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https://www.readbyqxmd.com/read/28095769/pcm-sabre-a-platform-for-benchmarking-and-comparing-outcome-prediction-methods-in-precision-cancer-medicine
#1
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
#2
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
#3
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
#4
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
#5
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
#6
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
#7
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
https://www.readbyqxmd.com/read/27902695/text-mining-genotype-phenotype-relationships-from-biomedical-literature-for-database-curation-and-precision-medicine
#8
Ayush Singhal, Michael Simmons, Zhiyong Lu
The practice of precision medicine will ultimately require databases of genes and mutations for healthcare providers to reference in order to understand the clinical implications of each patient's genetic makeup. Although the highest quality databases require manual curation, text mining tools can facilitate the curation process, increasing accuracy, coverage, and productivity. However, to date there are no available text mining tools that offer high-accuracy performance for extracting such triplets from biomedical literature...
November 2016: PLoS Computational Biology
https://www.readbyqxmd.com/read/27901055/mediboost-a-patient-stratification-tool-for-interpretable-decision-making-in-the-era-of-precision-medicine
#9
Gilmer Valdes, José Marcio Luna, Eric Eaton, Charles B Simone, Lyle H Ungar, Timothy D Solberg
Machine learning algorithms that are both interpretable and accurate are essential in applications such as medicine where errors can have a dire consequence. Unfortunately, there is currently a tradeoff between accuracy and interpretability among state-of-the-art methods. Decision trees are interpretable and are therefore used extensively throughout medicine for stratifying patients. Current decision tree algorithms, however, are consistently outperformed in accuracy by other, less-interpretable machine learning models, such as ensemble methods...
November 30, 2016: Scientific Reports
https://www.readbyqxmd.com/read/27896973/patterns-in-biomedical-data-how-do-we-find-them
#10
Anna O Basile, Anurag Verma, Marta Byrska-Bishop, Sarah A Pendergrass, Christian Darabos, H Lester Kirchner
Given the exponential growth of biomedical data, researchers are faced with numerous challenges in extracting and interpreting information from these large, high-dimensional, incomplete, and often noisy data. To facilitate addressing this growing concern, the "Patterns in Biomedical Data-How do we find them?" session of the 2017 Pacific Symposium on Biocomputing (PSB) is devoted to exploring pattern recognition using data-driven approaches for biomedical and precision medicine applications. The papers selected for this session focus on novel machine learning techniques as well as applications of established methods to heterogeneous data...
2016: Pacific Symposium on Biocomputing
https://www.readbyqxmd.com/read/27896759/identification-and-clinical-translation-of-biomarker-signatures-statistical-considerations
#11
Emanuel Schwarz
Powerful machine learning tools exist to extract biological patterns for diagnosis or prediction from high-dimensional datasets. Simultaneous advances in high-throughput profiling technologies have led to a rapid acceleration of biomarker discovery investigations across all areas of medicine. However, the translation of biomarker signatures into clinically useful tools has thus far been difficult. In this chapter, several important considerations are discussed that influence such translation in the context of classifier design...
2017: Methods in Molecular Biology
https://www.readbyqxmd.com/read/27873411/training-physicians-for-the-real-world-of-medicine-administration-based-learning
#12
Jason Rosenstock, Garrett M Sparks
Tired of outdated teaching formats like case-based learning (CBL), problem-based learning (PBL) and team-based learning (TBL)? We wanted something fresh for our medical school, something that would prepare our graduates for the modern practice of medicine, something that would satisfy regulatory agencies and our deans. After doing an extensive needs assessment, which we ignored, we decided to replace basic science in our curriculum with something more practical: administration-based learning (ABL). We taught students how to fix fax machines, how to deal with angry team members, and how to maximise revenue in private practice - lessons that were well received and were more consistent with what physicians really need to learn to be effective practitioners...
December 2016: Medical Education
https://www.readbyqxmd.com/read/27870246/materials-informatics-statistical-modeling-in-material-science
#13
REVIEW
Abraham Yosipof, Klimentiy Shimanovich, Hanoch Senderowitz
Material informatics is engaged with the application of informatic principles to materials science in order to assist in the discovery and development of new materials. Central to the field is the application of data mining techniques and in particular machine learning approaches, often referred to as Quantitative Structure Activity Relationship (QSAR) modeling, to derive predictive models for a variety of materials-related "activities". Such models can accelerate the development of new materials with favorable properties and provide insight into the factors governing these properties...
December 2016: Molecular Informatics
https://www.readbyqxmd.com/read/27848006/clinical-fracture-risk-evaluated-by-hierarchical-agglomerative-clustering
#14
C Kruse, P Eiken, P Vestergaard
: Clustering analysis can identify subgroups of patients based on similarities of traits. From data on 10,775 subjects, we document nine patient clusters of different fracture risks. Differences emerged after age 60 and treatment compliance differed by hip and lumbar spine bone mineral density profiles. INTRODUCTION: The purposes of this study were to establish and quantify patient clusters of high, average and low fracture risk using an unsupervised machine learning algorithm...
November 16, 2016: Osteoporosis International
https://www.readbyqxmd.com/read/27826573/a-gentle-introduction-to-artificial-neural-networks
#15
EDITORIAL
Zhongheng Zhang
Artificial neural network (ANN) is a flexible and powerful machine learning technique. However, it is under utilized in clinical medicine because of its technical challenges. The article introduces some basic ideas behind ANN and shows how to build ANN using R in a step-by-step framework. In topology and function, ANN is in analogue to the human brain. There are input and output signals transmitting from input to output nodes. Input signals are weighted before reaching output nodes according to their respective importance...
October 2016: Annals of Translational Medicine
https://www.readbyqxmd.com/read/27819294/big-genomics-and-clinical-data-analytics-strategies-for-precision-cancer-prognosis
#16
Ghim Siong Ow, Vladimir A Kuznetsov
The field of personalized and precise medicine in the era of big data analytics is growing rapidly. Previously, we proposed our model of patient classification termed Prognostic Signature Vector Matching (PSVM) and identified a 37 variable signature comprising 36 let-7b associated prognostic significant mRNAs and the age risk factor that stratified large high-grade serous ovarian cancer patient cohorts into three survival-significant risk groups. Here, we investigated the predictive performance of PSVM via optimization of the prognostic variable weights, which represent the relative importance of one prognostic variable over the others...
November 7, 2016: Scientific Reports
https://www.readbyqxmd.com/read/27798253/oxytocin-receptor-gene-variations-predict-neural-and-behavioral-response-to-oxytocin-in-autism
#17
Takamitsu Watanabe, Takeshi Otowa, Osamu Abe, Hitoshi Kuwabara, Yuta Aoki, Tatsunobu Natsubori, Hidemasa Takao, Chihiro Kakiuchi, Kenji Kondo, Masashi Ikeda, Nakao Iwata, Kiyoto Kasai, Tsukasa Sasaki, Hidenori Yamasue
Oxytocin appears beneficial for autism spectrum disorder (ASD), and more than 20 single-nucleotide polymorphisms (SNPs) in oxytocin receptor (OXTR) are relevant to ASD. However, neither biological functions of OXTR SNPs in ASD nor critical OXTR SNPs that determine oxytocin's effects on ASD remains known. Here, using a machine-learning algorithm that was designed to evaluate collective effects of multiple SNPs and automatically identify most informative SNPs, we examined relationships between 27 representative OXTR SNPs and six types of behavioral/neural response to oxytocin in ASD individuals...
October 19, 2016: Social Cognitive and Affective Neuroscience
https://www.readbyqxmd.com/read/27784037/deconstructing-pretest-risk-enrichment-to-optimize-prediction-of-psychosis-in-individuals-at-clinical-high-risk
#18
Paolo Fusar-Poli, Grazia Rutigliano, Daniel Stahl, André Schmidt, Valentina Ramella-Cravaro, Shetty Hitesh, Philip McGuire
Importance: Pretest risk estimation is routinely used in clinical medicine to inform further diagnostic testing in individuals with suspected diseases. To our knowledge, the overall characteristics and specific determinants of pretest risk of psychosis onset in individuals undergoing clinical high risk (CHR) assessment are unknown. Objectives: To investigate the characteristics and determinants of pretest risk of psychosis onset in individuals undergoing CHR assessment and to develop and externally validate a pretest risk stratification model...
December 1, 2016: JAMA Psychiatry
https://www.readbyqxmd.com/read/27781485/big-data-in-radiation-therapy-challenges-and-opportunities
#19
Tim Lustberg, Johan van Soest, Arthur Jochems, Timo Deist, Yvonka van Wijk, Sean Walsh, Philippe Lambin, Andre Dekker
Data collected and generated by radiation oncology can be classified by the Volume, Variety, Velocity and Veracity (4Vs) of Big Data because they are spread across different care providers and not easily shared owing to patient privacy protection. The magnitude of the 4Vs is substantial in oncology, especially owing to imaging modalities and unclear data definitions. To create useful models ideally all data of all care providers are understood and learned from; however, this presents challenges in the guise of poor data quality, patient privacy concerns, geographical spread, interoperability and large volume...
January 2017: British Journal of Radiology
https://www.readbyqxmd.com/read/27766937/leveraging-graph-topology-and-semantic-context-for-pharmacovigilance-through-twitter-streams
#20
Ryan Eshleman, Rahul Singh
BACKGROUND: Adverse drug events (ADEs) constitute one of the leading causes of post-therapeutic death and their identification constitutes an important challenge of modern precision medicine. Unfortunately, the onset and effects of ADEs are often underreported complicating timely intervention. At over 500 million posts per day, Twitter is a commonly used social media platform. The ubiquity of day-to-day personal information exchange on Twitter makes it a promising target for data mining for ADE identification and intervention...
October 6, 2016: BMC Bioinformatics
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