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2024 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2024

作者： Pierini, Elisa Mazza, Ludovica Petti, Manuela Tieri, Paolo Sapienza University of Rome Data Science MSc Program Rome Italy Sapienza University of Rome Diag Dept. Comp. Contr. Manag. Eng. Rome Italy Cnr National Research Council Iac Institute for Applied Computing Rome Italy

ISBN: (纸本)9798350386226

Drug repositioning is a promising strategy to discover new therapeutic applications for existing drugs, significantly reducing the time and costs associated with traditional drug development. This study employs a network medicine approach to analyze successful cases of drug repositioning, focusing on the exploratory hypothesis that the efficacy of repositioning may be determined by functional similarity between between diseases for which the drug was originally designed and diseases for which the same drug is reused. Network medicine tools were employed to investigate the connections between disease-associated genes, proteins, and approved drugs. Biological networks, including protein-protein interactions and functional interactions networks, as well as gene- and drug-disease association data are analyzed to identify functional similarities and possible molecular connections between diseases and treatments. Using clustering techniques and topological analysis, the results reveal a suggestive overlap of involved genes and functional interactions, emphasizing the value of computational methods in accelerating drug repositioning efforts and improving understanding of drug repositioning dynamics for more efficient therapeutic interventions. © 2024 IEEE.

关键词： Network theory (graphs)

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Multi-layer network modelling of genomic and transcriptomic data to investigate the response to checkpoint inhibitors in NSCLC

Multi-layer network modelling of genomic and transcriptomic ...

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IEEE International Conference on Bioinformatics and Biomedicine (BIBM)

作者： Davide Mascolo Lorenzo Farina Manuela Petti MSc Program in Data Science Sapienza University of Rome Rome Italy Dept. of Computer Control and Management Engineering Sapienza University of Rome Rome Italy

Recent innovations and developments in molecular biology and biotechnology have made it possible to acquire and store large omics datasets. In particular genomics, transcriptomics and the study of their relationship represent the key elements to understand how genotype influences phenotype. In this work we investigate the potential of multi-layer network modeling in integrating genomic and transcriptomic data of 152 advanced non-small cell lung cancer (NSCLC) patients treated with anti-PD-(L)1 therapy. For the transcriptomic layer, we performed differential expression and differential co-expression analyses in order to identify a subset of key genes in differentiating responder patients from non-responders. Adding to the genomic-transcriptomic model other three layers related to immune, myeloid and curated immunotherapy-based literature signatures we obtained a 5-layer Patient Similarity Network. The application of Similarity Network Fusion algorithm revealed a statistically significant stratification of patients (p-value ≤ 0.05) which allows the identification of two clusters characterized by patients responding and not responding to immunotherapy.

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Tracking X-Ray Variability in Next-generation EHT Low-luminosity Active Galactic Nucleus Targets

arXiv

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arXiv 2025年

作者： Ford, Nicole M. Nowak, Michael Ramakrishnan, Venkatessh Haggard, Daryl Dage, Kristen Nair, Dhanya G. Chan, Chi-Kwan Department of Physics McGill University 3600 Rue University MontrealQCH3A 2T8 Canada Trottier Space Institute 3550 Rue University MontréalQCH3A 2A7 Canada Department of Physics Washington University in Saint Louis MSC 1105-109-02 One Brookings Drive St. LouisMO63130-4899 United States Finnish Centre for Astronomy with ESO University of Turku TurkuFI-20014 Finland Aalto University Metsähovi Radio Observatory Metsähovintie 114 KylmäläFI-02540 Finland International Centre for Radio Astronomy Research Curtin University GPO Box U1987 PerthWA6845 Australia Astronomy Department Universidad de Concepción Casilla Concepción160-C Chile Max Planck Institute for Radio Astronomy Auf dem Hügel 69 Bonn53121 Germany Steward Observatory Department of Astronomy University of Arizona 933 N. Cherry Avenue TucsonAZ85721 United States Data Science Institute University of Arizona 1230 N. Cherry Avenue TucsonAZ85721 United States Program in Applied Mathematics University of Arizona 617 N. Santa Rita TucsonAZ85721 United States

We present a 5 month NICER X-ray monitoring campaign for two low-luminosity active galactic nuclei (LLAGNs)—NGC 4594 and IC 1459—with complementary Swift and NuSTAR observations. Utilizing an absorbed power-law and thermal source model combined with NICER’s SCORPEON background model, we demonstrate the effectiveness of joint source–background modeling for constraining emission from faint, background-dominated targets. Both sources are dominated by nuclear power-law emission with photon indices Γ ∼ 1.5–2, with NGC 4594 being slightly harder than IC 1459. The thermal contribution in both sources is fainter, but constant, with kT ∼ 0.5 keV (∼5 × 106 K). The power-law flux and Γ are strongly anticorrelated in both sources, as has been seen for other LLAGNs with radiatively inefficient accretion flows. NGC 4594 is the brighter source and exhibits significant aperiodic variability. Its variability timescale with an upper limit of 5–7 days indicates emission originating from ≾100 rg, at the scale of the inner accretion flow. A spectral break found at ∼6 keV, while tentative, could arise from synchrotron/inverse Compton emission. This high-cadence LLAGN X-ray monitoring campaign underlines the importance of multiwavelength variability studies for a sample of LLAGNs to truly understand their accretion and outflow physics. © 2025, CC BY.

关键词： Active Galactic Nuclei

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Developing Eye Tracking Exercise System for Treatment of Lazy Eye

Developing Eye Tracking Exercise System for Treatment of Laz...

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Medical Technologies National Conference (TIPTEKNO)

作者： Kaan Kurtel Melahat Kizil Umuthan Ercan Suzan E. Ozsimsek Hasan Baris Mihci Department of Software Engineering Izmir University of Economics Balcova Izmir Turkey Izmir University of Economics Paramedic Program Vocational School of Health Services Luleå University of Technology MSc. Student in Applied Artificial Intelligence E-commerce Project Manager Macquarie University MSc. Student in Data Science

The aim of this study is to develop a fun and effective computer application game for children with amblyopia and to prevent eye strain in people whose work or lifestyle involves constantly looking at screens. The main motivation is to use children’s enthusiasm for playing video games for the treatment of amblyopia. For this, hardware and software products were brought together, and a software was developed according to software engineering principles. The system was tested with a group of volunteers and necessary alterations were made.

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Exploring Drug Repurposing Success Stories Through a Network-based Approach: Insights from a Case Study

Exploring Drug Repurposing Success Stories Through a Network...

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IEEE International Conference on Bioinformatics and Biomedicine (BIBM)

作者： Elisa Pierini Ludovica Mazza Manuela Petti Paolo Tieri Data Science MSc Program Sapienza University of Rome Rome Italy DIAG Dept. Comp. Contr. Manag. Eng. Sapienza University of Rome Rome Italy IAC Institute for Applied Computing CNR National Research Council Rome Italy

ISBN: (数字)9798350386226

ISBN: (纸本)9798350386233

关键词： Drugs Proteins Obesity Costs Knowledge based systems Focusing Machine learning Biology Diabetes Diseases

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SHELLEY: Exploring Learning-Based Network Alignment on Biological data

SHELLEY: Exploring Learning-Based Network Alignment on Biolo...

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IEEE International Conference on Bioinformatics and Biomedicine (BIBM)

作者： Riccardo De Luca Manuela Petti Pietro H. Guzzi Paolo Tieri MSc Program in Data Science Sapienza University of Rome Rome Italy DIAG Dept. Comp. Contr. Manag. Eng. Sapienza University of Rome Rome Italy Dept. UMG Magna Graecia University Catanzaro Italy IAC Institute for Applied Computing CNR National Research Council Rome Italy

ISBN: (数字)9798350386226

ISBN: (纸本)9798350386233

Global network alignment is the computational problem of determining the similarity between nodes of different networks to establish a one-to-one correspondence between them. It has important applications in the biological field, particularly for discovering similar roles between the elements of different systems or for transferring knowledge from a well-studied system to another. In this paper, we present SHELLEY, a tool that facilitates the development, testing, and combination of learning-based network alignment algorithms by providing a set of modules that allow for the recreation and combination of both representation learning methods (RLMs) and deep matching methods (DMMs). We then present a case study in which we apply this tool to a protein-protein interaction network (PPI), demonstrating how the representation phase of RLMs is crucial for model robustness against *** code of SHELLEY is available at: https://***/rickydeluca/shelley

关键词： Representation learning Proteins Protein engineering Codes Biological system modeling Noise Robustness data models Biological information theory Testing

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Publisher Correction: Shared heritability and functional enrichment across six solid cancers

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Nature communications 2019年第1期10卷 4386页

作者： Xia Jiang Hilary K Finucane Fredrick R Schumacher Stephanie L Schmit Jonathan P Tyrer Younghun Han Kyriaki Michailidou Corina Lesseur Karoline B Kuchenbaecker Joe Dennis David V Conti Graham Casey Mia M Gaudet Jeroen R Huyghe Demetrius Albanes Melinda C Aldrich Angeline S Andrew Irene L Andrulis Hoda Anton-Culver Antonis C Antoniou Natalia N Antonenkova Susanne M Arnold Kristan J Aronson Banu K Arun Elisa V Bandera Rosa B Barkardottir Daniel R Barnes Jyotsna Batra Matthias W Beckmann Javier Benitez Sara Benlloch Andrew Berchuck Sonja I Berndt Heike Bickeböller Stephanie A Bien Carl Blomqvist Stefania Boccia Natalia V Bogdanova Stig E Bojesen Manjeet K Bolla Hiltrud Brauch Hermann Brenner James D Brenton Mark N Brook Joan Brunet Hans Brunnström Daniel D Buchanan Barbara Burwinkel Ralf Butzow Gabriella Cadoni Trinidad Caldés Maria A Caligo Ian Campbell Peter T Campbell Géraldine Cancel-Tassin Lisa Cannon-Albright Daniele Campa Neil Caporaso André L Carvalho Andrew T Chan Jenny Chang-Claude Stephen J Chanock Chu Chen David C Christiani Kathleen B M Claes Frank Claessens Judith Clements J Margriet Collée Marcia Cruz Correa Fergus J Couch Angela Cox Julie M Cunningham Cezary Cybulski Kamila Czene Mary B Daly Anna deFazio Peter Devilee Orland Diez Manuela Gago-Dominguez Jenny L Donovan Thilo Dörk Eric J Duell Alison M Dunning Miriam Dwek Diana M Eccles Christopher K Edlund Digna R Velez Edwards Carolina Ellberg D Gareth Evans Peter A Fasching Robert L Ferris Triantafillos Liloglou Jane C Figueiredo Olivia Fletcher Renée T Fortner Florentia Fostira Silvia Franceschi Eitan Friedman Steven J Gallinger Patricia A Ganz Judy Garber José A García-Sáenz Simon A Gayther Graham G Giles Andrew K Godwin Mark S Goldberg David E Goldgar Ellen L Goode Marc T Goodman Gary Goodman Kjell Grankvist Mark H Greene Henrik Gronberg Jacek Gronwald Pascal Guénel Niclas Håkansson Per Hall Ute Hamann Freddie C Hamdy Robert J Hamilton Jochen Hampe Aage Haugen Florian Heitz Rolando Herrero Peter Hillemanns Michael Hoffmeister Estrid Høgdall Yun-Ch Program in Genetic Epidemiology and Statistical Genetics Harvard T.H. Chan School of Public Health 677 Huntington Ave Boston MA 02115 USA. xiajiang@hsph.harvard.edu. Unit of Cardiovascular Epidemiology Institute of Environmental Medicine Karolinska Institutet Nobels vagen 13 17177 Stockholm Sweden. xiajiang@hsph.harvard.edu. Department of Epidemiology Harvard T.H. Chan School of Public Health 677 Huntington Ave Boston MA 02115 USA. Program in Medical and Population Genetics Broad Institute of MIT and Harvard 75 Ames St Cambridge MA 02142 USA. Department of Population and Quantitative Health Sciences Case Western Reserve University 10900 Eucid Avenue Cleveland OH 44106 USA. Seidman Cancer Center University Hospitals Cleveland OH 44106 USA. Department of Cancer Epidemiology H. Lee Moffitt Cancer Center and Research Institute 12902 Magnolia Dr. MRC-CANCONT Tampa FL 33612 USA. Department of Gastrointestinal Oncology H. Lee Moffitt Cancer Center and Research Institute 12902 Magnolia Dr. MRC-CANCONT Tampa FL 33612 USA. Centre for Cancer Genetic Epidemiology Department of Oncology University of Cambridge 2 Worts' Causeway Cambridge CB1 8RN UK. Department of Biomedical Data Science The Geisel School of Medicine at Dartmouth 1 Medical Center Drive Lebanon NH 03756 USA. Centre for Cancer Genetic Epidemiology Department of Public Health and Primary Care University of Cambridge 2 Worts' Causeway Cambridge CB1 8RN UK. Department of Electron Microscopy/Molecular Pathology The Cyprus Institute of Neurology and Genetics 1683 Nicosia Cyprus. Genetic Epidemiology Group International Agency for Research on Cancer 150 Cours Albert Thomas 69008 Lyon France. Section of Genetics International Agency for Research on Cancer 150 cours Albert Thomas 69008 Lyon France. Division of Psychiatry University College London Maple House 149 Tottenham Court Road London W1T 7NF UK. UCL Genetics Institute University College London Gower Street London WC1E 6BT UK.

An amendment to this paper has been published and can be accessed via a link at the top of the paper.

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