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“Machine Learning” “Precision Medicine”

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https://www.readbyqxmd.com/read/30298124/classification-of-pediatric-asthma-from-phenotype-discovery-to-clinical-practice
#1
REVIEW
Ceyda Oksel, Sadia Haider, Sara Fontanella, Clement Frainay, Adnan Custovic
Advances in big data analytics have created an opportunity for a step change in unraveling mechanisms underlying the development of complex diseases such as asthma, providing valuable insights that drive better diagnostic decision-making in clinical practice, and opening up paths to individualized treatment plans. However, translating findings from data-driven analyses into meaningful insights and actionable solutions requires approaches and tools which move beyond mining and patterning longitudinal data. The purpose of this review is to summarize recent advances in phenotyping of asthma, to discuss key hurdles currently hampering the translation of phenotypic variation into mechanistic insights and clinical setting, and to suggest potential solutions that may address these limitations and accelerate moving discoveries into practice...
2018: Frontiers in Pediatrics
https://www.readbyqxmd.com/read/30289891/a-quantile-regression-forest-based-method-to-predict-drug-response-and-assess-prediction-reliability
#2
Yun Fang, Peirong Xu, Jialiang Yang, Yufang Qin
Drug response prediction is a critical step for personalized treatment of cancer patients and ultimately leads to precision medicine. A lot of machine-learning based methods have been proposed to predict drug response from different types of genomic data. However, currently available methods could only give a "point" prediction of drug response value but fail to provide the reliability and distribution of the prediction, which are of equal interest in clinical practice. In this paper, we proposed a method based on quantile regression forest and applied it to the CCLE dataset...
2018: PloS One
https://www.readbyqxmd.com/read/30267047/prediction-models-of-functional-outcomes-for-individuals-in-the-clinical-high-risk-state-for-psychosis-or-with-recent-onset-depression-a-multimodal-multisite-machine-learning-analysis
#3
Nikolaos Koutsouleris, Lana Kambeitz-Ilankovic, Stephan Ruhrmann, Marlene Rosen, Anne Ruef, Dominic B Dwyer, Marco Paolini, Katharine Chisholm, Joseph Kambeitz, Theresa Haidl, André Schmidt, John Gillam, Frauke Schultze-Lutter, Peter Falkai, Maximilian Reiser, Anita Riecher-Rössler, Rachel Upthegrove, Jarmo Hietala, Raimo K R Salokangas, Christos Pantelis, Eva Meisenzahl, Stephen J Wood, Dirk Beque, Paolo Brambilla, Stefan Borgwardt
Importance: Social and occupational impairments contribute to the burden of psychosis and depression. There is a need for risk stratification tools to inform personalized functional-disability preventive strategies for individuals in at-risk and early phases of these illnesses. Objective: To determine whether predictors associated with social and role functioning can be identified in patients in clinical high-risk (CHR) states for psychosis or with recent-onset depression (ROD) using clinical, imaging-based, and combined machine learning; assess the geographic, transdiagnostic, and prognostic generalizability of machine learning and compare it with human prognostication; and explore sequential prognosis encompassing clinical and combined machine learning...
September 26, 2018: JAMA Psychiatry
https://www.readbyqxmd.com/read/30247662/radiomics-with-artificial-intelligence-for-precision-medicine-in-radiation-therapy
#4
Hidetaka Arimura, Mazen Soufi, Kamezawa, Kenta Ninomiya, Masahiro Yamada
Recently, the concept of radiomics has emerged from radiation oncology. It is a novel approach for solving the issues of precision medicine and how it can be performed, based on multimodality medical images that are non-invasive, fast and low in cost. Radiomics is the comprehensive analysis of massive numbers of medical images in order to extract a large number of phenotypic features (radiomic biomarkers) reflecting cancer traits, and it explores the associations between the features and patients' prognoses in order to improve decision-making in precision medicine...
September 22, 2018: Journal of Radiation Research
https://www.readbyqxmd.com/read/30243959/neural-representations-of-aversive-value-encoding-in-pain-catastrophizers
#5
Christopher A Brown, Abeer F Almarzouki, Richard J Brown, Anthony K P Jones
Chronic pain is exacerbated by maladaptive cognition such as pain catastrophizing (PC). Biomarkers of PC mechanisms may aid precision medicine for chronic pain. Here, we investigate EEG biomarkers using mass univariate and multivariate (machine learning) approaches. We test theoretical notions that PC results from a combination of augmented aversive-value encoding ("magnification") and persistent expectations of pain ("rumination"). Healthy individuals with high or low levels of PC underwent an experimental pain model involving nociceptive laser stimuli preceded by cues predicting forthcoming pain intensity...
September 21, 2018: NeuroImage
https://www.readbyqxmd.com/read/30231499/deep-learning-in-drug-discovery-and-medicine-scratching-the-surface
#6
REVIEW
Dibyendu Dana, Satishkumar V Gadhiya, Luce G St Surin, David Li, Farha Naaz, Quaisar Ali, Latha Paka, Michael A Yamin, Mahesh Narayan, Itzhak D Goldberg, Prakash Narayan
The practice of medicine is ever evolving. Diagnosing disease, which is often the first step in a cure, has seen a sea change from the discerning hands of the neighborhood physician to the use of sophisticated machines to use of information gleaned from biomarkers obtained by the most minimally invasive of means. The last 100 or so years have borne witness to the enormous success story of allopathy, a practice that found favor over earlier practices of medical purgatory and homeopathy. Nevertheless, failures of this approach coupled with the omics and bioinformatics revolution spurred precision medicine, a platform wherein the molecular profile of an individual patient drives the selection of therapy...
September 18, 2018: Molecules: a Journal of Synthetic Chemistry and Natural Product Chemistry
https://www.readbyqxmd.com/read/30223787/data-driven-human-transcriptomic-modules-determined-by-independent-component-analysis
#7
Weizhuang Zhou, Russ B Altman
BACKGROUND: Analyzing the human transcriptome is crucial in advancing precision medicine, and the plethora of over half a million human microarray samples in the Gene Expression Omnibus (GEO) has enabled us to better characterize biological processes at the molecular level. However, transcriptomic analysis is challenging because the data is inherently noisy and high-dimensional. Gene set analysis is currently widely used to alleviate the issue of high dimensionality, but the user-defined choice of gene sets can introduce biasness in results...
September 17, 2018: BMC Bioinformatics
https://www.readbyqxmd.com/read/30216362/prediction-of-early-breast-cancer-patient-survival-using-ensembles-of-hypoxia-signatures
#8
Inna Y Gong, Natalie S Fox, Vincent Huang, Paul C Boutros
BACKGROUND: Biomarkers are a key component of precision medicine. However, full clinical integration of biomarkers has been met with challenges, partly attributed to analytical difficulties. It has been shown that biomarker reproducibility is susceptible to data preprocessing approaches. Here, we systematically evaluated machine-learning ensembles of preprocessing methods as a general strategy to improve biomarker performance for prediction of survival from early breast cancer. RESULTS: We risk stratified breast cancer patients into either low-risk or high-risk groups based on four published hypoxia signatures (Buffa, Winter, Hu, and Sorensen), using 24 different preprocessing approaches for microarray normalization...
2018: PloS One
https://www.readbyqxmd.com/read/30193110/decoding-the-genomics-of-abdominal-aortic-aneurysm
#9
Jingjing Li, Cuiping Pan, Sai Zhang, Joshua M Spin, Alicia Deng, Lawrence L K Leung, Ronald L Dalman, Philip S Tsao, Michael Snyder
A key aspect of genomic medicine is to make individualized clinical decisions from personal genomes. We developed a machine-learning framework to integrate personal genomes and electronic health record (EHR) data and used this framework to study abdominal aortic aneurysm (AAA), a prevalent irreversible cardiovascular disease with unclear etiology. Performing whole-genome sequencing on AAA patients and controls, we demonstrated its predictive precision solely from personal genomes. By modeling personal genomes with EHRs, this framework quantitatively assessed the effectiveness of adjusting personal lifestyles given personal genome baselines, demonstrating its utility as a personal health management tool...
September 6, 2018: Cell
https://www.readbyqxmd.com/read/30170101/a-deep-look-into-the-future-of-quantitative-imaging-in-oncology-a-statement-of-working-principles-and-proposal-for-change
#10
REVIEW
Olivier Morin, Martin Vallières, Arthur Jochems, Henry C Woodruff, Gilmer Valdes, Steve E Braunstein, Joachim E Wildberger, Javier E Villanueva-Meyer, Vasant Kearney, Sue S Yom, Timothy D Solberg, Philippe Lambin
The adoption of enterprise digital imaging, along with the development of quantitative imaging methods and the re-emergence of statistical learning, has opened the opportunity for transformative data science research for more personalized cancer treatments. In the last 5 years, accumulating evidence has indicated that non-invasive advanced imaging analytics, i.e. radiomics, can reveal key components of tumor phenotype for multiple lesions at multiple time points over the course of treatment. Many groups using home-grown software have extracted engineered and deep quantitative features on three-dimensional medical images for better spatial and longitudinal understanding of tumor biology and for the prediction of diverse outcomes...
August 28, 2018: International Journal of Radiation Oncology, Biology, Physics
https://www.readbyqxmd.com/read/30145981/from-hype-to-reality-data-science-enabling-personalized-medicine
#11
Holger Fröhlich, Rudi Balling, Niko Beerenwinkel, Oliver Kohlbacher, Santosh Kumar, Thomas Lengauer, Marloes H Maathuis, Yves Moreau, Susan A Murphy, Teresa M Przytycka, Michael Rebhan, Hannes Röst, Andreas Schuppert, Matthias Schwab, Rainer Spang, Daniel Stekhoven, Jimeng Sun, Andreas Weber, Daniel Ziemek, Blaz Zupan
BACKGROUND: Personalized, precision, P4, or stratified medicine is understood as a medical approach in which patients are stratified based on their disease subtype, risk, prognosis, or treatment response using specialized diagnostic tests. The key idea is to base medical decisions on individual patient characteristics, including molecular and behavioral biomarkers, rather than on population averages. Personalized medicine is deeply connected to and dependent on data science, specifically machine learning (often named Artificial Intelligence in the mainstream media)...
August 27, 2018: BMC Medicine
https://www.readbyqxmd.com/read/30124358/rise-of-deep-learning-for-genomic-proteomic-and-metabolomic-data-integration-in-precision-medicine
#12
Dmitry Grapov, Johannes Fahrmann, Kwanjeera Wanichthanarak, Sakda Khoomrung
Machine learning (ML) is being ubiquitously incorporated into everyday products such as Internet search, email spam filters, product recommendations, image classification, and speech recognition. New approaches for highly integrated manufacturing and automation such as the Industry 4.0 and the Internet of things are also converging with ML methodologies. Many approaches incorporate complex artificial neural network architectures and are collectively referred to as deep learning (DL) applications. These methods have been shown capable of representing and learning predictable relationships in many diverse forms of data and hold promise for transforming the future of omics research and applications in precision medicine...
August 20, 2018: Omics: a Journal of Integrative Biology
https://www.readbyqxmd.com/read/30082298/machine-learning-and-genomics-precision-medicine-versus-patient-privacy
#13
REVIEW
C-A Azencott
Machine learning can have a major societal impact in computational biology applications. In particular, it plays a central role in the development of precision medicine, whereby treatment is tailored to the clinical or genetic features of the patient. However, these advances require collecting and sharing among researchers large amounts of genomic data, which generates much concern about privacy. Researchers, study participants and governing bodies should be aware of the ways in which the privacy of participants might be compromised, as well as of the large body of research on technical solutions to these issues...
September 13, 2018: Philosophical Transactions. Series A, Mathematical, Physical, and Engineering Sciences
https://www.readbyqxmd.com/read/30082061/using-technology-to-inform-and-deliver-precise-personalized-care-to-patients-with-end-stage-kidney-disease
#14
REVIEW
Len Usvyat, Lorien S Dalrymple, Franklin W Maddux
Consistent with the increase of precision medicine, the care of patients with end-stage kidney disease (ESKD) requiring maintenance dialysis therapy should evolve to become more personalized. Precise and personalized care is nuanced and informed by a number of factors including an individual's needs and preferences, disease progression, and response to and tolerance of treatments. Technology can support the delivery of more precise and personalized care through multiple mechanisms, including more accurate and real-time assessments of key care elements, enhanced treatment monitoring, and remote monitoring of home dialysis therapies...
July 2018: Seminars in Nephrology
https://www.readbyqxmd.com/read/30076490/automatic-normalization-of-anatomical-phrases-in-radiology-reports-using-unsupervised-learning
#15
Amir M Tahmasebi, Henghui Zhu, Gabriel Mankovich, Peter Prinsen, Prescott Klassen, Sam Pilato, Rob van Ommering, Pritesh Patel, Martin L Gunn, Paul Chang
In today's radiology workflow, free-text reporting is established as the most common medium to capture, store, and communicate clinical information. Radiologists routinely refer to prior radiology reports of a patient to recall critical information for new diagnosis, which is quite tedious, time consuming, and prone to human error. Automatic structuring of report content is desired to facilitate such inquiry of information. In this work, we propose an unsupervised machine learning approach to automatically structure radiology reports by detecting and normalizing anatomical phrases based on the Systematized Nomenclature of Medicine-Clinical Terms (SNOMED CT) ontology...
August 3, 2018: Journal of Digital Imaging: the Official Journal of the Society for Computer Applications in Radiology
https://www.readbyqxmd.com/read/30051410/big-data-analysis-cluster-analysis-and-machine-learning-approaches
#16
Amparo Alonso-Betanzos, Verónica Bolón-Canedo
Medicine will experience many changes in the coming years because the so-called "medicine of the future" will be increasingly proactive, featuring four basic elements: predictive, personalized, preventive, and participatory. Drivers for these changes include the digitization of data in medicine and the availability of computational tools that deal with massive volumes of data. Thus, the need to apply machine-learning methods to medicine has increased dramatically in recent years while facing challenges related to an unprecedented large number of clinically relevant features and highly specific diagnostic tests...
2018: Advances in Experimental Medicine and Biology
https://www.readbyqxmd.com/read/30018130/machine-learning-for-better-prognostic-stratification-and-driver-gene-identification-using-somatic-copy-number-variations-in-anaplastic-oligodendroglioma
#17
Shai Rosenberg, Francois Ducray, Agusti Alentorn, Caroline Dehais, Nabila Elarouci, Aurelie Kamoun, Yannick Marie, Marie-Laure Tanguy, Aurélien De Reynies, Karima Mokhtari, Dominique Figarella-Branger, Jean-Yves Delattre, Ahmed Idbaih
BACKGROUND: 1p/19q-codeleted anaplastic gliomas have variable clinical behavior. We have recently shown that the common 9p21.3 allelic loss is an independent prognostic factor in this tumor type. The aim of this study is to identify less frequent genomic copy number variations (CNVs) with clinical importance that may shed light on molecular oncogenesis of this tumor type. MATERIALS AND METHODS: A cohort of 197 patients with anaplastic oligodendroglioma was collected as part of the French POLA network...
July 17, 2018: Oncologist
https://www.readbyqxmd.com/read/29994057/physonline-an-online-feature-extraction-and-machine-learning-pipeline-for-real-time-analysis-of-streaming-physiological-data
#18
Jacob Sutton, Ruhi Mahajan, Oguz Akbilgic, Rishikesan Kamaleswaran
Real-time analysis of streaming physiological data to identify earlier abnormal conditions is an important aspect of precision medicine. However, open-source systems supporting this workflow are lacking. In this paper, we present PhysOnline, a pipeline built on the open-source Apache Spark platform to ingest streaming physiological data for online feature extraction and machine learning. We consider scalability factors for horizontal deployment to support growing analysis requirements. We further integrate real-time feature extraction, including pattern recognition methods as well as descriptive statistical components to identify temporal characteristics of waveform signals...
May 2, 2018: IEEE Journal of Biomedical and Health Informatics
https://www.readbyqxmd.com/read/29993662/transfer-learning-for-molecular-cancer-classification-using-deep-neural-networks
#19
Rahul Kumar Sevakula, Vikas Singh, Nishchal K Verma, Chandan Kumar, Yan Cui
The emergence of deep learning has impacted numerous machine learning based applications and research. The reason for its success lies in two main advantages: 1) it provides the ability to learn very complex non-linear relationships between features and 2) it allows one to leverage information from unlabeled data that does not belong to the problem being handled. This paper presents a transfer learning procedure for cancer classification, which uses feature selection and normalization techniques in conjunction with stacked sparse auto-encoders on gene expression data...
April 4, 2018: IEEE/ACM Transactions on Computational Biology and Bioinformatics
https://www.readbyqxmd.com/read/29988820/applied-machine-learning-for-the-prediction-of-growth-of-abdominal-aortic-aneurysm-in-humans
#20
R Lee, D Jarchi, R Perera, A Jones, I Cassimjee, A Handa, D A Clifton
Objective: Accurate prediction of abdominal aortic aneurysm (AAA) growth in an individual can allow personalised stratification of surveillance intervals and better inform the timing for surgery. The authors recently described the novel significant association between flow mediated dilatation (FMD) and future AAA growth. The feasibility of predicting future AAA growth was explored in individual patients using a set of benchmark machine learning techniques. Methods: The Oxford Abdominal Aortic Aneurysm Study (OxAAA) prospectively recruited AAA patients undergoing the routine NHS management pathway...
2018: EJVES Short Reports
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