Filters








62 Hits in 4.3 sec

Convolutional Neural Networks for Multidrug-resistant and Drug-sensitive Tuberculosis Distinction

Daniel Braun, Michael Singhof, Martha Tatusch, Stefan Conrad
2017 Conference and Labs of the Evaluation Forum  
Tuberculosis is a widespread disease and one of the top causes of death worldwide. Especially the distinction between drug-sensitive and multidrug-resistant tuberculosis is still problematic.  ...  We then utilise a shallow convolutional neural network for the following classification.  ...  Acknowledgements Computational support and infrastructure was partially provided by the "Centre for Information and Media Technology" (ZIM) at the University of Düsseldorf (Germany).  ... 
dblp:conf/clef/0002ST017 fatcat:g6yoecpe3jch7aanwb5fas756a

Detection of Multidrug-resistant Tuberculosis using Convolutional Neural Networks and Decision Trees

Martha Tatusch, Stefan Conrad
2018 Conference and Labs of the Evaluation Forum  
Within the ImageCLEF 2018 challenge the automatic distinction between drug-sensitive and multidrug-resistant tuberculosis was investigated by only using the CT scan, age and gender of a patient.  ...  In this paper, we present different approaches using convolutional neural networks, decision trees and the combination of both classifiers.  ...  Since the distinction between drug-sensitive (DS) and multidrugresistant (MDR) tuberculosis is difficult and necessitates several expensive tests, it would be helpful to find an automated solution that  ... 
dblp:conf/clef/Tatusch018 fatcat:bpmr5kay2rfffkscunmpmnxdlm

A convolutional neural network highlights mutations relevant to antimicrobial resistance in Mycobacterium tuberculosis [article]

Anna G. Green, Chang H. Yoon, Michael L. Chen, Luca Freschi, Matthias I. Gröschel, Isaac Kohane, Andrew Beam, Maha Farhat
2021 bioRxiv   pre-print
Here, we present a deep convolutional neural network (CNN) that predicts the antibiotic resistance phenotypes of M. tuberculosis isolates.  ...  AbstractLong diagnostic wait times hinder international efforts to address multi-drug resistance in M. tuberculosis.  ...  Acknowledgements We thank members of the Farhat lab for discussion and input. We are grateful to Dr. Peter Koo, Dr.  ... 
doi:10.1101/2021.12.06.471431 fatcat:cyybgkeow5fuzlvqole4dvfjpa

Prediction of Multi Drug Resistant Tuberculosis using Machine Learning Techniques

2019 International Journal of Engineering and Advanced Technology  
Multi Drug Resistant Tuberculosis (MDR-TB) is a type of tuberculosis bacteria which are resistant to anti-TB drugs, drugs like isoniazid (INH) and rifampin (RMP).  ...  Mycobacterium Tuberculosis bacteria is the primary cause for Tuberculosis. TB is one of the main reasons of mortality around the world.  ...  International Journal of Engineering and Advanced Technology (IJEAT) ISSN: 2249 -8958, Volume-9 Issue-2, December, 2019  ... 
doi:10.35940/ijeat.b2531.129219 fatcat:leshfls3pbe7tduviypkqcbgpi

Beyond multidrug resistance: Leveraging rare variants with machine and statistical learning models in Mycobacterium tuberculosis resistance prediction

Michael L. Chen, Akshith Doddi, Jimmy Royer, Luca Freschi, Marco Schito, Matthew Ezewudo, Isaac S. Kohane, Andrew Beam, Maha Farhat
2019 EBioMedicine  
The diagnosis of multidrug resistant and extensively drug resistant tuberculosis is a global health priority.  ...  drug resistance to 10 anti-tuberculosis drugs.  ...  Fig. 7 . 7 A schematic of the multidrug wide and deep neural network architecture. Data flows from bottom to top through the wide (left) and deep (right) paths of the neural network.  ... 
doi:10.1016/j.ebiom.2019.04.016 pmid:31047860 pmcid:PMC6557804 fatcat:4pegfoz33zgfrj6am7kqdpsb7m

Evolution of Machine Learning in Tuberculosis Diagnosis: A Review of Deep Learning-Based Medical Applications

Manisha Singh, Gurubasavaraj Veeranna Pujar, Sethu Arun Kumar, Meduri Bhagyalalitha, Handattu Shankaranarayana Akshatha, Belal Abuhaija, Anas Ratib Alsoud, Laith Abualigah, Narasimha M. Beeraka, Amir H. Gandomi
2022 Electronics  
Well-timed diagnosis and treatment are an arch to full recovery of the patient. Computer-aided diagnosis (CAD) has been a hopeful choice for TB diagnosis.  ...  Tuberculosis (TB) is an infectious disease that has been a major menace to human health globally, causing millions of deaths yearly.  ...  Acknowledgments: The authors are thankful to Indian Council of Medical Research (ICMR), New Delhi for funding and the Principal, JSS College of Pharmacy, JSS Academy of Higher Education and Research, Mysore  ... 
doi:10.3390/electronics11172634 fatcat:gywjdavvi5f57lkzlijzazmk6m

Use of Artificial Intelligence and Machine Learning for Discovery of Drugs for Neglected Tropical Diseases

David A. Winkler
2021 Frontiers in Chemistry  
The pathways for the development of machine learning methods in the short to medium term and the use of other artificial intelligence methods for drug discovery is discussed.  ...  Here, the application of artificial intelligence, largely the subset called machine learning, to modelling and prediction of biological activities and discovery of new drugs for neglected tropical diseases  ...  Drug resistant malaria and TB are common, with almost 500,000 new cases of multidrug-resistant tuberculosis in 2016 and a 45% mortality rate worldwide.  ... 
doi:10.3389/fchem.2021.614073 pmid:33791277 pmcid:PMC8005575 fatcat:obk5rdcpb5eblmghmey63ulztm

Paving the way for precise diagnostics of antimicrobial resistant bacteria

Hao Wang, Chenhao Jia, Hongzhao Li, Rui Yin, Jiang Chen, Yan Li, Min Yue
2022 Frontiers in Molecular Biosciences  
, which is not just for an effective controlling strategy on AMR but also for protecting the longevity of valuable antimicrobials currently and in the future.  ...  Nowadays, biotechnology and machine learning advancements help create more fundamental knowledge of distinct spatiotemporal dynamics in AMR bacterial adaptation and evolutionary processes.  ...  ; gentamicin 4 drugs' Accuracy: 0.75-0.88 Forest; Convolutional Neural Network Logistic Regression for four drugs' Accuracy: 0.77-0.85 Random Forest for 4 drugs' Accuracy: 0.77-0.92 Convolutional Neural  ... 
doi:10.3389/fmolb.2022.976705 pmid:36032670 pmcid:PMC9413203 fatcat:nvvtpm5hljgt5dilkdl6z7myf4

Which Current and Novel Diagnostic Avenues for Bacterial Respiratory Diseases?

Héloïse Rytter, Anne Jamet, Mathieu Coureuil, Alain Charbit, Elodie Ramond
2020 Frontiers in Microbiology  
While multidrug-resistant bacteria is one of the biggest health threats in the coming decades, clinicians urgently need access to novel diagnostic technologies.  ...  We also aim to highlight the mutual benefits of fundamental and clinical studies for a better understanding of lung infections and their more efficient diagnostic management.  ...  To perform these analyses, several convolutional neural networks (CNN or ConvNet) have been designed.  ... 
doi:10.3389/fmicb.2020.616971 pmid:33362754 pmcid:PMC7758241 fatcat:p5pjxkafhvcvxo5ue7ef4xc32i

Artificial Intelligence in Translational Medicine

Simone Brogi, Vincenzo Calderone
2021 International Journal of Translational Medicine  
in breakthroughs for advancing human health.  ...  Consequently, during the last decade the system for managing, analyzing, processing and extrapolating information from scientific data has been considerably modified in several fields, including the medical  ...  ML/DL approaches suitable in the drug discovery field include RF, Artificial Neural Networks (ANN), Deep Neural Networks (DNN), Graph Convolutional Neural Networks (GCNN), Convolutional Neural Networks  ... 
doi:10.3390/ijtm1030016 fatcat:c6g6ld26gjg6jbkcddauo44qvu

Co-AMPpred for in silico-aided predictions of antimicrobial peptides by integrating composition-based features

Onkar Singh, Wen-Lian Hsu, Emily Chia-Yu Su
2021 BMC Bioinformatics  
However, a microorganism's ability to adapt and to resist existing antibiotics triggered the scientific community to develop alternatives to conventional antibiotics.  ...  Our code and datasets are available at https://github.com/onkarS23/CoAMPpred.  ...  Yu-Lun Hsieh and Mr. Tso-Yang Yeh for their support and suggestions in experimental work. We want to extend our gratitude to Ms.  ... 
doi:10.1186/s12859-021-04305-2 fatcat:mbgkiqahnfbebd2jxijdbpjs4m

Integrated Computational Approaches and Tools for Allosteric Drug Discovery

Sheik Amamuddy, Veldman, Manyumwa, Khairallah, Agajanian, Oluyemi, Verkhivker, Tastan Bishop
2020 International Journal of Molecular Sciences  
and modulatorswith some applications to pathogen resistance and precision medicine.  ...  The proliferation of novel computational approaches for predictingligand–protein interactions and binding using dynamic and network-centric perspectives has ledto new insights into allosteric mechanisms  ...  The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.  ... 
doi:10.3390/ijms21030847 pmid:32013012 pmcid:PMC7036869 fatcat:y2z5vnsylnduhc7gntjkpcoslq

PhD Thesis. Computer-Aided Assessment of Tuberculosis with Radiological Imaging: From rule-based methods to Deep Learning [article]

Pedro M. Gordaliza
2022 arXiv   pre-print
However, this procedure is unfeasible to process the thousands of volume images belonging to the different TB animal models and humans required for a suitable (pre-)clinical trial.  ...  Tuberculosis (TB) is an infectious disease caused by Mycobacterium tuberculosis (Mtb.) that produces pulmonary damage due to its airborne nature.  ...  The data corresponding to new cases in 2020 with MDR-TB (Multidrug-Resistant Tuberculosis) and RR-TB (Rifampicin-Resistant Tuberculosis), as well as the percentage of cases that had already been treated  ... 
arXiv:2205.15909v1 fatcat:b2qvw2kvlbhk5lhomqezwnbt4u

High-resolution mapping of fluoroquinolones in TB rabbit lesions reveals specific distribution in immune cell types

Landry Blanc, Isaac B Daudelin, Brendan K Podell, Pei-Yu Chen, Matthew Zimmerman, Amanda J Martinot, Rada M Savic, Brendan Prideaux, Véronique Dartois
2018 eLife  
This work constitutes a methodological advance for the co-localization of drugs and infectious agents at high spatial resolution in diseased tissues, which can be applied to other diseases with complex  ...  Understanding the distribution patterns of antibiotics at the site of infection is paramount to selecting adequate drug regimens and developing new antibiotics.  ...  Acknowledgements We thank M Gennaro and her team for providing guidance with flow cytometry experiments. Additional information  ... 
doi:10.7554/elife.41115 pmid:30427309 pmcid:PMC6249001 fatcat:riqnnhvgl5h6diyxyqnl6kixsa

Deep learning approaches for natural product discovery from plant endophytic microbiomes

Shiva Abdollahi Aghdam, Amanda May Vivian Brown
2021 Environmental Microbiome  
AbstractPlant microbiomes are not only diverse, but also appear to host a vast pool of secondary metabolites holding great promise for bioactive natural products and drug discovery.  ...  Yet, most microbes within plants appear to be uncultivable, and for those that can be cultivated, their metabolic potential lies largely hidden through regulatory silencing of biosynthetic genes.  ...  Acknowledgements We thank Carolin Frank for useful comments on the draft manuscript.  ... 
doi:10.1186/s40793-021-00375-0 pmid:33758794 pmcid:PMC7972023 fatcat:tysm3alwwvab3ptmzbkeilwcfq
« Previous Showing results 1 — 15 out of 62 results