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arXiv 2024年

作者： Kasif, Simon Department of Biomedical Engineering Program in Bioinformatics Department of Computer Science Boston University United States

AI Safety has become a vital front-line concern of many scientists within and outside the AI community. There are many immediate and long term anticipated risks that range from existential risk to human existence to deep fakes and bias in machine learning systems [1-5]. In this paper, we reduce the full scope and immense complexity of AI safety concerns to a trilogy of three important but tractable opportunities for advances that have the short-term potential to improve AI safety and reliability without reducing AI innovation in critical domains. In this perspective, we discuss this vision based on several case studies that already produced proofs of concept in critical ML applications in biomedical science. © 2024, CC BY-NC-ND.

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A retinoic acid:YAP1 signaling axis controls atrial lineage commitment

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Cell Reports 2025年第5期44卷 115687页

作者： Abraham, Elizabeth Kostina, Aleksandra Volmert, Brett Roule, Thomas Huang, Ling Yu, Jingting Williams, April E. Megill, Emily Douglas, Aidan Pericak, Olivia M. Morris, Alex Stronati, Eleonora Larrinaga-Zamanillo, Arantza Fueyo, Raquel Zubillaga, Mikel Andrake, Mark D. Akizu, Naiara Aguirre, Aitor Estaras, Conchi Department of Cardiovascular Sciences Aging + Cardiovascular Discovery Center Lewis Katz School of Medicine Temple University Philadelphia 19140 PA United States Institute for Quantitative Health Science and Engineering Division of Developmental and Stem Cell Biology Michigan State University East Lansing 48824 MI United States Department of Biomedical Engineering College of Engineering Michigan State University East Lansing 48824 MI United States Raymond G. Perelman Center for Cellular and Molecular Therapeutics The Children's Hospital of Philadelphia Philadelphia 19104 PA United States Integrative Genomics and Bioinformatics Core Salk Institute for Biological Studies La Jolla 92037 CA United States Department of Child and Adolescence Psychiatry The Children's Hospital of Philadelphia Philadelphia 19104 PA United States Department of Chemical and Systems Biology Stanford University School of Medicine Stanford 94305 CA United States Cancer Epigenetics Institute Fox Chase Cancer Center Philadelphia 19111 PA United States Molecular Modeling Facility Program in Cancer Signaling and Microenvironment Fox Chase Cancer Center Philadelphia 19111 PA United States

In cardiac progenitor cells (CPCs), retinoic acid (RA) signaling induces atrial lineage gene expression and acquisition of an atrial cell fate. To achieve this, RA coordinates a complex regulatory network of downstream effectors that is not fully identified. To address this gap, we applied a functional genomics approach (i.e., scRNA-seq and snATAC-seq) to untreated and RA-treated human embryonic stem cell (hESC)-derived CPCs. Unbiased analysis revealed that the Hippo effectors YAP1 and TEAD4 are integrated with the atrial transcription factor enhancer network and that YAP1 activates RA enhancers in CPCs. Furthermore, Yap1 deletion in mouse embryos compromises the expression of RA-induced genes, such as Nr2f2, in the CPCs of the second heart field. Accordingly, in hESC-derived patterned heart organoids, YAP1 regulates the formation of an atrial chamber but is dispensable for the formation of a ventricle. Overall, our findings revealed that YAP1 cooperates with RA signaling to induce atrial lineages during cardiogenesis. © 2025 The Author(s)

关键词： atrial differentiation cardiac enhancers cardiac progenitors chamber development CP: Developmental biology heart organoids NR2F2 retinoic acid signaling SHF TEAD4 YAP1

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Multiplexed Nanoscopy via Buffer Exchange

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ACS Nano 2024年第34期18卷 23445-23456页

作者： Chang, Ting-Jui Ben Yang, T. Tony Department of Electrical Engineering National Taiwan University Taipei10617 Taiwan Graduate Institute of Biomedical Electronics and Bioinformatics National Taiwan University Taipei10617 Taiwan Department of Physics National Taiwan University Taipei10617 Taiwan Nano Science and Technology Program Taiwan International Graduate Program Academia Sinica and National Taiwan University Taipei10617 Taiwan

Understanding cellular functions, particularly in their intricate complexity, can greatly benefit from the spatial mapping of diverse molecules through multitarget single-molecule localization microscopy (SMLM). Existing methodologies, primarily restricting the encoding dimensions to color and lifetime or requiring cyclic staining, often involve broad chromatic detection, specialized optical configurations, or sophisticated labeling techniques. Here, we propose a simple approach called buffer-exchange stochastic optical reconstruction microscopy (beSTORM), which introduces an additional dimension to differentiate between single molecules irrespective of their spectral properties. This method leverages the distinguishable photoblinking responses to distinct buffer conditions, offering a straightforward yet effective means of fluorophore discrimination. Through buffer exchanges, beSTORM achieves multitarget SMLM imaging with minimal crosstalk. Direct integration with expansion microscopy (ExM) demonstrates its capability to resolve up to six proteins at the molecular level within a single emission color without chromatic aberration. Overall, beSTORM presents a highly compatible imaging platform, promising significant advancements in highly multiplexed nanoscopy for exploring multiple targets in biological systems with nanoscale precision. © 2024 The Authors. Published by American Chemical Society.

关键词： Stochastic systems

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Learning modular structures from network data and node variables 31

Learning modular structures from network data and node varia...

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31st International Conference on Machine Learning, ICML 2014

作者： Azizi, Elham Airoldi, Edoardo M. Galagan, James E. Bioinformatics Program Boston University BostonMA02215 United States Department of Statistics Harvard University CamrbdigeMA02138 United States Departments of Biomedical Engineering and Microbiology Boston University BostonMA02215 United States

ISBN: (纸本)9781634393973

A standard technique for understanding underlying dependency structures among a set of variables posits a shared conditional probability distribution for the variables measured on individ-uals within a group. This approach is often referred to as module networks, where individuals are represented by nodes in a network, groups are termed modules, and the focus is on estimating the network structure among modules. However, estimation solely from node-specific variables can lead to spurious dependencies, and un- verifiable structural assumptions are often used for regularization. Here, we propose an extended model that leverages direct observations about the network in addition to node-specific variables. By integrating complementary data types, we avoid the need for structural assumptions. We illustrate theoretical and practical significance of the model and develop a reversible- jump MCMC learning procedure for learning modules and model parameters. We demonstrate the method accuracy in predicting modular structures from synthetic data and capability to learn regulatory modules in the Mycobacterium tuberculosis gene regulatory network. Copyright © (2014) by the International Machine Learning Society (IMLS) All rights reserved.

关键词： Probability distributions

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Noninvasive assessment of hydroquinidine effect in Brugada syndrome (QUIET BrS)

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Heart Rhythm 2025年

作者： Isbister, Julia C. Strocchi, Marina Riedy, Matthew Yeates, Laura Gray, Belinda Singer, Emma S. Bagnall, Richard D. Ingles, Jodie Raju, Hariharan Semsarian, Christopher Niederer, Steven A. Sy, Raymond W. Faculty of Medicine and Health University of Sydney Sydney NSW Australia Department of Cardiology Royal Prince Alfred Hospital Sydney NSW Australia National Heart and Lung Institute Imperial College London London United Kingdom School of Biomedical Engineering and Imaging Sciences King's College London London United Kingdom Medtronic Australasia Sydney NSW Australia Agnes Ginges Centre for Molecular Cardiology at Centenary Institute The University of Sydney Sydney NSW Australia Genomics and Inherited Disease Program Garvan Institute of Medical Research University of New South Wales Sydney NSW Australia Bioinformatics and Molecular Genetics Group at Centenary Institute The University of Sydney Sydney NSW Australia Faculty of Medicine Health and Human Sciences Macquarie University Sydney NSW Australia The Alan Turing Institute London United Kingdom

Background: Hydroquinidine reduces arrhythmic events in patients with Brugada syndrome (BrS). The mechanism by which it exerts antiarrhythmic benefit and its electrophysiological effects on BrS substrate remain incompletely understood. Objective: This study aimed to determine the effect of hydroquinidine on ventricular depolarization and repolarization in patients with BrS in vivo. Methods: Twelve patients with BrS underwent electrocardiography (standard, high-lead, and signal averaged) and electrocardiographic imaging at baseline and “on-treatment” with hydroquinidine 300 mg twice daily. ST-segment elevation, activation time, repolarization time, and activation-recovery interval (ARI) were computed for the ventricles and right ventricular outflow tract (RVOT). Serum hydroquinidine levels were determined, and adverse drug events were captured through a medication survey. Results: Hydroquinidine increased repolarization time (301.1 ± 24.1 ms vs 348.8 ± 28.3 ms;P<.001), repolarization gradients (1.1 ± 0.4 ms/mm vs 1.6 ± 0.4 ms/mm;P<0.001), and ARI (241.3 ± 18.1 ms vs 284.8 ± 21.5 ms;P<.001) in the RVOT, with a greater change in the RVOT than in the rest of the ventricles. In contrast, activation parameters did not change significantly on-treatment with hydroquinidine, although there was a subtle increase in ST-segment elevation over the RVOT (1.5 ± 0.7 mV vs 1.8 ± 0.8 mV;P<.001). Hydroquinidine levels did not correlate with electrophysiological changes or occurrence of adverse drug reactions. One patient developed frequent nonsustained ventricular tachycardia on-treatment with hydroquinidine. Conclusion: Hydroquinidine primarily affects ventricular repolarization and action potential duration (indicated by ARI) in patients with BrS and demonstrates regional variation with more significant changes in the RVOT. © 2024 Heart Rhythm Society

关键词： Activation-recovery interval Brugada syndrome Electrocardiographic imaging Hydroquinidine Noninvasive Ventricular activation Ventricular repolarization

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Modern views of ancient metabolic networks

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Current Opinion in Systems Biology 2018年 8卷 117-124页

作者： Goldford, Joshua E. Segrè, Daniel Bioinformatics Program and Biological Design Center Boston University Boston 02215 MA United States Department of Biology Boston University Boston 02215 MA United States Department of Biomedical Engineering Boston University Boston 02215 MA United States Department of Physics Boston University Boston 02215 MA United States

Metabolism is a molecular, cellular, ecological and planetary phenomenon, whose fundamental principles are likely at the heart of what makes living matter different from inanimate one. Systems biology approaches developed for the quantitative analysis of metabolism at multiple scales can help understand metabolism's ancient history. In this review, we highlight work that uses network-level approaches to shed light on key innovations in ancient life, including the emergence of proto-metabolic networks, collective autocatalysis and bioenergetics coupling. Recent experiments and computational analyses have revealed new aspects of this ancient history, paving the way for the use of large datasets to further improve our understanding of life's principles and abiogenesis. © 2018 The Authors

关键词： Evolution Metabolism Network expansion Origin of life

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Serum proteomic approach to identifying differentially expressed proteins in effusive feline infectious peritonitis

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Scientific Reports 2025年第1期15卷 1-12页

作者： Wassamon Moyadee Jatuporn Rattanasrisomporn Sittiruk Roytrakul Janthima Jaresitthikunchai Narumon Phaonakrop Kiattawee Choowongkomon Sekkarin Ploypetch Natthasit Tansakul Amonpun Rattanasrisomporn Graduate Program in Animal Health and Biomedical Sciences Faculty of Veterinary Medicine Kasetsart University Bangkok Thailand Department of Companion Animal Clinical Sciences Faculty of Veterinary Medicine Kasetsart University Bangkok Thailand National Center for Genetic Engineering and Biotechnology National Science and Technology Development Agency Pathum Thani Thailand Department of Biochemistry Faculty of Science Kasetsart University Bangkok Thailand Department of Clinical Sciences and Public Health Faculty of Veterinary Science Mahidol University Nakhon Pathom Thailand Department of Pharmacology Faculty of Veterinary Medicine Kasetsart University Bangkok Thailand Interdisciplinary of Genetic Engineering and Bioinformatics Graduate School Kasetsart University Bangkok Thailand

Feline infectious peritonitis (FIP) is a lethal, viral-induced immune-mediated disease that remains a challenge for diagnosis and treatment in cats. Proteomic profiling, which analyzes the protein content of biological samples, offers the potential to identify novel biomarkers that could improve the diagnosis and management of FIP. This study aims to assess the serum proteome and identify proteins that differentiate healthy cats from cats diagnosed with effusive FIP using liquid chromatography coupled with tandem mass spectrometry (LC–MS/MS). A total of 30 cats diagnosed with effusive FIP and 27 clinically normal cats were enrolled. Twenty-three proteins were significantly (p< 0.01, ≥ fivefold change in abundance) differentially expressed between cats with effusive FIP and controls. Among these, the P2X purinoceptor, DNA topoisomerase, Notch receptor 2, and cadherin-17 were identified as key proteins of interest in cats with effusive FIP. Our findings suggest that these differentially expressed proteins could serve as potential diagnostic biomarkers and therapeutic targets for FIP. However, further studies are needed to validate these findings and explore their potential applications.

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Using a Recurrent Neural Network Model for Classification of Tweets Conveyed Influenza-related Information 1

Using a Recurrent Neural Network Model for Classification of...

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1st International Workshop on Digital Disease Detection using Social Media, DDDSM 2017, co-located with the 8th International Joint Conference on Natural Language Processing, IJCNLP 2017

作者： Wang, Chen-Kai Singh, Onkar Tang, Zhao-Li Dai, Hong-Jie Graduate Institute of Biomedical Informatics Taipei Medical University Taipei Taiwan Bioinformatics Program Taiwan International Graduate Program Institute of Information Science Academia Sinica Taipei Taiwan Institute of Biomedical Informatics National Yang-Ming University Taipei Taiwan Department of Computer Science and Information Engineering National Taitung University Taitung Taiwan Interdisciplinary Program of Green and Information Technology National Taitung University Taitung Taiwan

ISBN: (纸本)9781948087070

Traditional disease surveillance systems depend on outpatient reporting and virological test results released by hospitals. These data have valid and accurate information about emerging outbreaks but it's often not timely. In recent years the exponential growth of users getting connected to social media provides immense knowledge about epidemics by sharing related information. Social media can now flag more immediate concerns related to outbreaks in real time. In this paper we apply the long short-term memory recurrent neural network (RNN) architecture to classify tweets conveyed influenza-related information and compare its performance with baseline algorithms including support vector machine (SVM), decision tree, naive Bayes, simple logistics, and naive Bayes multinomial. The developed RNN model achieved an F-score of 0.845 on the MedWeb task test set, which outperforms the F-score of SVM without applying the synthetic minority oversampling technique by 0.08. The F-score of the RNN model is within 1% of the highest score achieved by SVM with oversampling technique. © 2017 AFNLP

关键词： Support vector machines

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Learning Cellular Objectives from Fluxes by Inverse Optimization

Learning Cellular Objectives from Fluxes by Inverse Optimiza...

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IEEE Annual Conference on Decision and Control

作者： Qi Zhao Arion Stettner Ed Reznik Daniel Segre Ioannis Ch. Paschalidis Dept. of Electrical and Computer Eng. and Division of Systems Engineering Boston University Department of Biomedical Engineering Boston University Bioinformatics Program Department of Biology and Department of Biomedical Engineering Boston University

ISBN: (纸本)9781479978878

Flux Balance Analysis (FBA) is a widely used approach for studying biochemical networks, and in particular the genome-scale metabolic network reconstructions. It formulates the problem of predicting a cell's chemical reaction fluxes as the linear optimization problem of maximizing a cellular objective (e.g., growth) subject to constraints capturing stoichiometry mass balances of the metabolic network and bounds that reflect the composition of the growth medium. In practice, however, reaction fluxes of the cells under specific growth conditions are available to be measured, but the primal FBA objective function is not necessarily known. Understanding its structure can elucidate the cellular metabolic control mechanisms and infer important information regarding an organism's evolution. To that end, we have developed an Inverse Flux Balance Analysis (InvFBA) method which is a novel inverse optimization-based framework for inferring metabolic objective functions. Within this framework, we present three different forms of objective functions: linear, quadratic, and non-parametric. We show that in all cases, the inverse problem is tractable and can be solved efficiently. We provide several numerical examples to show that the inference of the objective function is consistent with simulated flux data and actual measurements.

关键词： Linear programming Kernel Optimization Biochemistry Biomass biomedical measurement

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Computational prediction of binding hotspots

Computational prediction of binding hotspots

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Annual International Conference of the IEEE engineering in Medicine and Biology Society (EMBC)

作者： W. Tong L. Li Z. Weng Department of Biomedical Engineering Boston University Boston MA USA Bioinformatics Program Boston University Boston MA USA

We combine side-chain modeling, energy minimization and binding free energy calculation to predict point mutations with significant impacts on binding affinities (binding hotspots). Our method achieves high accuracy for two datasets (alanine-scanning mutations in ASEdb and 570 mutations on protease-inhibitor complexes). In particular, we can predict mutations that lead to improved binding with success. We discuss various factors that may contribute the prediction accuracy, including the amino acid to mutate to, and the position of the mutation.

关键词： Proteins Genetic mutations Amino acids Accuracy Libraries Predictive models Electrostatics Databases Biochemistry biomedical measurements

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