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Multiscale modeling meets machine learning: What can we learn? [article]

Grace C.Y. Peng, Mark Alber, Adrian Buganza Tepole, William Cannon, Suvranu De, Salvador Dura-Bernal, Krishna Garikipati, George Karniadakis, William W. Lytton, Paris Perdikaris, Linda Petzold, Ellen Kuhl
2020 arXiv   pre-print
With a view towards applications in the life sciences, we discuss the state of the art of combining machine learning and multiscale modeling, identify applications and opportunities, raise open questions  ...  We anticipate that it will stimulate discussion within the community of computational mechanics and reach out to other disciplines including mathematics, statistics, computer science, artificial intelligence  ...  How can we use machine learning to bridge scales? For example, machine learning could be used to explore responses of both immune and tumor cells in cancer based on single-cell data.  ... 
arXiv:1911.11958v2 fatcat:u4d4snmwq5bfrcvt7is6fkswgy

Machine Learning Applications for Therapeutic Tasks with Genomics Data [article]

Kexin Huang, Cao Xiao, Lucas M. Glass, Cathy W. Critchlow, Greg Gibson, Jimeng Sun
2021 arXiv   pre-print
Thanks to the increasing availability of genomics and other biomedical data, many machine learning approaches have been proposed for a wide range of therapeutic discovery and development tasks.  ...  This survey overviews recent research at the intersection of machine learning, genomics, and therapeutic development.  ...  A. et al. (2018), ‘Predicting tumor cell line response to drug pairs with deep learning’, BMC Bioinformatics 19(18), 71–79. Xiong, H. Y., Alipanahi, B., Lee, L.  ... 
arXiv:2105.01171v1 fatcat:d2nbrjt4tvak7momoxxjlmqk2m

Domain adaptation-based deep learning for automated Tumor Cell (TC) scoring and survival analysis on PD-L1 stained tissue images

Ansh Kapil, Armin Meier, Keith Steele, Marlon Rebelatto, Katharina Nekolla, Alexander Haragan, Abraham Silva, Aleksandra Zuraw, Craig Barker, Marietta L. Scott, Tobias Wiestler, Simon Lanzmich (+2 others)
2021 IEEE Transactions on Medical Imaging  
The first system learns to replicate the pathologist assessment of the Tumor Cell (TC) score with a cut-point for positivity at 25% for patient stratification.  ...  We report the ability of two deep learning-based decision systems to stratify non-small cell lung cancer (NSCLC) patients treated with checkpoint inhibitor therapy into two distinct survival groups.  ...  Based on this mechanism of action it was hypothesized that the percentage of PD-L1 positive epithelial cells in tumor tissue samples is associated with response to therapy and overall survival.  ... 
doi:10.1109/tmi.2021.3081396 pmid:34003747 fatcat:74q5zxbiszcerbuqsa6jvu4gui

Recent evolutions of machine learning applications in clinical laboratory medicine

Sander De Bruyne, Marijn M. Speeckaert, Wim Van Biesen, Joris R. Delanghe
2020 Critical reviews in clinical laboratory sciences  
, and the detection of antimicrobial resistance.  ...  Machine learning (ML) is gaining increased interest in clinical laboratory medicine, mainly triggered by the decreased cost of generating and storing data using laboratory automation and computational  ...  The authors state that, given the broad range of algorithms, ML-based automated verification systems are likely to be widely applicable and have the potential to significantly improve workflow and productivity  ... 
doi:10.1080/10408363.2020.1828811 pmid:33045173 fatcat:woqldsqrgvcpnd6iut3hilab3i

Statistical contributions to bioinformatics: Design, modelling, structure learning and integration

Jeffrey S. Morris, Veerabhadran Baladandayuthapani
2017 Statistical Modelling  
The advent of high-throughput multi-platform genomics technologies providing whole-genome molecular summaries of biological samples has revolutionalized biomedical research.  ...  extraction, (3) unified modeling, and (4) structure learning and integration.  ...  These problems are partially responsible for limiting the impact of high-throughput proteomics on biomedical science (Clark and Gutstein, 2008) .  ... 
doi:10.1177/1471082x17698255 pmid:29129969 pmcid:PMC5679480 fatcat:nhyi5e2nqrh4pdatrnfnt7h6p4

Multiplex bioimaging of single-cell spatial profiles for precision cancer diagnostics and therapeutics

Mayar Allam, Shuangyi Cai, Ahmet F. Coskun
2020 npj Precision Oncology  
In this mass, normal and malignant cells create tumor microenvironment that is heterogeneous among patients.  ...  A residue from primary tumors leaks into the bloodstream as cell clusters and single cells, providing clues about disease progression and therapeutic response.  ...  Distinct cell invasion and drug response profiles revealed that the tumor microenvironment can significantly influence cancerogenesis 89 .  ... 
doi:10.1038/s41698-020-0114-1 pmid:32377572 pmcid:PMC7195402 fatcat:abv3srgl55ekjpqpdascerxxdq

Prediction of individual response to anticancer therapy: historical and future perspectives

Florian T. Unger, Irene Witte, Kerstin A. David
2014 Cellular and Molecular Life Sciences (CMLS)  
Twenty years ago, the development of high-throughput technologies, e.g. cDNA microarrays enabled a more detailed analysis of drug responses.  ...  In the past, cell cultures were used as in vitro models to predict in vivo response to chemotherapy.  ...  Acknowledgments We thank BlackPool Design for the preparation of original figures. Conflict of interest The authors declare that there is no conflict of interest.  ... 
doi:10.1007/s00018-014-1772-3 pmid:25387856 pmcid:PMC4309902 fatcat:g5ih63izk5aojo2izjyoueqrsq

A technology platform to assess multiple cancer agents simultaneously within a patient's tumor

Richard A. Klinghoffer, S. Bahram Bahrami, Beryl A. Hatton, Jason P. Frazier, Alicia Moreno-Gonzalez, Andrew D. Strand, William S. Kerwin, Joseph R. Casalini, Derek J. Thirstrup, Sheng You, Shelli M. Morris, Korashon L. Watts (+16 others)
2015 Science Translational Medicine  
Abbasi-Shaffer from the Investigational Drug Services in the Seattle Cancer Care Alliance for the drug loading of the CIVO device and their valuable input toward the improvement of the technology.  ...  Bowell at the University of Washington for their contribution in the feasibility clinical study and their valuable input toward the improvement of the clinical technology; and S. Miller and N.  ...  As a consequence, assessments of the impact of potential new drugs are often flawed, and seemingly promising agents that kill cancer cells under standard tissue culture conditions translate poorly into  ... 
doi:10.1126/scitranslmed.aaa7489 pmid:25904742 pmcid:PMC4770902 fatcat:wayqhk3ou5b4ro45uc34fn7rtq

Precision Medicine Treatment in Acute Myeloid Leukemia Is Not a Dream

Ugo Testa, Elvira Pelosi, Germana Castelli
2021 Hemato  
, changing during disease evolution and in response to treatment.  ...  AML is a highly heterogenous disease that includes many molecular subtypes; each subtype is heterogeneous both for the presence of variable co-mutations and complex combinations of clones and subclones  ...  Conflicts of Interest: The authors declare no conflict of interest.  ... 
doi:10.3390/hemato2010008 fatcat:5eco3xgs6jdn7oux6wuu5uk6aa

Replication Fork Remodeling and Therapy Escape in DNA Damage Response-Deficient Cancers

Martin Liptay, Joana S. Barbosa, Sven Rottenberg
2020 Frontiers in Oncology  
However, in BRCA-deficient tumors, loss of these factors leads to restored stability of RFs and acquired drug resistance.  ...  Most cancers have lost a critical DNA damage response (DDR) pathway during tumor evolution.  ...  Here, we expect that computational pathology and deep learning algorithms will have a major impact to overcome the problem of inter-observer variability.  ... 
doi:10.3389/fonc.2020.00670 pmid:32432041 pmcid:PMC7214843 fatcat:22p4rrkre5bhnk4djyytlmfebe

Artificial Intelligence in Digital Breast Pathology: Techniques and Applications

Asmaa Ibrahim, Paul Gamble, Ronnachai Jaroensri, Mohammed M. Abdelsamea, Craig H. Mermel, Po-Hsuan Cameron Chen, Emad A. Rakha
2019 Breast  
In parallel, advances in artificial intelligence (AI) along with the growing digitization of pathology slides for the primary diagnosis are a promising approach to meet the demand for more accurate detection  ...  , classification and prediction of behaviour of breast tumours.  ...  It's our belief that for machine learning to actually impact breast cancer patients, leading researchers need to recognize and shoulder (at least in part) the responsibility for developing tools that people  ... 
doi:10.1016/j.breast.2019.12.007 pmid:31935669 pmcid:PMC7375550 fatcat:u24dfeb6zndvpcsx56cgkhcifm

Computational Approaches in Theranostics: Mining and Predicting Cancer Data

Tânia F. G. G. Cova, Daniel J. Bento, Sandra C. C. Nunes
2019 Pharmaceutics  
states, and address other relevant aspects, including tumor detection and heterogeneity, progression and metastasis, and drug resistance.  ...  The ability to understand the complexity of cancer-related data has been prompted by the applications of (1) computer and data sciences, including data mining, predictive analytics, machine learning, and  ...  Different machine learning and artificial intelligence algorithms have provided tools for the automated assessment of tumor response, which have reduced the number of tasks and the variation between readings  ... 
doi:10.3390/pharmaceutics11030119 pmid:30871264 pmcid:PMC6471740 fatcat:f3ktwwwcsvgepiopdhifiglebi

Ten most important things to learn from the ACCF/AHA 2011 expert consensus document on hypertension in the elderly

Wilbert S. Aronow, Maciej Banach
2011 Blood Pressure  
National Committee on Pre- vention, Detection, Evaluation, and Treatment of High Blood Pressure" (22) .  ...  Although "The Seventh Report of the Joint National Committee on Prevention, Detection, Evaluation, and Treatment of High Blood Pressure" recommends that elderly hypertensive patients with cerebrovascular  ... 
doi:10.3109/08037051.2011.615902 pmid:21991999 fatcat:xja3izenvvhbnnobspy6pgjcgy

34th Annual Meeting & Pre-Conference Programs of the Society for Immunotherapy of Cancer (SITC 2019): part 1

2019 Journal for ImmunoTherapy of Cancer  
We found that patients with recurrence post-LT have significantly higher densities of MPO+ PMNs compared to those with no recurrence.  ...  Slides were stained using qmIF for MPO (PMNs), CD3 (T cells), CD8 (cytotoxic T cells), CD68 (macrophages), HLA-DR (immune activation), and Hep-Par1 (hepatocytes/tumor).  ...  Acknowledgements We acknowledge the support of all investigators and clinical coordinators responsible for enrolling patients to this trial.  ... 
doi:10.1186/s40425-019-0763-1 pmid:31694725 pmcid:PMC6833189 fatcat:y2lipm7rwrav3jdwdo3um27qhu

Deep learning applications in pulmonary medical imaging: recent updates and insights on COVID-19

Hanan Farhat, George E. Sakr, Rima Kilany
2020 Machine Vision and Applications  
Shortly after deep learning algorithms were applied to Image Analysis, and more importantly to medical imaging, their applications increased significantly to become a trend.  ...  This paper reviews the development of deep learning applications in medical image analysis targeting pulmonary imaging and giving insights of contributions to COVID-19.  ...  A drawn conclusion is that the most preferred trained CNNs are the end-to-end ones, on which transfer learning has high impact.  ... 
doi:10.1007/s00138-020-01101-5 pmid:32834523 pmcid:PMC7386599 fatcat:tkkylrptc5hkpoj52hjs3kuttu
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