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“Machine Learning” “Precision Medicine”

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https://www.readbyqxmd.com/read/30531069/opportunities-and-challenges-for-developing-closed-loop-bioelectronic-medicines
#1
REVIEW
Patrick D Ganzer, Gaurav Sharma
The peripheral nervous system plays a major role in the maintenance of our physiology. Several peripheral nerves intimately regulate the state of the brain, spinal cord, and visceral systems. A new class of therapeutics, called bioelectronic medicines, are being developed to precisely regulate physiology and treat dysfunction using peripheral nerve stimulation. In this review, we first discuss new work using closed-loop bioelectronic medicine to treat upper limb paralysis. In contrast to open-loop bioelectronic medicines, closed-loop approaches trigger 'on demand' peripheral nerve stimulation due to a change in function (e...
January 2019: Neural Regeneration Research
https://www.readbyqxmd.com/read/30523334/machine-learning-based-patient-specific-prediction-models-for-knee-osteoarthritis
#2
REVIEW
Afshin Jamshidi, Jean-Pierre Pelletier, Johanne Martel-Pelletier
Osteoarthritis (OA) is an extremely common musculoskeletal disease. However, current guidelines are not well suited for diagnosing patients in the early stages of disease and do not discriminate patients for whom the disease might progress rapidly. The most important hurdle in OA management is identifying and classifying patients who will benefit most from treatment. Further efforts are needed in patient subgrouping and developing prediction models. Conventional statistical modelling approaches exist; however, these models are limited in the amount of information they can adequately process...
December 6, 2018: Nature Reviews. Rheumatology
https://www.readbyqxmd.com/read/30511518/-precision-screening-and-treatment-of-human-papilloma-virus-related-cervical-cancer
#3
Zheng Hu, Ding Ma
Cervical cancer is a complex disease caused by both genetic susceptibility and environmental factors. Inherited genomic variance, high-risk human papilloma virus (HPV) infection/integration, genome methylation and somatic mutation could all constitute one machine learning model, laying the ground for molecular classification and the precision medicine of cervical cancer. Therefore, for cervical screening, next generation sequencing (NGS)-based HPV DNA and other molecular tests as well as dynamic machine learning models would accurately predict patients with potential to develop the cancer, thereby reducing the burden of repeated screening...
February 25, 2018: Zhejiang da Xue Xue Bao. Yi Xue Ban, Journal of Zhejiang University. Medical Sciences
https://www.readbyqxmd.com/read/30510440/precision-pharmacotherapy-psychiatry-s-future-direction-in-preventing-diagnosing-and-treating-mental-disorders
#4
REVIEW
Andreas Menke
Mental disorders account for around one-third of disability worldwide and cause enormous personal and societal burden. Current pharmacotherapies and nonpharmacotherapies do help many patients, but there are still high rates of partial or no response, delayed effect, and unfavorable adverse effects. The current diagnostic taxonomy of mental disorders by the Diagnostic and Statistical Manual of Mental Disorders and the International Classification of Diseases relies on presenting signs and symptoms, but does not reflect evidence from neurobiological and behavioral systems...
2018: Pharmacogenomics and Personalized Medicine
https://www.readbyqxmd.com/read/30472716/machine-learning-and-imaging-informatics-in-oncology
#5
REVIEW
Huan-Hsin Tseng, Lise Wei, Sunan Cui, Yi Luo, Randall K Ten Haken, Issam El Naqa
In the era of personalized and precision medicine, informatics technologies utilizing machine learning (ML) and quantitative imaging are witnessing a rapidly increasing role in medicine in general and in oncology in particular. This expanding role ranges from computer-aided diagnosis to decision support of treatments with the potential to transform the current landscape of cancer management. In this review, we aim to provide an overview of ML methodologies and imaging informatics techniques and their recent application in modern oncology...
November 23, 2018: Oncology
https://www.readbyqxmd.com/read/30472499/human-skeletal-muscle-cell-atlas-unraveling-cellular-secrets-utilizing-muscle-on-a-chip-differential-expansion-microscopy-mass-spectrometry-nanothermometry-and-machine-learning
#6
Bhanu P Jena, Domenico L Gatti, Suzan Arslanturk, Sebastian Pernal, Douglas J Taatjes
The 'Human Cell Atlas' project has been launched to obtain a comprehensive understanding of all cell types, the fundamental living units that constitute the human body. This is a global partnership and effort involving experts from many disciplines, from computer science, engineering to medicine, and is supported by several private and public organizations, among them, the Chan Zuckerberg Foundation, the National Institutes of Health, and Google, that will greatly benefit humanity. Nearly 37 trillion cells of various shapes, sizes, and composition, are precisely organized to constitute the human body...
November 16, 2018: Micron: the International Research and Review Journal for Microscopy
https://www.readbyqxmd.com/read/30470933/precision-immunoprofiling-by-image-analysis-and-artificial-intelligence
#7
REVIEW
Viktor H Koelzer, Korsuk Sirinukunwattana, Jens Rittscher, Kirsten D Mertz
Clinical success of immunotherapy is driving the need for new prognostic and predictive assays to inform patient selection and stratification. This requirement can be met by a combination of computational pathology and artificial intelligence. Here, we critically assess computational approaches supporting the development of a standardized methodology in the assessment of immune-oncology biomarkers, such as PD-L1 and immune cell infiltrates. We examine immunoprofiling through spatial analysis of tumor-immune cell interactions and multiplexing technologies as a predictor of patient response to cancer treatment...
November 23, 2018: Virchows Archiv: An International Journal of Pathology
https://www.readbyqxmd.com/read/30417117/multi-faceted-computational-assessment-of-risk-and-progression-in-oligodendroglioma-implicates-notch-and-pi3k-pathways
#8
Sameer H Halani, Safoora Yousefi, Jose Velazquez Vega, Michael R Rossi, Zheng Zhao, Fatemeh Amrollahi, Chad A Holder, Amelia Baxter-Stoltzfus, Jennifer Eschbacher, Brent Griffith, Jeffrey J Olson, Tao Jiang, Joseph R Yates, Charles G Eberhart, Laila M Poisson, Lee A D Cooper, Daniel J Brat
Oligodendrogliomas are diffusely infiltrative gliomas defined by IDH -mutation and co-deletion of 1p/19q. They have highly variable clinical courses, with survivals ranging from 6 months to over 20 years, but little is known regarding the pathways involved with their progression or optimal markers for stratifying risk. We utilized machine-learning approaches with genomic data from The Cancer Genome Atlas to objectively identify molecular factors associated with clinical outcomes of oligodendroglioma and extended these findings to study signaling pathways implicated in oncogenesis and clinical endpoints associated with glioma progression...
2018: NPJ Precision Oncology
https://www.readbyqxmd.com/read/30401894/machine-learning-predicts-individual-cancer-patient-responses-to-therapeutic-drugs-with-high-accuracy
#9
Cai Huang, Evan A Clayton, Lilya V Matyunina, L DeEtte McDonald, Benedict B Benigno, Fredrik Vannberg, John F McDonald
Precision or personalized cancer medicine is a clinical approach that strives to customize therapies based upon the genomic profiles of individual patient tumors. Machine learning (ML) is a computational method particularly suited to the establishment of predictive models of drug response based on genomic profiles of targeted cells. We report here on the application of our previously established open-source support vector machine (SVM)-based algorithm to predict the responses of 175 individual cancer patients to a variety of standard-of-care chemotherapeutic drugs from the gene-expression profiles (RNA-seq or microarray) of individual patient tumors...
November 6, 2018: Scientific Reports
https://www.readbyqxmd.com/read/30376045/gene-ontology-concept-recognition-using-named-concept-understanding-the-various-presentations-of-the-gene-functions-in-biomedical-literature
#10
Chia-Jung Yang, Jung-Hsien Chiang
OBJECTIVE: A major challenge in precision medicine is the development of patient-specific genetic biomarkers or drug targets. The firsthand information of the genes associated with the pathologic pathways of interest is buried in the ocean of biomedical literature. Gene ontology concept recognition (GOCR) is a biomedical natural language processing task used to extract and normalize the mentions of gene ontology (GO), the controlled vocabulary for gene functions across many species, from biomedical text...
January 1, 2018: Database: the Journal of Biological Databases and Curation
https://www.readbyqxmd.com/read/30349060/predicting-the-need-for-a-reduced-drug-dose-at-first-prescription
#11
Adrien Coulet, Nigam H Shah, Maxime Wack, Mohammad B Chawki, Nicolas Jay, Michel Dumontier
Prescribing the right drug with the right dose is a central tenet of precision medicine. We examined the use of patients' prior Electronic Health Records to predict a reduction in drug dosage. We focus on drugs that interact with the P450 enzyme family, because their dosage is known to be sensitive and variable. We extracted diagnostic codes, conditions reported in clinical notes, and laboratory orders from Stanford's clinical data warehouse to construct cohorts of patients that either did or did not need a dose change...
October 22, 2018: Scientific Reports
https://www.readbyqxmd.com/read/30323170/uncovering-the-heterogeneity-and-temporal-complexity-of-neurodegenerative-diseases-with-subtype-and-stage-inference
#12
Alexandra L Young, Razvan V Marinescu, Neil P Oxtoby, Martina Bocchetta, Keir Yong, Nicholas C Firth, David M Cash, David L Thomas, Katrina M Dick, Jorge Cardoso, John van Swieten, Barbara Borroni, Daniela Galimberti, Mario Masellis, Maria Carmela Tartaglia, James B Rowe, Caroline Graff, Fabrizio Tagliavini, Giovanni B Frisoni, Robert Laforce, Elizabeth Finger, Alexandre de Mendonça, Sandro Sorbi, Jason D Warren, Sebastian Crutch, Nick C Fox, Sebastien Ourselin, Jonathan M Schott, Jonathan D Rohrer, Daniel C Alexander
The heterogeneity of neurodegenerative diseases is a key confound to disease understanding and treatment development, as study cohorts typically include multiple phenotypes on distinct disease trajectories. Here we introduce a machine-learning technique-Subtype and Stage Inference (SuStaIn)-able to uncover data-driven disease phenotypes with distinct temporal progression patterns, from widely available cross-sectional patient studies. Results from imaging studies in two neurodegenerative diseases reveal subgroups and their distinct trajectories of regional neurodegeneration...
October 15, 2018: Nature Communications
https://www.readbyqxmd.com/read/30321294/cross-sectional-whole-genome-sequencing-and-epidemiological-study-of-multidrug-resistant-mycobacterium-tuberculosis-in-china
#13
Hairong Huang, Nan Ding, Tingting Yang, Cuidan Li, Xinmiao Jia, Guirong Wang, Jun Zhong, Ju Zhang, Guanglu Jiang, Shuqi Wang, Zhaojing Zong, Wei Jing, Yongliang Zhao, Shaofa Xu, Fei Chen
Background: The increase in MDR-TB severely hampers TB prevention and control in China, a country with the second highest MDR-TB burden globally. The first nationwide drug-resistant TB surveillance program provides an opportunity to comprehensively investigate the epidemiological/drug-resistance characteristics, potential drug-resistance mutations, and effective population changes of Chinese MDR-TB. Methods: We sequenced 357 MDR strains from 4,600 representative TB-positive sputum samples collected from the survey (70 counties in 31 provinces)...
October 15, 2018: Clinical Infectious Diseases: An Official Publication of the Infectious Diseases Society of America
https://www.readbyqxmd.com/read/30298124/classification-of-pediatric-asthma-from-phenotype-discovery-to-clinical-practice
#14
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
#15
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
#16
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...
November 1, 2018: JAMA Psychiatry
https://www.readbyqxmd.com/read/30247662/radiomics-with-artificial-intelligence-for-precision-medicine-in-radiation-therapy
#17
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
#18
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...
January 1, 2019: NeuroImage
https://www.readbyqxmd.com/read/30231499/deep-learning-in-drug-discovery-and-medicine-scratching-the-surface
#19
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
#20
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
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