity Prediction Models","authors":"Carlos A. Hernández-Garrido ,&nbsp;Norberto Sánchez-Cruz","doi":"10.1016/j.ailsci.2023.100087","DOIUrl":"https://doi.org/10.1016/j.ailsci.2023.100087","url":null,"abstract":"<div><p>The accuracy of machine learning models for protein-ligand binding affinity prediction depends on the quality of the experimental data they are trained on. Most of these models are trained and tested on different subsets of the PDBbind database, which is the main source of protein-ligand complexes with annotated binding affinity in the public domain. However, estimating its experimental uncertainty is not straightforward because just a few protein-ligand complexes have more than one measurement associated. In this work, we analyze bioactivity data from ChEMBL to estimate the experimental uncertainty associated with the three binding affinity measures included in the PDBbind (K_i, K_d, and IC₅₀), as well as the effect of combining them. The experimental uncertainty of combining these three affinity measures was characterized by a mean absolute error of 0.78 logarithmic units, a root mean square error of 1.04 and a Pearson correlation coefficient of 0.76. These estimations were contrasted with the performances obtained by state-of-the-art machine learning models for binding affinity prediction, showing that these models tend to be overoptimistic when evaluated on the core set from PDBbind.</p></div>","PeriodicalId":72304,"journal":{"name":"Artificial intelligence in the life sciences","volume":"4 ","pages":"Article 100087"},"PeriodicalIF":0.0,"publicationDate":"2023-12-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"49711349","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}

{"title":"Deep neural network architectures for cardiac image segmentation","authors":"Jasmine El-Taraboulsi ,&nbsp;Claudia P. Cabrera ,&nbsp;Caroline Roney ,&nbsp;Nay Aung","doi":"10.1016/j.ailsci.2023.100083","DOIUrl":"10.1016/j.ailsci.2023.100083","url":null,"abstract":"<div><p>Imaging plays a fundamental role in the effective diagnosis, staging, management, and monitoring of various cardiac pathologies. Successful radiological analysis relies on accurate image segmentation, a technically arduous process, prone to human-error. To overcome the laborious and time-consuming nature of cardiac image analysis, deep learning approaches have been developed, enabling the accurate, time-efficient, and highly personalised diagnosis, staging and management of cardiac pathologies.</p><p>Here, we present a review of over 60 papers, proposing deep learning models for cardiac image segmentation. We summarise the theoretical basis of Convolutional Neural Networks, Fully Convolutional Neural Networks, U-Net, V-Net, No-New-U-Net (nnU-Net), Transformer Networks, DeepLab, Generative Adversarial Networks, Auto Encoders and Recurrent Neural Networks. In addition, we identify pertinent performance-enhancing measures including adaptive convolutional kernels, atrous convolutions, attention gates, and deep supervision modules.</p><p>Top-performing models in ventricular, myocardial, atrial and aortic segmentation are explored, highlighting U-Net and nnU-Net-based model architectures achieving state-of-the art segmentation accuracies. Additionally, key gaps in the current research and technology are identified, and areas of future research are suggested, aiming to guide the innovation and clinical adoption of automated cardiac segmentation methods.</p></div>","PeriodicalId":72304,"journal":{"name":"Artificial intelligence in the life sciences","volume":"4 ","pages":"Article 100083"},"PeriodicalIF":0.0,"publicationDate":"2023-12-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"45888732","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}

{"title":"First-generation themed article collections","authors":"Jürgen Bajorath,&nbsp;Steve Gardner,&nbsp;Francesca Grisoni,&nbsp;Carolina Horta Andrade,&nbsp;Johannes Kirchmair,&nbsp;Melissa Landon,&nbsp;José L. Medina-Franco,&nbsp;Filip Miljković,&nbsp;Floriane Montantari,&nbsp;Raquel Rodríguez-Pérez","doi":"10.1016/j.ailsci.2023.100088","DOIUrl":"https://doi.org/10.1016/j.ailsci.2023.100088","url":null,"abstract":"","PeriodicalId":72304,"journal":{"name":"Artificial intelligence in the life sciences","volume":"4 ","pages":"Article 100088"},"PeriodicalIF":0.0,"publicationDate":"2023-12-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2667318523000326/pdfft?md5=cf39af5e517634e52f28e066ee1fd1da&pid=1-s2.0-S2667318523000326-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"136697050","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Analysis of Swin-UNet vision transformer for Inferior Vena Cava filter segmentation from CT scans","authors":"Rahul Gomes ,&nbsp;Tyler Pham ,&nbsp;Nichol He ,&nbsp;Connor Kamrowski ,&nbsp;Joseph Wildenberg","doi":"10.1016/j.ailsci.2023.100084","DOIUrl":"10.1016/j.ailsci.2023.100084","url":null,"abstract":"<div><h3>Purpose</h3><p>The purpose of this study is to develop an accurate deep learning model capable of Inferior Vena Cava (IVC) filter segmentation from CT scans. The study does a comparative assessment of the impact of Residual Networks (ResNets) complemented with reduced convolutional layer depth and also analyzes the impact of using vision transformer architectures without performance degradation.</p></div><div><h3>Materials and Methods</h3><p>This experimental retrospective study on 84 CT scans consisting of 54618 slices involves design, implementation, and evaluation of segmentation algorithm which can be used to generate a clinical report for the presence of IVC filters on abdominal CT scans performed for any reason. Several variants of patch-based 3D-Convolutional Neural Network (CNN) and the Swin UNet Transformer (Swin-UNETR) are used to retrieve the signature of IVC filters. The Dice Score is used as a metric to compare the performance of the segmentation models.</p></div><div><h3>Results</h3><p>Model trained on UNet variant using four ResNet layers showed a higher segmentation performance achieving median Dice = 0.92 [Interquartile range(IQR): 0.85, 0.93] compared to the plain UNet model with four layers having median Dice = 0.89 [IQR: 0.83, 0.92]. Segmentation results from ResNet with two layers achieved a median Dice = 0.93 [IQR: 0.87, 0.94] which was higher than the plain UNet model with two layers at median Dice = 0.87 [IQR: 0.77, 0.90]. Models trained using SWIN-based transformers performed significantly better in both training and validation datasets compared to the four CNN variants. The validation median Dice was highest in 4 layer Swin UNETR at 0.88 followed by 2 layer Swin UNETR at 0.85.</p></div><div><h3>Conclusion</h3><p>Utilization of vision based transformer Swin-UNETR results in segmentation output with both low bias and variance thereby solving a real-world problem within healthcare for advanced Artificial Intelligence (AI) image processing and recognition. The Swin UNETR will reduce the time spent manually tracking IVC filters by centralizing within the electronic health record. Link to <span>GitHub</span><svg><path></path></svg> repository.</p></div>","PeriodicalId":72304,"journal":{"name":"Artificial intelligence in the life sciences","volume":"4 ","pages":"Article 100084"},"PeriodicalIF":0.0,"publicationDate":"2023-12-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"46348564","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}

{"title":"Discovery of novel A2AR antagonists through deep learning-based virtual screening","authors":"Miru Tang ,&nbsp;Chang Wen ,&nbsp;Jie Lin ,&nbsp;Hongming Chen ,&nbsp;Ting Ran","doi":"10.1016/j.ailsci.2023.100058","DOIUrl":"10.1016/j.ailsci.2023.100058","url":null,"abstract":"<div><p>The A_2A adenosine receptor (A_2AR) is emerging as a promising drug target for cancer immunotherapy. Novel A_2AR antagonists are highly demanded due to few candidates entering clinic trials specific for cancer treatment. Structure-based virtual screening has made a great contribution to discover novel A_2AR antagonists, but most depended on inefficient molecular docking on relatively small molecular databases. In this work, a deep learning strategy was applied to accelerate docking-based virtual screening, through which new structural types of A_2AR antagonists for an extremely large molecular library were found successfully.</p></div>","PeriodicalId":72304,"journal":{"name":"Artificial intelligence in the life sciences","volume":"3 ","pages":"Article 100058"},"PeriodicalIF":0.0,"publicationDate":"2023-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"46817392","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}