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https://www.readbyqxmd.com/read/28227016/towards-sophisticated-learning-from-ehrs-increasing-prediction-specificity-and-accuracy-using-clinically-meaningful-risk-criteria
#1
Ieva Vasiljeva, Ognjen Arandjelovic, Ieva Vasiljeva, Ognjen Arandjelovic, Ieva Vasiljeva, Ognjen Arandjelovic
Computer based analysis of Electronic Health Records (EHRs) has the potential to provide major novel insights of benefit both to specific individuals in the context of personalized medicine, as well as on the level of population-wide health care and policy. The present paper introduces a novel algorithm that uses machine learning for the discovery of longitudinal patterns in the diagnoses of diseases. Two key technical novelties are introduced: one in the form of a novel learning paradigm which enables greater learning specificity, and another in the form of a risk driven identification of confounding diagnoses...
August 2016: Conference Proceedings: Annual International Conference of the IEEE Engineering in Medicine and Biology Society
https://www.readbyqxmd.com/read/28222333/immunoprofiling-as-a-predictor-of-patient-s-response-to-cancer-therapy-promises-and-challenges
#2
REVIEW
Daniel Bethmann, Zipei Feng, Bernard A Fox
Immune cell infiltration is common to many tumors and has been recognized by pathologists for more than 100 years. The application of digital imaging and objective assessment software allowed a concise determination of the type and quantity of immune cells and their location relative to the tumor and, in the case of colon cancer, characterized overall survival better than AJCC TNM staging. Subsequently, expression of PD-L1, by 50% or more tumor cells, identified NSCLC patients with double the response rate to anti-PD-1...
February 18, 2017: Current Opinion in Immunology
https://www.readbyqxmd.com/read/28176850/application-of-machine-learning-models-to-predict-tacrolimus-stable-dose-in-renal-transplant-recipients
#3
Jie Tang, Rong Liu, Yue-Li Zhang, Mou-Ze Liu, Yong-Fang Hu, Ming-Jie Shao, Li-Jun Zhu, Hua-Wen Xin, Gui-Wen Feng, Wen-Jun Shang, Xiang-Guang Meng, Li-Rong Zhang, Ying-Zi Ming, Wei Zhang
Tacrolimus has a narrow therapeutic window and considerable variability in clinical use. Our goal was to compare the performance of multiple linear regression (MLR) and eight machine learning techniques in pharmacogenetic algorithm-based prediction of tacrolimus stable dose (TSD) in a large Chinese cohort. A total of 1,045 renal transplant patients were recruited, 80% of which were randomly selected as the "derivation cohort" to develop dose-prediction algorithm, while the remaining 20% constituted the "validation cohort" to test the final selected algorithm...
February 8, 2017: Scientific Reports
https://www.readbyqxmd.com/read/28166733/a-machine-learning-classifier-trained-on-cancer-transcriptomes-detects-nf1-inactivation-signal-in-glioblastoma
#4
Gregory P Way, Robert J Allaway, Stephanie J Bouley, Camilo E Fadul, Yolanda Sanchez, Casey S Greene
BACKGROUND: We have identified molecules that exhibit synthetic lethality in cells with loss of the neurofibromin 1 (NF1) tumor suppressor gene. However, recognizing tumors that have inactivation of the NF1 tumor suppressor function is challenging because the loss may occur via mechanisms that do not involve mutation of the genomic locus. Degradation of the NF1 protein, independent of NF1 mutation status, phenocopies inactivating mutations to drive tumors in human glioma cell lines. NF1 inactivation may alter the transcriptional landscape of a tumor and allow a machine learning classifier to detect which tumors will benefit from synthetic lethal molecules...
February 6, 2017: BMC Genomics
https://www.readbyqxmd.com/read/28154050/beyond-prediction-using-big-data-for-policy-problems
#5
REVIEW
Susan Athey
Machine-learning prediction methods have been extremely productive in applications ranging from medicine to allocating fire and health inspectors in cities. However, there are a number of gaps between making a prediction and making a decision, and underlying assumptions need to be understood in order to optimize data-driven decision-making.
February 3, 2017: Science
https://www.readbyqxmd.com/read/28153954/hot-topics-will-machine-learning-change-medicine
#6
Michael Forsting
No abstract text is available yet for this article.
February 2, 2017: Journal of Nuclear Medicine: Official Publication, Society of Nuclear Medicine
https://www.readbyqxmd.com/read/28137222/machine-learning-and-molecular-dynamics-based-insights-into-mode-of-actions-of-insulin-degrading-enzyme-modulators
#7
Salma Jamal, Sukriti Goyal, Asheesh Shanker, Abhinav Grover
BACKGROUND: Alzheimer's disease (AD) is one of the most common lethal neurodegenerative disorders having impact on the lives of millions of people worldwide. The disease lacks effective treatment options and the unavailability of the drugs to cure the disease necessitates the development of effectual anti-Alzheimer drugs. Several mechanisms have been reported underlying the association of the two disorders, diabetes and dementia, one among which is the insulin-degrading enzyme (IDE) which is known to degrade insulin as well beta-amyloid peptides...
January 30, 2017: Combinatorial Chemistry & High Throughput Screening
https://www.readbyqxmd.com/read/28128933/computational-sensing-using-low-cost-and-mobile-plasmonic-readers-designed-by-machine-learning
#8
Zachary S Ballard, Daniel Shir, Aashish Bhardwaj, Sarah Bazargan, Shyama Sathianathan, Aydogan Ozcan
Plasmonic sensors have been used for a wide range of biological and chemical sensing applications. Emerging nanofabrication techniques have enabled these sensors to be cost-effectively mass manufactured onto various types of substrates. To accompany these advances, major improvements in sensor read-out devices must also be achieved to fully realize the broad impact of plasmonic nanosensors. Here, we propose a machine learning framework which can be used to design low-cost and mobile multispectral plasmonic readers that do not use traditionally employed bulky and expensive stabilized light sources or high-resolution spectrometers...
February 1, 2017: ACS Nano
https://www.readbyqxmd.com/read/28128301/discovering-novel-phenotypes-with-automatically-inferred-dynamic-models-a-partial-melanocyte-conversion-in-xenopus
#9
Daniel Lobo, Maria Lobikin, Michael Levin
Progress in regenerative medicine requires reverse-engineering cellular control networks to infer perturbations with desired systems-level outcomes. Such dynamic models allow phenotypic predictions for novel perturbations to be rapidly assessed in silico. Here, we analyzed a Xenopus model of conversion of melanocytes to a metastatic-like phenotype only previously observed in an all-or-none manner. Prior in vivo genetic and pharmacological experiments showed that individual animals either fully convert or remain normal, at some characteristic frequency after a given perturbation...
January 27, 2017: Scientific Reports
https://www.readbyqxmd.com/read/28127429/machine-learning-and-systems-genomics-approaches-for-multi-omics-data
#10
REVIEW
Eugene Lin, Hsien-Yuan Lane
In light of recent advances in biomedical computing, big data science, and precision medicine, there is a mammoth demand for establishing algorithms in machine learning and systems genomics (MLSG), together with multi-omics data, to weigh probable phenotype-genotype relationships. Software frameworks in MLSG are extensively employed to analyze hundreds of thousands of multi-omics data by high-throughput technologies. In this study, we reviewed the MLSG software frameworks and future directions with respect to multi-omics data analysis and integration...
2017: Biomarker Research
https://www.readbyqxmd.com/read/28126242/artificial-intelligence-in-medicine
#11
Pavel Hamet, Johanne Tremblay
Artificial Intelligence (AI) is a general term that implies the use of a computer to model intelligent behavior with minimal human intervention. AI is generally accepted as having started with the invention of robots. The term derives from the Czech word robota, meaning biosynthetic machines used as forced labor. In this field, Leonardo Da Vinci's lasting heritage is today's burgeoning use of robotic-assisted surgery, named after him, for complex urologic and gynecologic procedures. Da Vinci's sketchbooks of robots helped set the stage for this innovation...
January 11, 2017: Metabolism: Clinical and Experimental
https://www.readbyqxmd.com/read/28125523/machine-learning-based-classification-of-38-years-of-spine-related-literature-into-100-research-topics
#12
David C Sing, Lionel N Metz, Stefan Dudli
STUDY DESIGN: Retrospective review. OBJECTIVE: To identify the top 100 spine research topics. SUMMARY OF BACKGROUND DATA: Recent advances in "machine learning," or computers learning without explicit instructions, have yielded broad technological advances. Topic modeling algorithms can be applied to large volumes of text to discover quantifiable themes and trends. METHODS: Abstracts were extracted from the National Library of Medicine PubMed database from five prominent peer-reviewed spine journals (European Spine Journal [ESJ], The Spine Journal [SpineJ], Spine, Journal of Spinal Disorders and Techniques [JSDT], Journal of Neurosurgery: Spine [JNS])...
January 25, 2017: Spine
https://www.readbyqxmd.com/read/28116551/tensor-factorization-for-precision-medicine-in-heart-failure-with-preserved-ejection-fraction
#13
Yuan Luo, Faraz S Ahmad, Sanjiv J Shah
Heart failure with preserved ejection fraction (HFpEF) is a heterogeneous clinical syndrome that may benefit from improved subtyping in order to better characterize its pathophysiology and to develop novel targeted therapies. The United States Precision Medicine Initiative comes amid the rapid growth in quantity and modality of clinical data for HFpEF patients ranging from deep phenotypic to trans-omic data. Tensor factorization, a form of machine learning, allows for the integration of multiple data modalities to derive clinically relevant HFpEF subtypes that may have significant differences in underlying pathophysiology and differential response to therapies...
January 23, 2017: Journal of Cardiovascular Translational Research
https://www.readbyqxmd.com/read/28095769/pcm-sabre-a-platform-for-benchmarking-and-comparing-outcome-prediction-methods-in-precision-cancer-medicine
#14
Noah Eyal-Altman, Mark Last, Eitan Rubin
BACKGROUND: Numerous publications attempt to predict cancer survival outcome from gene expression data using machine-learning methods. A direct comparison of these works is challenging for the following reasons: (1) inconsistent measures used to evaluate the performance of different models, and (2) incomplete specification of critical stages in the process of knowledge discovery. There is a need for a platform that would allow researchers to replicate previous works and to test the impact of changes in the knowledge discovery process on the accuracy of the induced models...
January 17, 2017: BMC Bioinformatics
https://www.readbyqxmd.com/read/28067293/drug-response-prediction-as-a-link-prediction-problem
#15
Zachary Stanfield, Mustafa Coşkun, Mehmet Koyutürk
Drug response prediction is a well-studied problem in which the molecular profile of a given sample is used to predict the effect of a given drug on that sample. Effective solutions to this problem hold the key for precision medicine. In cancer research, genomic data from cell lines are often utilized as features to develop machine learning models predictive of drug response. Molecular networks provide a functional context for the integration of genomic features, thereby resulting in robust and reproducible predictive models...
January 9, 2017: Scientific Reports
https://www.readbyqxmd.com/read/28064045/a-l1-regularized-feature-selection-method-for-local-dimension-reduction-on-microarray-data
#16
Shun Guo, Donghui Guo, Lifei Chen, Qingshan Jiang
Dimension reduction is a crucial technique in machine learning and data mining, which is widely used in areas of medicine, bioinformatics and genetics. In this paper, we propose a two-stage local dimension reduction approach for classification on microarray data. In first stage, a new L1-regularized feature selection method is defined to remove irrelevant and redundant features and to select the important features (biomarkers). In the next stage, PLS-based feature extraction is implemented on the selected features to extract synthesis features that best reflect discriminating characteristics for classification...
December 31, 2016: Computational Biology and Chemistry
https://www.readbyqxmd.com/read/28048357/su-f-r-05-multidimensional-imaging-radiomics-geodesics-a-novel-manifold-learning-based-automatic-feature-extraction-method-for-diagnostic-prediction-in-multiparametric-imaging
#17
V Parekh, M A Jacobs
PURPOSE: Multiparametric radiological imaging is used for diagnosis in patients. Potentially extracting useful features specific to a patient's pathology would be crucial step towards personalized medicine and assessing treatment options. In order to automatically extract features directly from multiparametric radiological imaging datasets, we developed an advanced unsupervised machine learning algorithm called the multidimensional imaging radiomics-geodesics(MIRaGe). METHODS: Seventy-six breast tumor patients underwent 3T MRI breast imaging were used for this study...
June 2016: Medical Physics
https://www.readbyqxmd.com/read/28035540/prediction-of-cold-and-heat-patterns-using-anthropometric-measures-based-on-machine-learning
#18
Bum Ju Lee, Jae Chul Lee, Jiho Nam, Jong Yeol Kim
OBJECTIVE: To examine the association of body shape with cold and heat patterns, to determine which anthropometric measure is the best indicator for discriminating between the two patterns, and to investigate whether using a combination of measures can improve the predictive power to diagnose these patterns. METHODS: Based on a total of 4,859 subjects (3,000 women and 1,859 men), statistical analyses using binary logistic regression were performed to assess the signifificance of the difference and the predictive power of each anthropometric measure, and binary logistic regression and Naive Bayes with the variable selection technique were used to assess the improvement in the predictive power of the patterns using the combined measures...
December 29, 2016: Chinese Journal of Integrative Medicine
https://www.readbyqxmd.com/read/28019017/intelligent-and-automatic-in-vivo-detection-and-quantification-of-transplanted-cells-in-mri
#19
Muhammad Jamal Afridi, Arun Ross, Xiaoming Liu, Margaret F Bennewitz, Dorela D Shuboni, Erik M Shapiro
PURPOSE: Magnetic resonance imaging (MRI)-based cell tracking has emerged as a useful tool for identifying the location of transplanted cells, and even their migration. Magnetically labeled cells appear as dark contrast in T2*-weighted MRI, with sensitivities of individual cells. One key hurdle to the widespread use of MRI-based cell tracking is the inability to determine the number of transplanted cells based on this contrast feature. In the case of single cell detection, manual enumeration of spots in three-dimensional (3D) MRI in principle is possible; however, it is a tedious and time-consuming task that is prone to subjectivity and inaccuracy on a large scale...
December 26, 2016: Magnetic Resonance in Medicine: Official Journal of the Society of Magnetic Resonance in Medicine
https://www.readbyqxmd.com/read/28011145/a-functional-genomic-meta-analysis-of-clinical-trials-in-systemic-sclerosis-towards-precision-medicine-and-combination-therapy
#20
Jaclyn N Taroni, Viktor Martyanov, J Matthew Mahoney, Michael L Whitfield
Systemic sclerosis (SSc) is an orphan, systemic autoimmune disease with no FDA-approved treatments. Its heterogeneity and rarity often result in underpowered clinical trials making the analysis and interpretation of associated molecular data challenging. We performed a meta-analysis of gene expression data from skin biopsies of SSc patients treated with five therapies: mycophenolate mofetil (MMF), rituximab, abatacept, nilotinib, and fresolimumab. A common clinical improvement criterion of -20% OR -5 modified Rodnan Skin Score was applied to each study...
December 20, 2016: Journal of Investigative Dermatology
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