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Interpretable prediction of drug synergy for breast cancer by random forest with features from Boolean modeling of signaling pathways

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Scientific Reports 2025年第1期15卷 1-18页

作者： Taoma, Kittisak Ruengjitchatchawalya, Marasri Kusonmano, Kanthida Termsaithong, Teerasit Sutthibutpong, Thana Liangruksa, Monrudee Laomettachit, Teeraphan Bioinformatics and Systems Biology Program School of Bioresources and Technology King Mongkut’s University of Technology Thonburi Bangkok 10150 Thailand School of Information Technology King Mongkut’s University of Technology Thonburi Bangkok 10140 Thailand Biotechnology Program School of Bioresources and Technology King Mongkut’s University of Technology Thonburi Bangkok 10150 Thailand Systems Biology and Bioinformatics Research Group Pilot Plant Development and Training Institute King Mongkut’s University of Technology Thonburi Bangkok 10150 Thailand Learning Institute King Mongkut’s University of Technology Thonburi Bangkok 10140 Thailand Theoretical and Computational Physics Group Center of Excellence in Theoretical and Computational Science King Mongkut’s University of Technology Thonburi Bangkok 10140 Thailand Department of Physics Faculty of Science King Mongkut’s University of Technology Thonburi Bangkok 10140 Thailand National Nanotechnology Center (NANOTEC) National Science and Technology Development Agency (NSTDA) Pathum Thani 12120 Thailand

Breast cancer is a complex and challenging disease to treat, and despite progress in combating it, drug resistance remains a significant hindrance. Drug combinations have shown promising results in improving therapeutic outcomes, and many machine learning models have been proposed to identify potential drug combinations. Recently, there has been a growing emphasis on enhancing the interpretability of machine learning models to improve our biological understanding of the drug mechanisms underlying the predictions. In this study, we developed a random forest model using simulated protein activities derived from Boolean modeling of breast cancer signaling pathways as input features. The model demonstrates a moderate Pearson’s correlation coefficient of 0.40 between the predicted and experimentally observed synergistic scores, with the area under the curve (AUC) of 0.67. Despite its moderate performance, the model offers insights into the interpretable mechanisms behind its predictions. The model’s input features consist solely of the individual protein activities simulated in response to drug treatments. Therefore, the framework allows for the analysis of each protein’s contribution to the synergy level of each drug pair, enabling a direct interpretation of the drugs’ actions on the signaling networks of breast cancer. We demonstrated the interpretability of our approach by identifying proteins responsible for drug resistance and sensitivity in specific cell lines. For example, the analysis revealed that the combination of MEK and STAT3 inhibitors exhibits only a moderate synergistic effect on MDA-MB-468 due to the negative contributions of mTORC1 and NF-κB that diminish the efficacy of the drug pair. The model further predicted that hyperactive PTEN would sensitize the cells to the drug pair. Our framework enhances the understanding of drug mechanisms at the level of the signaling pathways, potentially leading to more effective treatment designs. © The Author(s) 2025.

关键词： Breast cancer Drug combinations Drug resistance Machine learning Signaling pathways

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Resisting efficiency’s overreach

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Science 2023年第6663期381卷 1162页

作者： Dov Greenbaum Mark Gerstein The reviewer is at the Zvi Meitar Institute for Legal Implications of Emerging Technologies and Harry Radzyner Law School Reichman University Herzliya Israel. The reviewer is at the Department of Molecular Biophysics and Biochemistry Yale University New Haven CT USA. The reviewer is at the Program in Computational Biology and Bioinformatics and Department of Computer Science Yale University New Haven CT USA.

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White Paper on Enabling Global Image Data Sharing in the Life Sciences

arXiv

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arXiv 2024年

作者： Bajcsy, Peter Bhattiprolu, Sreenivas Börner, Katy Cimini, Beth A. Collinson, Lucy Ellenberg, Jan Fiolka, Reto Giger, Maryellen Goscinski, Wojtek Hartley, Matthew Hotaling, Nathan Horwitz, Rick Jug, Florian Kreshuk, Anna Lundberg, Emma Mathur, Aastha Narayan, Kedar Onami, Shuichi Plant, Anne L. Prior, Fred Swedlow, Jason Taylor, Adam Keppler, Antje National Institute of Standards and Technology GaithersburgMD20899 United States ZEISS Microscopy Customer Center DublinCA94568 United States Intelligent Systems Engineering Indiana University BloomingtonIN47408 United States Imaging Platform Broad Institute CambridgeMA United States Electron Microscopy Science Technology Platform Francis Crick Institute 1 Midland Road LondonNW1 1AT United Kingdom European Molecular Biology Laboratory Heidelberg Germany Lyda Hill Department of Bioinformatics UT Southwestern Medical Center DallasTX United States Department of Radiology and Committee on Medical Physics University of Chicago ChicagoIL United States National Imaging Facility Brisbane Australia European Molecular Biology Laboratory European Bioinformatics Institute Hinxton Cambridge United Kingdom National Center for Advancing Translational Science National Institutes of Health Rockville United States Allen Institute for Cell Science Seattle United States Fondazione Human Technopole Milan Italy Stanford University California US and SciLifeLab KTH Royal Institute of Technology Stockholm Sweden Center for Molecular Microscopy Center for Cancer Research National Cancer Institute National Institutes of Health BethesdaMD20892 United States Cancer Research Technology Program Frederick National Laboratory for Cancer Research FrederickMD21702 United States RIKEN Center for Biosystems Dynamics Research Kobe Japan Department of Biomedical Informatics Department of Radiology University of Arkansas for Medical Sciences Little RockAR72205 United States Divisions of Computational Biology and Molecular Cell and Developmental Biology University of Dundee United Kingdom Sage Bionetworks SeattleWA United States

Coordinated collaboration is essential to realize the added value of and infrastructure requirements for global image data sharing in the life sciences. In this White Paper, we take a first step at presenting some of the most common use cases as well as critical/emerging use cases of (including the use of artificial intelligence for) biological and medical image data, which would benefit tremendously from better frameworks for sharing (including technical, resourcing, legal, and ethical aspects). In the second half of this paper, we paint an ‘ideal world scenario’ for how global image data sharing could work and benefit all life sciences and beyond. As this is still a long way off, we conclude by suggesting several concrete measures directed toward our institutions, existing imaging communities and data initiatives, and national funders, as well as publishers. Our vision is that within the next ten years, most researchers in the world will be able to make their datasets openly available and use quality image data of interest to them for their research and benefit. This paper is published in parallel with a companion White Paper entitled "Harmonizing the Generation and Pre-publication Stewardship of FAIR Image Data", which addresses challenges and opportunities related to producing well-documented and high-quality image data that is ready to be shared. The driving goal is to address remaining challenges and democratize access to everyday practices and tools for a spectrum of biomedical researchers, regardless of their expertise, access to resources, and geographical location. © 2024, CC BY-NC-SA.

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A general kernel boosting framework integrating pathways for predictive modeling based on genomic data

arXiv

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arXiv 2020年

作者： Zeng, Li Yu, Zhaolong Zhang, Yiliang Zhao, Hongyu Department of Biostatistics Yale University New HavenCT06511 United States Interdepartmental Program in Computational Biology and Bioinformatics Yale University New HavenCT06511 United States Department of Genetics Yale School of Medicine New HavenCT06510 United States

Predictive modeling based on genomic data has gained popularity in biomedical research and clinical practice by allowing researchers and clinicians to identify biomarkers and tailor treatment decisions more efficiently. Analysis incorporating pathway information can boost discovery power and better connect new findings with biological mechanisms. In this article, we propose a general framework, Pathway-based Kernel Boosting (PKB), which incorporates clinical information and prior knowledge about pathways for prediction of binary, continuous and survival outcomes. We introduce appropriate loss functions and optimization procedures for different outcome types. Our prediction algorithm incorporates pathway knowledge by constructing kernel function spaces from the pathways and use them as base learners in the boosting procedure. Through extensive simulations and case studies in drug response and cancer survival datasets, we demonstrate that PKB can substantially outperform other competing methods, better identify biological pathways related to drug response and patient survival, and provide novel insights into cancer pathogenesis and treatment response. Copyright © 2020, The Authors. All rights reserved.

关键词： Forecasting

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Author Correction: Functional genomics data: privacy risk assessment and technological mitigation

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Nature reviews. Genetics 2022年第4期23卷 259页

作者： Gamze Gürsoy Tianxiao Li Susanna Liu Eric Ni Charlotte M Brannon Mark B Gerstein Computational Biology and Bioinformatics Program Yale University New Haven CT USA. Molecular Biophysics and Biochemistry Yale University New Haven CT USA. Molecular Cellular and Developmental Biology Yale University New Haven CT USA. Statistics and Data Science Yale University New Haven CT USA. Department of Biology Stanford University Stanford CA USA. Computational Biology and Bioinformatics Program Yale University New Haven CT USA. mark@***. Molecular Biophysics and Biochemistry Yale University New Haven CT USA. mark@***. Statistics and Data Science Yale University New Haven CT USA. mark@***. Computer Science Yale University New Haven CT USA. mark@***.

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Algal biomass based bio-refineries: Concurrent pre-treatment strategies and perspectives for sustainable feedstock

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Biomass and Bioenergy 2024年 187卷

作者： Shashikumar, Uday Ranjan, Kumar Rakesh Sharma, Ankit Naina Subramanian, Balaji Tsai, Pei-Chien Lin, Yuan-Chung Lay, Chyi-How Wang, Chin-Tsan Biswas, Shelly Ponnusamy, Vinoth Kumar Department of Medicinal and Applied Chemistry Kaohsiung Medical University Kaohsiung City 807 Taiwan Department of Chemistry Amity Institute of Applied Sciences Amity University Uttar Pradesh Noida201303 India Department of Mechanical Engineering SRM Institute of Science and Technology Kattankulathur Tamil Nadu Chennai603203 India Institute of Environmental Engineering National Sun Yat-Sen University Taiwan Master Program of Green Energy Science and Technology Feng Chia University Taiwan Department of Mechanical and Electro-Mechanical Engineering National I-Lan University I Lan Taiwan Department of Space Engineering and Rocketry Birla Institute of Technology Mesra Jharkhand Ranchi835215 India 807 Kaohsiung City Taiwan 807 Kaohsiung City Taiwan Department of Computational Biology Institute of Bioinformatics Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Tamil Nadu Chennai602105 India Institute of Aquatic Science and Technology National Kaohsiung University of Science and Technology Kaohsiung City81157 Taiwan Department of Food Science National Pingtung University of Science and Technology Pingtung91201 Taiwan

The quest for viable and scalable biofuel sources has been at the forefront of scientific innovation for the past three decades. Due to its rich chemical constituents, microalgal biomass has emerged as a pivotal sustainable and scalable feedstock for biorefineries. This comprehensive review critically analyzes the different types of microalgae feedstock, concurrent extraction technologies, bio-pre-treatment procedures, and the key chemical and physical parameters influencing lipid formation and algal biofuel production. We propose a novel approach of photo-initiated culturing of algal biomass using photobioreactors (PBRs) to address the limitations of concurrent space and time-related constraints. The innovative photo bio-refinery strategy presented herein aims to enhance sustainability factors while minimizing emissions, catering to the needs of futuristic non-electric vehicles. A comparative quality analysis of microalgae-derived biofuel against conventional fossil fuels and other biofuels is conducted, considering chemical, environmental, economic, and social perspectives. Furthermore, we elucidate the efficacy of bio-pre-treatment strategies such as dehydration, hydrothermal liquefaction, pyrolysis, and gasification in optimizing biofuel production. The proposed photo biorefineries exhibit the potential to yield a diverse range of value-added products, including biodiesel, biogases, bio-fertilizers, bio-pesticides, bio-alcohols, dyes, proteins, carotenoids, and drug vitals. This review provides a comprehensive framework for the development of sustainable and efficient microalgae-based biorefineries, paving the way for a greener and more economically viable future in the biofuel industry. © 2024 Elsevier Ltd

关键词： Feedstocks

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Worm Perturb-Seq: massively parallel whole-animal RNAi and RNA-seq

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Nature Communications 2025年第1期16卷 1-21页

作者： Zhang, Hefei Li, Xuhang Song, Dongyuan Yukselen, Onur Nanda, Shivani Kucukural, Alper Li, Jingyi Jessica Garber, Manuel Walhout, Albertha J. M. Department of Systems Biology University of Massachusetts Chan Medical School Worcester MA United States Bioinformatics Interdepartmental Ph.D. Program University of California Los Angeles CA United States Department of Genetics and Genome Sciences University of Connecticut Health Center Farmington CT United States Via Scientific Inc. Cambridge MA United States Pathology Beth Israel Deaconess Medical Center Harvard Medical School Boston MA United States Department of Genomics and Computational Biology University of Massachusetts Chan Medical School Worcester MA United States Department of Statistics and Data Science Department of Biostatistics Department of Computational Medicine and Department of Human Genetics University of California Los Angeles CA United States

Transcriptomes provide highly informative molecular phenotypes that, combined with gene perturbation, can connect genotype to phenotype. An ultimate goal is to perturb every gene and measure transcriptome changes, however, this is challenging, especially in whole animals. Here, we present ‘Worm Perturb-Seq (WPS)’, a method that provides high-resolution RNA-sequencing profiles for hundreds of replicate perturbations at a time in living animals. WPS introduces multiple experimental advances combining strengths of Caenhorhabditis elegans genetics and multiplexed RNA-sequencing with a novel analytical framework, EmpirDE. EmpirDE leverages the unique power of large transcriptomic datasets and improves statistical rigor by using gene-specific empirical null distributions to identify DEGs. We apply WPS to 103 nuclear hormone receptors (NHRs) and find a striking ‘pairwise modularity’ in which pairs of NHRs regulate shared target genes. We envision the advances of WPS to be useful not only for C. elegans, but broadly for other models, including human cells. © The Author(s) 2025.

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Minimization-Aware Recursive K∗ (MARK∗ ): A Novel, Provable Algorithm that Accelerates Ensemble-Based Protein Design and Provably Approximates the Energy Landscape 1

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23rd International Conference on Research in computational Molecular biology, RECOMB 2019

作者： Jou, Jonathan D. Holt, Graham T. Lowegard, Anna U. Donald, Bruce R. Department of Computer Science Duke University DurhamNC United States Computational Biology and Bioinformatics Program Duke University DurhamNC United States Department of Biochemistry Duke University Medical Center DurhamNC United States Department of Chemistry Duke University DurhamNC United States

ISBN: (数字)9783030170837

ISBN: (纸本)9783030170820

Protein design algorithms that model continuous sidechain flexibility and conformational ensembles better approximate the in vitro and in vivo behavior of proteins. The previous state of the art, iMinDEE- A∗ - K∗, computes provable Ε -approximations to partition functions of protein states (e.g., bound vs. unbound) by computing provable, admissible pairwise-minimized energy lower bounds on protein conformations and using the A∗ enumeration algorithm to return a gap-free list of lowest-energy conformations. iMinDEE-A ∗ - K∗ runs in time sublinear in the number of conformations, but can be trapped in loosely-bounded, low-energy conformational wells containing many conformations with highly similar energies. That is, iMinDEE- A∗ - K∗ is unable to exploit the correlation between protein conformation and energy: similar conformations often have similar energy. We introduce two new concepts that exploit this correlation: Minimization-Aware Enumeration and Recursive K∗. We combine these two insights into a novel algorithm, Minimization-Aware Recursive K∗ (MARK∗ ), that tightens bounds not on single conformations, but instead on distinct regions of the conformation space. We compare the performance of iMinDEE- A∗ - K∗ vs. MARK∗ by running the BBK∗ algorithm, which provably returns sequences in order of decreasing K∗ score, using either iMinDEE- A∗ - K∗ or MARK∗ to approximate partition functions. We show on 200 design problems that MARK∗ not only enumerates and minimizes vastly fewer conformations than the previous state of the art, but also runs upÂ to two orders of magnitude faster. Finally, we show that MARK∗ not only efficiently approximates the partition function, but also provably approximates the energy landscape. To our knowledge, MARK∗ is the first algorithm to do so. We use MARK∗ to analyze the change in energy landscape of the bound and unbound states of the HIV-1 capsid protein C-terminal domain in complex with camelid V H H, and measure the change in conformati

关键词： Proteins

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Investigating the Efficacy of Data Mining Techniques for Crowdsourced Biological Datasets

Investigating the Efficacy of Data Mining Techniques for Cro...

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2019 IEEE MIT Undergraduate Research Technology Conference, URTC 2019

作者： Regan, Ilona Alkurdi, Dany Paisarnsrisomsuk, Sarun Ruiz, Carolina Ryder, Elizabeth F. Gegear, Rob Bioinformatics and Computational Biology Program Worcester Polytechnic Institute WorcesterMA United States Princeton University Department of Near Eastern Studies PrincetonNJ United States Worcester Polytechnic Institute Department of Computer Science WorcesterMA United States Worcester Polytechnic Institute Department of Biology and Biotechnology WorcesterMA United States University of Massachusetts Dartmouth Department of Biology DartmouthMA United States

ISBN: (纸本)9781728158181

The rapid worldwide decline of wild pollinators over recent years poses a significant environmental threat due to the critical keystone role that pollinators play in terrestrial ecosystems. In order to gain insight into the major anthropogenic factors causing these declines, researchers have collected large amounts of ecological data. Yet, they often lack the computational tools needed to analyze the information contained in such datasets. We investigate various data analysis techniques for the Beecology Project, which is a citizen science based effort to rapidly collect data on foraging habits of bumblebee species native to Massachusetts. Different data mining approaches were explored, including association analysis, trend analysis, classification, regression, and clustering. It was found that different techniques were more suitable depending on the biological research question. Future work will focus on making tools utilizing these approaches available online through the Beecology website, where they can be used by the public to determine how best to protect our native pollinators and the diverse ecosystems they support. © 2019 IEEE.

关键词： Data mining

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Biogas production and its utilization in internal combustion engines - A review

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Process Safety and Environmental Protection 2024年 186卷 518-539页

作者： Vasan, Vinod Sridharan, Naveen Venkatesh Feroskhan, M. Vaithiyanathan, Sugumaran Subramanian, Balaji Tsai, Pei-Chien Lin, Yuan-Chung Lay, Chyi-How Wang, Chin-Tsan Ponnusamy, Vinoth Kumar Vellore Institute of Technology Chennai600127 India Department of Mechanical Engineering Faculty of Engineering and Technology SRM Institute of Science and Technology Kattankulathur Chennai603203 India Kaohsiung City807 Taiwan Department of Computational Biology Institute of Bioinformatics Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Tamil Nadu Chennai602105 India Institute of Environmental Engineering National Sun Yat-sen University Kaohsiung804 Taiwan Center for Emerging Contaminants Research National Sun Yat-sen University Kaohsiung804 Taiwan Master's Program of Green Energy Science and Technology Feng Chia University Taichung City407102 Taiwan Department of Mechanical and Electro-Mechanical Engineering National I-Lan University I Lan Taiwan Research Center for Precision Environmental Medicine Kaohsiung Medical University Kaohsiung City807 Taiwan Kaohsiung City807 Taiwan Division of Operation and Maintenance Engineering Luleå University of Technology Luleå Sweden Department of Chemistry National Sun Yat-sen University Kaohsiung804 Taiwan

This review presents the significance and current challenges associated with fossil fuels, highlighting the need for cleaner and more sustainable energy sources. The significant object of the current study is to concentrate on increased interest in utilizing biogas as a viable substitute for traditional fuels, delving into its characteristics, benefits, accessibility, feasibility, and prospects as an environmentally sustainable energy option. The current study extensively analyses the prospect and significance of biogas in mitigating emissions regulations in Internal Combustion Engines (ICEs). This paper provides a comprehensive overview of recent research on incorporating biogas in internal combustion engines, focusing on emission reduction and the corresponding impact on engine performance. The subject matter encompasses a range of facets about biogas, encompassing its origins, chemical composition, purification, enhancement techniques, and requisite engine adjustments to enable the effective utilization of biogas as an alternative fuel. The study also evaluates the performance of biogas-fuelled ICEs based on the results of cross-referencing with contemporary research and investigates peripheral aspects such as additive inclusion to increase biogas capabilities as fuel and potential applications in advanced engine modes of operation. The review identifies research gaps in the domains of biogas processing and utilization in ICEs and acknowledges existing challenges specific to the adoption of biogas as a conventional fuel alternative. Future research directions are also prescribed, further highlighting the importance of transitioning from fossil fuels to meet environmental goals and global energy demands. © 2024 The Institution of Chemical Engineers

关键词： Biogas

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