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Comprehensive mutational scanning of EGFR reveals TKI sensitivities of extracellular domain mutants (Vol 15, 2742, 2024)

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NATURE COMMUNICATIONS 2024年第1期15卷 1-18页

作者： Hayes, Tikvah K. Aquilanti, Elisa Persky, Nicole S. Yang, Xiaoping Kim, Erica E. Brenan, Lisa Goodale, Amy B. Alan, Douglas Sharpe, Ted Shue, Robert E. Westlake, Lindsay Golomb, Lior Silverman, Brianna R. Morris, Myshal D. Fisher, Ty Running Beyene, Eden Li, Yvonne Y. Cherniack, Andrew D. Piccioni, Federica Hicks, J. Kevin Chi, Andrew S. Cahill, Daniel P. Dietrich, Jorg Batchelor, Tracy T. Root, David E. Johannessen, Cory M. Meyerson, Matthew Department of Medical Oncology Dana-Farber Cancer Institute & Harvard Medical School Boston MA USA Cancer Program The Broad Institute of M.I.T. and Harvard Cambridge MA USA Genetic Perturbation Platform The Broad Institute of M.I.T. and Harvard Cambridge MA USA Data Science Platform The Broad Institute of M.I.T. and Harvard Cambridge Cambridge MA USA Summer Honors Undergraduate Research Program Harvard Medical School Boston MA USA Department of Pathology H. Lee Moffitt Cancer Center and Research Institute Tampa FL USA Center for Neuro-Oncology Division of Neuro-Oncology Massachusetts General Hospital Boston MA USA Department of Neurology Division of Neuro-Oncology Massachusetts General Hospital Boston MA USA Department of Neurology Brigham and Women’s Hospital & Harvard Medical School Boston MA USA

The epidermal growth factor receptor, EGFR, is frequently activated in lung cancer and glioblastoma by genomic alterations including missense mutations. The different mutation spectra in these diseases are reflected in divergent responses to EGFR inhibition: significant patient benefit in lung cancer, but limited in glioblastoma. Here, we report a comprehensive mutational analysis of EGFR function. We perform saturation mutagenesis of EGFR and assess function of ~22,500 variants in a human EGFR-dependent lung cancer cell line. This approach reveals enrichment of erlotinib-insensitive variants of known and unknown significance in the dimerization, transmembrane, and kinase domains. Multiple EGFR extracellular domain variants, not associated with approved targeted therapies, are sensitive to afatinib and dacomitinib in vitro. Two glioblastoma patients with somatic EGFR G598V dimerization domain mutations show responses to dacomitinib treatment followed by within-pathway resistance mutation in one case. In summary, this comprehensive screen expands the landscape of functional EGFR variants and suggests broader clinical investigation of EGFR inhibition for cancers harboring extracellular domain mutations.

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Finding shortest and nearly shortest path nodes in large substantially incomplete networks

arXiv

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arXiv 2022年

作者： Kitsak, Maksim Ganin, Alexander Elmokashfi, Ahmed Cui, Hongzhu Eisenberg, Daniel A. Alderson, David L. Korkin, Dmitry Linkov, Igor Faculty of Electrical Engineering Mathematics and Computer Science Delft University of Technology Delft Netherlands Network Science Institute Northeastern University BostonMA02115 United States Department of Systems and Information Engineering University of Virginia CharlottesvilleVA22904 United States U.S. Army Engineer Research and Development Center Contractor Concord MA01742 United States Simula Metropolitan Center for Digital Engineering Oslo Norway Bioinformatics and Computational Biology Program Worcester Polytechnic Institute WorcesterMA01609 United States Institute for Genomic Medicine Columbia University Medical Center New YorkNY United States Operations Research Department Naval Postgraduate School MontereyCA93943 United States Data Science Program Worcester Polytechnic Institute WorcesterMA01609 United States Computer Science Department Worcester Polytechnic Institute WorcesterMA01609 United States U.S. Army Engineer Research and Development Center Environmental Laboratory Concord MA01742 United States

Dynamic processes on networks, be it information transfer in the Internet, contagious spreading in a social network, or neural signaling, take place along shortest or nearly shortest paths. Unfortunately, our maps of most large networks are substantially incomplete due to either the highly dynamic nature of networks, or high cost of network measurements, or both, rendering traditional path finding methods inefficient. We find that shortest paths in large real networks, such as the network of protein-protein interactions (PPI) and the Internet at the autonomous system (AS) level, are not random but are organized according to latent-geometric rules. If nodes of these networks are mapped to points in latent hyperbolic spaces, shortest paths in them align along geodesic curves connecting endpoint nodes. We find that this alignment is sufficiently strong to allow for the identification of shortest path nodes even in the case of substantially incomplete networks. We demonstrate the utility of latent-geometric path finding in problems of cellular pathway reconstruction and communication security. Copyright © 2022, The Authors. All rights reserved.

关键词： Proteins

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Comparing Naïve Bayes, Decision Tree and Logistic Regression Methods in Fraudulent Credit Card Transactions

Comparing Naïve Bayes, Decision Tree and Logistic Regressio...

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data Analytics for Business and Industry: Way Towards a Sustainable Economy (ICDABI), International Conference on

作者： Maha A. Alanezi Mawra T. Homeed Zahra S. Mohamed Ahmed M. Zeki Big Data Science & Analytics Master Program University of Bahrain Sakhir Kingdom of Bahrain Collage of Information Technology University of Bahrain Sakhir Kingdom of Bahrain

ISBN: (数字)9781728196756

ISBN: (纸本)9781728196763

data mining is utilized to explore banks' data to unravel any hidden scams and detect potential frauds. The aim of this paper is to compare between the Naïve Bayes, Decision Tree and Logistic Regression in fraudulent credit card transactions. Cross-Industry Standard Process for data Mining (CRISP-DM) is followed to achieve the aim of this research. In terms of accuracy, the best classification model was Logistic Regression with 94.6% accuracy, compared with the Decision Tree and Naïve Bayes that showed accuracy of 89.1% and 90.9% respectively. Other measures were also calculated like time needed to build the model among others.

关键词： Support vector machines Business Bayes methods Hidden Markov models Logistics data models Credit cards

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Front Cover: Generation of Molecular Counterfactuals for Explainable Machine Learning Based on Core-Substituent Recombination (ChemMedChem 3/2024)

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ChemMedChem 2024年第3期19卷

作者： Alec Lamens Prof. Jürgen Bajorath Department of Life Science Informatics and Data Science B-IT LIMES Program Unit Chemical Biology and Medicinal Chemistry Rheinische Friedrich-Wilhelms-Universität Friedrich-Hirzebruch-Allee 5/6 53115 Bonn Germany Lamarr Institute for Machine Learning and Artificial Intelligence Rheinische Friedrich-Wilhelms-Universität Bonn Friedrich-Hirzebruch-Allee 5/6 53115 Bonn Germany

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Fast and scalable image search for histology

arXiv

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arXiv 2021年

作者： Chen, Chengkuan Lu, Ming Y. Williamson, Drew F.K. Chen, Tiffany Y. Schaumberg, Andrew J. Mahmood, Faisal Department of Pathology Brigham and Women's Hospital Harvard Medical School BostonMA United States Cancer Program Broad Institute of Harvard and Mit CambridgeMA United States Cancer Data Science Dana-Farber Cancer Institute BostonMA United States

The expanding adoption of digital pathology has enabled the curation of large repositories of histology whole slide images (WSIs), which contain a wealth of information. Similar pathology image search offers the opportunity to comb through large historical repositories of gigapixel WSIs to identify cases with similar morphological features and can be particularly useful for diagnosing rare diseases, identifying similar cases for predicting prognosis, treatment outcomes and potential clinical trial success. A critical challenge in developing a WSI search and retrieval system is scalability, which is uniquely challenging given the need to search a growing number of slides that each can consist of billions of pixels and are several gigabytes in size. Such systems are typically slow and retrieval speed often scales with the size of the repository they search through, making their clinical adoption tedious and are not feasible for repositories that are constantly growing. Here we present Fast Image Search for Histopathology (FISH), a histology image search pipeline that is infinitely scalable and achieves constant search speed that is independent of the image database size, while being interpretable and without requiring detailed annotations. FISH uses self-supervised deep learning to encode meaningful representations from WSIs and a Van Emde Boas tree for fast search, followed by an uncertainty-based ranking algorithm to retrieve similar WSIs. We evaluated FISH on multiple tasks and datasets with over 22,000 patient cases spanning 56 disease subtypes. We additionally demonstrate that FISH can be used to assist with the diagnosis of rare cancer types where sufficient cases may not be available to train traditional supervised deep models. FISH is available as an easy-to-use, open source software package (https://***/mahmoodlab/FISH). © 2021, CC BY-NC-ND.

关键词： Diseases

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Interpretation of NLP models through input marginalization

arXiv

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arXiv 2020年

作者： Kim, Siwon Yi, Jihun Kim, Eunji Yoon, Sungroh Data Science and Artificial Intelligence Laboratory ECE Interdisciplinary Program in AI Institute of Engineering Research Seoul National University Seoul08826 Korea Republic of

To demystify the "black box" property of deep neural networks for natural language processing (NLP), several methods have been proposed to interpret their predictions by measuring the change in prediction probability after erasing each token of an input. Since existing methods replace each token with a predefined value (i.e., zero), the resulting sentence lies out of the training data distribution, yielding misleading interpretations. In this study, we raise the out-of-distribution problem induced by the existing interpretation methods and present a remedy;we propose to marginalize each token out. We interpret various NLP models trained for sentiment analysis and natural language inference using the proposed method. Copyright © 2020, The Authors. All rights reserved.

关键词： Sentiment analysis

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Electronic Near Point of Convergence: A Tool to Aid the Assessment of Concussion

Electronic Near Point of Convergence: A Tool to Aid the Asse...

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IEEE International Conference on Bioinformatics and Biomedicine (BIBM)

作者： Bo Liang Leslie S. Prichep Saloni Kanakia Jeffrey J. Bazarian Tracey Covassin Robert J. Elbin Gillian A. Hotz Jeb F. Struder Rubén Armañanzas R&D Department BrainScope Company Inc. Chevy Chase MD USA Department of Emergency Medicine University of Rochester School of Medicine Rochester NY USA Department of Kinesiology Michigan State University East Lansing MI USA Office Sports Concussion Research University of Arkansas Fayetteville AR USA UHealth Concussion Program University of Miami Miami FL USA Korey Stringer Institute University of Connecticut CT USA Institute of Data Science and Artificial Intelligence TECNUN School of Engineering University of Navarra Pamplona Spain

Near Point of Convergence (NPC) is a widely used test when assessing oculomotor dysfunction in patients with mild Traumatic Brain Injury (mTBI) or concussion. A new electronic version of the Near Point Convergence test (eNPC) was developed to automatically measure NPC distance, addressing the need to improve the administration and precision of NPC testing. Age-adjusted normative values were achieved by using goodness-of-fit and generalized linear regression models. Target subjects included males and females aged 13 to 50 years. Normative values were computed as a function of age for records from 230 healthy volunteers. eNPC values from an independent cohort of 237 patients clinically deemed to have concussion and 245 matched controls were transformed into z-scores relative to age-expected normal eNPC values and then compared to demonstrate performance in a clinical population. Mean differences between matched controls and concussed patients showed a highly significant difference for eNPC z-scores between groups. The maximum Odds-Ratio (OR) value for z-scores was reached in the cutoff interval 0.75 to 1 with a mean of 2.614 and a median of 2.627. Usability for the eNPC test was high with a rate of obtaining measurements between 92% and 98%. Using z-scores helped reduce variance of the measurement and accounted for the effect of age. Significant separation between concussed patients and matched controls validated the use of the eNPC mathematical model. Further, high OR values using z-scores demonstrated potential improved clinical utility of the eNPC in the prediction of oculomotor dysfunction.

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Mapping Emergency Medicine data to the Observational Medical Outcomes Partnership Common data Model: A Gap Analysis of the American College of Emergency Physicians Clinical Emergency data Registry

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JACEP Open 2025年第1期6卷 100016-100016页

作者： Cohen, Inessa Diao, Zihan Goyal, Pawan Gupta, Aarti Hawk, Kathryn Malcom, Bill Malicki, Caitlin Sharma, Dhruv Sweeney, Brian Weiner, Scott G. Venkatesh, Arjun Taylor, R. Andrew Department of Emergency Medicine Yale School of Medicine New Haven CT United States Section for Biomedical Informatics and Data Science Yale University School of Medicine New Haven CT United States Program of Computational Biology and Bioinformatics Yale University New Haven CT United States American College of Emergency Physicians Washington DC United States Department of Emergency Medicine Brigham and Women's Hospital Boston MA United States Center for Outcomes Research and Evaluation (CORE) Section of Cardiovascular Medicine Yale School of Medicine New Haven CT United States

Objectives: This study aims to conduct a gap analysis to determine the feasibility of mapping electronic health record data from the Clinical Emergency data Registry (CEDR) to the Observational Medical Outcomes Partnership Common data Model (OMOP-CDM). Methods: We employed a structured approach using a custom-built comparison matrix. This matrix facilitated the alignment of CEDR data fields with the corresponding elements in the OMOP-CDM schema. Each field was evaluated for compatibility, with categorization into 3 distinct types: direct matches, fields requiring transformation, and fields with no OMOP-CDM equivalent. The mapping process was informed by consultations with the Observational Health data sciences and Informatics community forums and was guided by existing documentation and best practices in data harmonization. We performed descriptive analyses, quantifying the extent of direct matches and identifying the specific transformations needed for each CEDR-CDM field to ensure compliance with the OMOP-CDM model. Results: Our analysis indicates a high degree of compatibility between CEDR and OMOP, with over 90% (244/269) of CEDR fields being successfully mapped. Specifically, 173 fields had direct matches, whereas 71 required transformations. Challenges identified include addressing fields unique to CEDR with no OMOP-CDM equivalent and managing the transformations required for proper alignment. Conclusion: The OMOP-CDM presents a promising framework for standardizing emergency medicine data, thereby enhancing future query automation, analytics, and cross-institutional collaboration. Despite the potential challenges in capturing unique CEDR fields and addressing necessary transformations, most emergency department data can be standardized within the OMOP-CDM, fostering broader insights and applications in research and public health. © 2024 The Author(s)

关键词： databases electronic health records emergency service informatics opioid/adverse effects quality of health care registries

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Modeling the number of confirmed cases of COVID-19 in East Java using negative binomial regression based on least square spline estimator

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AIP Conference Proceedings 2024年第1期3083卷

作者： Arip Ramadan Nur Chamidah I. Nyoman Budiantara Student of Doctoral Study Program of Mathematics and Natural Sciences Faculty of Science and Technology Airlangga University Surabaya 60115 Indonesia. Department of Information System School of Industrial and System Engineering Telkom University Surabaya 60231 Indonesia. Department of Mathematics Faculty of Science and Technology Airlangga University Surabaya 60115 Indonesia. Research Group of Statistical Modeling in Life Science Faculty of Science and Technology Airlangga University Surabaya 60115 Indonesia. Department of Statistics Faculty of Sciences and Data Analytics Sepuluh Nopember Institute of Technology Surabaya 60111 Indonesia.

The East Java Province has experienced a significant surge in number of confirmed cases of COVID-19. This study endeavors to investigate the potential correlation between weather conditions and the incidence of number of confirmed cases of COVID-19 in East Java. To achieve this, a nonparametric regression model, specifically, the Negative Binomial Regression (NBR) model based on the least squares spline estimator, was utilized. The outcomes of the study indicate that the Mean Absolute Percentage Error (MAPE) of nonparametic regression model is 0.30. Meanwhile, the MAPE for the parametric regression model is 0.34. The results show that a nonparametric regression model approach is better than parametric regression model approach. The study establishes that the truncated spline estimator based NBR model represents the best fit, with an MLCV value of -256.71. The findings of the study suggest that a temperature less than 21.75°C is associated with a decrease of 13.28 number of confirmed cases of COVID-19 per each 1°C increase, while a temperature between 21.75°C and 25.78°C is linked to an increase of 6.85 number of confirmed cases of COVID-19 per each 1°C increase. In contrast, a temperature greater than 25.78°C is associated with a decrease of 139.42 number of confirmed cases of COVID-19 per each 1°C increase. Similarly, a wind speed less than 5.57 m/s is related to a decrease of 12.99 number of confirmed cases of COVID-19 per each 1 m/s increase, whereas a wind speed between 5.57 m/s and 8.99 m/s is associated with a decrease of 10.29 number of confirmed cases of COVID-19 per each 1 m/s increase. Furthermore, a wind speed greater than 8.99 m/s is linked to a decrease of 19.16 number of confirmed cases of COVID-19 per each 1 m/s increase. The study provides evidence that higher temperatures and wind speeds result in a slower rise in the incidence of the number of confirmed cases of COVID-19. Consequently, it is recommended that the local government remains vigilant du

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Prognostic and therapeutic value of LSM5 gene in human brain cancer Glioma: An omics database exploration approach

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Informatics in Medicine Unlocked 2022年 34卷

作者： Ullah, Md. Asad Alam, Sayka Farzana, Maisha Tayab Moin, Abu Binte Sayed Prapty, Chowdhury Nusaiba Zohora, Umme Salma Rahman, Mohammad Shahedur Department of Biotechnology and Genetic Engineering Faculty of Biological Sciences Jahangirnagar University Dhaka Bangladesh Biotechnology Program Department of Mathematics and Natural Sciences School of Data and Sciences BRAC University Dhaka Bangladesh Department of Genetic Engineering and Biotechnology Faculty of Biological Sciences University of Chittagong Chattogram Bangladesh Wazed Miah Science Research Center Jahangirnagar University Dhaka Bangladesh

This study explored the diagnostic and therapeutic values of the LSM5 gene in two commonest types of gliomas, i.e., lower-grade glioma (LGG) and glioblastoma multiforme (GBM) by exploiting a bioinformatics-based database mining pipeline. LSM5 gene was reported to be upregulated at both mRNA and protein levels in both LGG and GBM tissues compared to adjacent normal tissues. Moreover, overexpression of LSM5 was later observed to be linked with glioma patients' demographic status and advancing cancer stages. Additionally, a differential and distinct methylation pattern of the LSM5 promoter and coding sequence in glioma tissues was reported. The gene of our interest was also discovered to undergo multiple amplification events (frequency of alteration: 1.1%) in both LGG and GBM patients. Furthermore, LSM5 overexpression was reported to be correlated with the poor overall and relapse-free survival of glioma patients. Subsequently, the immune phenotype analysis revealed that the expression of LSM5 correlates with the abundance levels of various immune cells i.e., B cell, T cell, dendritic cell, and immunomodulators, i.e., IL10RB, CD48, CD274 in glioma microenvironment. Finally, functional enrichment analysis of the neighbor genes of LSM5 showed that most of the genes are involved in different pivotal cellular processes like RNA splicing, DNA mismatch repair, and DNA replication. Overall, this study suggests that LSM5 and its downstream products are potential candidates for glioma diagnosis and treatment. © 2022

关键词： Biomarker Diagnosis Glioma LSM5 Treatment

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