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Machine learning approaches enable the discovery of therapeutics across domains

Molecular Therapy 2025年第5期33卷 2269-2278页

作者： Chhibbar, Prabal Das, Jishnu Centre for Systems Immunology Department of Immunology School of Medicine University of Pittsburgh Pittsburgh 15213 PA United States Department of Computational and Systems Biology School of Medicine University of Pittsburgh Pittsburgh 15213 PA United States Integrative Systems Biology PhD Program School of Medicine University of Pittsburgh Pittsburgh 15213 PA United States

Multi-modal datasets have grown exponentially in the last decade. This has created an enormous demand for machine learning models that can predict complex outcomes by leveraging cellular, molecular, and humoral profiles. Corresponding inference of mechanisms can help to uncover new therapeutic targets. Here, we discuss how biological principles guide the design of predictive models and how interpretable machine learning can lead to novel mechanistic insights. We provide descriptions of multiple learning techniques and how suited they are to domain adaptations. Finally, we talk about broad learning capabilities of foundation models on large datasets and whether they can be used to provide meaningful inference about biological datasets. © 2025 The Author(s)

关键词： deep learning foundation models interpretable machine learning therapeutics

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Dish-Trend: Intervention Modeling Simulator That Accounts for Trend Influences

Dish-Trend: Intervention Modeling Simulator That Accounts fo...

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2021 Winter Simulation Conference, WSC 2021

作者： Andjelkovic, Stefan Miskov-Zivanov, Natasa Computational and Systems Biology Joint Carnegie Mellon University University of Pittsburgh Computational Biology PhD Program University of Pittsburgh PittsburghPA15213 United States Electrical and Computer Engineering Bioengineering Computational and Systems Biology University of Pittsburgh PittsburghPA15213 United States

ISBN: (纸本)9781665433112

Simulation on directed graphs is an important method for understanding the dynamics in the systems where connectivity graphs contain cycles. Discrete Stochastic Heterogeneous Simulator (DiSH) is one of the simulation tools with wide application, which uses regulator values to calculate state updates of regulated elements. Here we present a new simulation approach DiSH-trend which also takes into account the trends in regulating elements. We demonstrate the features of trend-based regulation, as well as hybrid regulation, which is a combination of the trend-and level-based approaches. The modeling capabilities are demonstrated on a small toy model, showcasing different functionalities. Real-world capabilities are demonstrated on a larger network model of food insecurity in the Ethiopian region Oromia. Adding trend-based regulation to models results in increased modeling flexibility, and hybrid regulation improves qualitative dynamic behavior prediction. With appropriate data, DiSH-trend becomes a powerful tool for exploring intervention strategies. © 2021 IEEE.

关键词： Directed graphs

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High-resolution dynamic imaging of chromatin DNA communication using Oligo-LiveFISH

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Cell 2025年

作者： Zhu, Yanyu Balaji, Ashwin Han, Mengting Andronov, Leonid Roy, Anish R. Wei, Zheng Chen, Crystal Miles, Leanne Cai, Sa Gu, Zhengxi Tse, Ariana Yu, Betty Chentzu Uenaka, Takeshi Lin, Xueqiu Spakowitz, Andrew J. Moerner, W.E. Qi, Lei S. Department of Bioengineering Stanford University Stanford 94305 CA United States Department of Chemistry Stanford University Stanford 94305 CA United States Biophysics PhD Program Stanford University Stanford 94305 CA United States Computational Biology Program Public Health Sciences Division and Translational Data Science IRC Fred Hutchinson Cancer Center Seattle 98109 WA United States Department of Chemical Engineering Stanford University Stanford 94305 CA United States Department of Materials Science and Engineering Stanford University Stanford 94305 CA United States Institute for Stem Cell Biology & Regenerative Medicine and Department of Pathology Stanford University School of Medicine Stanford 94305 CA United States Sarafan ChEM-H Stanford University Stanford 94305 CA United States Chan Zuckerberg Biohub San Francisco 94080 CA United States

Three-dimensional (3D) genome dynamics are crucial for cellular functions and disease. However, real-time, live-cell DNA visualization remains challenging, as existing methods are often confined to repetitive regions, suffer from low resolution, or require complex genome engineering. Here, we present Oligo-LiveFISH, a high-resolution, reagent-based platform for dynamically tracking non-repetitive genomic loci in diverse cell types, including primary cells. Oligo-LiveFISH utilizes fluorescent guide RNA (gRNA) oligo pools generated by computational design, in vitro transcription, and chemical labeling, delivered as ribonucleoproteins. Utilizing machine learning, we characterized the impact of gRNA design and chromatin features on imaging efficiency. Multi-color Oligo-LiveFISH achieved 20-nm spatial resolution and 50-ms temporal resolution in 3D, capturing real-time enhancer and promoter dynamics. Our measurements and dynamic modeling revealed two distinct modes of chromatin communication, and active transcription slows enhancer-promoter dynamics at endogenous genes like FOS. Oligo-LiveFISH offers a versatile platform for studying 3D genome dynamics and their links to cellular processes and disease. © 2025 Elsevier Inc.

关键词： CRISPR imaging DNA communication dynamic tracking enhancer-promoter interaction high-resolution imaging live DNA imaging non-repetitive genome imaging Oligo-LiveFISH polymer modeling primary cell

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Aminoglycoside activity observed on single pre-translocation ribosome complexes (vol 6, pg 54, 2010)

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NATURE CHEMICAL biology 2010年第3期6卷 244-244页

作者： Feldman, Michael B. Terry, Daniel S. Altman, Roger B. Blanchard, Scott C. Department of Physiology and Biophysics Weill Medical College of Cornell University New York New York USA Weill Cornell/Rockefeller University/Sloan-Kettering Tri-Institutional MD-PhD Program New York New York USA Tri-Institutional Training Program in Computational Biology and Medicine New York New York USA.

Aminoglycoside-class antibiotics bind directly to ribosomal RNA, imparting pleiotropic effects on ribosome function. Despite in-depth structural investigations of aminoglycoside-RNA oligonucleotide and aminoglycoside-ribosome interactions, mechanisms explaining the unique ribosome inhibition profiles of chemically similar aminoglycosides remain elusive. Here, using single-molecule fluorescence resonance energy transfer (smFRET) methods, we show that high-affinity aminoglycoside binding to the conserved decoding site region of the functional pre-translocation ribosome complex specifically remodels the nature of intrinsic dynamic processes within the particle. The extents of these effects, which are distinct for each member of the aminoglycoside class, strongly correlate with their inhibition of EF-G-catalyzed translocation. Neomycin, a 4,5-linked aminoglycoside, binds with lower affinity to one or more secondary binding sites, mediating distinct structural and dynamic perturbations that further enhance translocation inhibition. These new insights help explain why closely related aminoglycosides elicit pleiotropic translation activities and demonstrate the potential utility of smFRET as a tool for dissecting the mechanisms of antibiotic action.

关键词： AMINOGLYCOSIDES

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CIWARS: A Web Server for Antibiotic Resistance Surveillance Using Longitudinal Metagenomic Data

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Journal of Molecular biology 2025年

作者： Emon, Muhit Islam Cheung, Yat Fei Stoll, James Rumi, Monjura Afrin Brown, Connor Choi, Joung Min Moumi, Nazifa Ahmed Ahmed, Shafayat Song, Haoqiu Sein, Justin Yao, Shunyu Khan, Ahmad Gupta, Suraj Kulkarni, Rutwik Butt, Ali Vikesland, Peter Pruden, Amy Zhang, Liqing Department of Computer Science Virginia Tech Blacksburg 24060 VA United States Fralin Life Science Institute Virginia Tech Blacksburg 24060 VA United States Department of Civil and Environmental Engineering Virginia Tech Blacksburg 24060 VA United States The Interdisciplinary PhD Program in Genetics Bioinformatics and Computational Biology Virginia Tech Blacksburg 24060 VA United States

The rise of antibiotic resistance (AR) poses a substantial threat to human and animal health, food security, and economic stability. Wastewater-based surveillance (WBS) has emerged as a powerful strategy for population-level AR monitoring, providing valuable data to guide public health and policy decisions. Metagenomic sequencing is especially promising, as it can yield comprehensive profiles of antibiotic resistance genes (ARGs) and other genes relevant to AR in a single run. However, online analytical platforms to facilitate analysis of longitudinal metagenomic data are lacking. To address this, we introduce CyberInfrastructure for Waterborne Antibiotic Resistance Surveillance (CIWARS), a web server configured for characterizing key AR trends from longitudinal metagenomic WBS data. CIWARS offers comprehensive profiling of ARGs and taxonomic profiling of pathogen-associated bacterial taxonomic groups, identifies potential associations of ARGs with mobile genetic elements (MGEs) and pathogen-containing taxa, and assesses resistome risk based on the co-occurrence of ARGs, MGEs, and pathogen-like sequences. Additionally, it detects anomalous AR indicators over time, aiding in identifying potential events of concern, such as the emergence of resistant strains or outbreaks. Through interactive temporal data visualization, CIWARS enables AR monitoring and can serve as a tool to inform effective and timely interventions to mitigate the spread and transmission of AR. Here, CIWARS is demonstrated using longitudinal metagenomic data from a wastewater treatment plant (WWTP) influent and effluent, but it can be extended to any environment. CIWARS provides a valuable tool to support global efforts to combat the evolution and spread of AR, while also guiding agricultural and public health efforts aimed at optimizing antibiotic use. The web server is freely available at https://***/. © 2025 Elsevier Ltd

关键词： anomaly detection antibiotic resistance (AR) AR indicator profiling longitudinal metagenomics wastewater-based surveillance

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Author Correction: Progressive plasticity during colorectal cancer metastasis

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Nature 2025年第8047期637卷 E28页

作者： Andrew Moorman Elizabeth K Benitez Francesco Cambuli Qingwen Jiang Ahmed Mahmoud Melissa Lumish Saskia Hartner Sasha Balkaran Jonathan Bermeo Simran Asawa Canan Firat Asha Saxena Fan Wu Anisha Luthra Cassandra Burdziak Yubin Xie Valeria Sgambati Kathleen Luckett Yanyun Li Zhifan Yi Ignas Masilionis Kevin Soares Emmanouil Pappou Rona Yaeger T Peter Kingham William Jarnagin Philip B Paty Martin R Weiser Linas Mazutis Michael D'Angelica Jinru Shia Julio Garcia-Aguilar Tal Nawy Travis J Hollmann Ronan Chaligné Francisco Sanchez-Vega Roshan Sharma Dana Pe'er Karuna Ganesh Computational and Systems Biology Program Memorial Sloan Kettering Cancer Center New York NY USA. Molecular Pharmacology Program Memorial Sloan Kettering Cancer Center New York NY USA. Weill Cornell/Rockefeller/Sloan Kettering Tri-Institutional MD-PhD Program New York NY USA. New York Genome Center New York NY USA. Pharmacology Program Weill Cornell Graduate School New York NY USA. Department of Medicine Memorial Sloan Kettering Cancer Center New York NY USA. Case Western Reserve University Cleveland OH USA. Department of Pathology and Laboratory Medicine Memorial Sloan Kettering Cancer Center New York NY USA. Department of Surgery Memorial Sloan Kettering Cancer Center New York NY USA. Tri-Institutional PhD Program in Computational Biology and Medicine New York NY USA. Bristol Myers Squibb Princeton NJ USA. Computational and Systems Biology Program Memorial Sloan Kettering Cancer Center New York NY USA. peerd@***. Howard Hughes Medical Institute Chevy Chase MD USA. peerd@***. Molecular Pharmacology Program Memorial Sloan Kettering Cancer Center New York NY USA. ganeshk@***. Department of Medicine Memorial Sloan Kettering Cancer Center New York NY USA. ganeshk@***.

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The anatomic determinants of conductive hearing loss secondary to tympanic membrane perforation

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Journal of Otology 2017年第3期12卷 125-131页

作者： David J.Carpenter Debara L.Tucci David M.Kaylie Dennis O.Frank-Ito Division of Head and Neck Surgery & Communication Sciences Department of Surgery Duke University Medical Center Durham NC USA Computational Biology & Bioinformatics PhD Program Duke University Durham NC USA Department of Mechanical Engineering and Materials Science Duke University Durham NC USA

Objectives: Recent studies have introduced middle ear volume(MEV) as a novel determinant of perforation-induced conductive hearing loss(CHL) in a mechanism driven by trans-tympanic membrane pressure differences. The primary aims of this preliminary report are to: 1) correlate CHL with perforation size; 2) describe the relationship between CHL and MEV; and 3) compare CHL across a range of cholesteatoma ***: A retrospective pilot study was performed in 31 subjects with audiometry indicative of conductive hearing loss, temporal bone CT scans,and no prior middle ear surgery. Perforation size and MEV were analyzed with respect to CHL in a cohort of 10 perforated ears with no cholesteatoma. CHLs were compared in 3 groups defined by extent of cholesteatoma ***: Ears with large and small perforations showed mean ABG values of 32.0 ± 15.7 dB and 16.0 ± 16.4 dB, respectively. A direct relationship was observed between MEV and CHL for ears with large perforations across all frequencies, whereas this relationship for small perforations was frequency-dependent. Finally, a statistically significant increase in CHL was found across ears with increasing cholesteatoma involvement at 1000 Hz(X^2(2) = 9.786, p = 0.008),2000 Hz(x^2(2) = 8.455, p = 0.015),and 4000 Hz(x^2(2)= 8.253, p = 0.016).Conclusions: These pilot data suggest that greater perforation-induced conductive hearing losses may be associated with larger perforation sizes and cholesteatoma. The correlation between MEV and CHL may require additional study.

关键词： Perforation-induced hearing loss Conductive hearing loss Middle ear volume Three-dimensional volume reconstruction

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Spectral clustering strategies for heterogeneous disease expression data 18

Spectral clustering strategies for heterogeneous disease exp...

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18th Pacific Symposium on Biocomputing, PSB 2013

作者： Huang, Grace T. Cunningham, Kathryn I. Benos, Panayiotis V. Chennubhotla, Chakra S. Department of Computational and Systems Biology University of Pittsburgh PittsburghPA United States Joint CMU-Pitt PhD Program in Computational Biology University of Pittsburgh PittsburghPA United States Clinical and Translational Science Institute University of Pittsburgh PittsburghPA United States Department of Computer Science University of Arizona TucsonAZ United States

Clustering of gene expression data simplifies subsequent data analyses and forms the basis of numerous approaches for biomarker identification, prediction of clinical outcome, and personalized therapeutic strategies. The most popular clustering methods such as K-means and hierarchical clustering are intuitive and easy to use, but they require arbitrary choices on their various parameters (number of clusters for K-means, and a threshold to cut the tree for hierarchical clustering). Human disease gene expression data are in general more difficult to cluster efficiently due to background (genotype) heterogeneity, disease stage and progression differences and disease subtyping;all of which cause gene expression datasets to be more heterogeneous. Spectral clustering has been recently introduced in many fields as a promising alternative to standard clustering methods. The idea is that pairwise comparisons can help reveal global features through the eigen techniques. In this paper, we developed a new recursive K-means spectral clustering method (ReKS) for disease gene expression data. We benchmarked ReKS on three large-scale cancer datasets and we compared it to different clustering methods with respect to execution time, background models and external biological knowledge. We found ReKS to be superior to the hierarchical methods and equally good to K-means, but much faster than them and without the requirement for a priori knowledge of K. Overall, ReKS offers an attractive alternative for efficient clustering of human disease data.

关键词： Cluster analysis

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Disagreement in middle ear volume estimation between tympanometry and three-dimensional volume reconstruction in the context of tympanic membrane perforation

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Journal of Otology 2017年第2期12卷 74-79页

作者： David J.Carpenter Debara L.Tucci David M.Kaylie Dennis O.Frank-Ito Division of Head and Neck Surgery&Communication Sciences Department of SurgeryDuke University Medical CenterDurhamNCUSA Computational Biology&Bioinformatics PhD Program Duke UniversityDurhamNCUSA Department of Mechanical Engineering and Materials Science Duke UniversityDurhamNCUSA

Introduction: Middle ear volume(MEV) is a clinically relevant parameter across middle ear diseases. MEV values between these techniques have never before been tested for agreement in ears with perforated tympanic membranes(TMs).Methods: Middle ears were identified from 36 patients ranging 18-89 years of age with TM perforations who underwent tympanometry and temporal bone computed tomography(CT) between 2005 and 2015. MEVs calculated by both tympanometry and three-dimensional volume reconstruction(3DVR) were analyzed for agreement using Bland Altman plots. The differences between tympanometric and 3DVR MEV values for each given middle ear were characterized across MEV quartiles(1= smallest; 4= largest) and across increasing states of middle ear disease using Kruskale Wallis and Wilcoxon testing with Bonferroni ***: Bland Altman plots demonstrated significant disagreement between MEV measurement techniques. Differences between tympanometric(T) and 3DVR MEV values were significantly greater with increasing average(i.e.(Tt3DVR)/2)) MEV per linear regression(p < 0.0001). Significance was demonstrated between fourth and first average MEV quartiles(p= 0.0024), fourth and second quartiles(p= 0.0024), third and first quartiles(p= 0.0048), and third and second quartiles(p= 0.048). Absolute MEV difference was not significantly different across varying states of middle ear disease(p= 0.44).Conclusion: Statistically and clinically significant disagreement was demonstrated between tympanometric and 3DVR MEV values. Studies that vary in MEV estimation techniques may be expected to demonstrate significantly different results. These preliminary results suggest that clinicians should endeavor to seek further confirmation when interpreting high tympanometric MEV values.

关键词： Middle ear volume Tympanometry Three-dimensional volume reconstruction Tympanic membrane perforation

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Conservation of ancient full-length open reading frames in vertebrate endogenous retroviruses

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Retrovirology 2011年第2期8卷 1-2页

作者： Hugo Martins Palle Villesen Bioinformatics Research Centre Aarhus University Denmark PhD Program in Computational Biology Instituto Gulbenkian de Ciências Oeiras Portugal

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