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https://www.readbyqxmd.com/read/28916782/predicting-clinical-outcomes-from-large-scale-cancer-genomic-profiles-with-deep-survival-models
#1
Safoora Yousefi, Fatemeh Amrollahi, Mohamed Amgad, Chengliang Dong, Joshua E Lewis, Congzheng Song, David A Gutman, Sameer H Halani, Jose Enrique Velazquez Vega, Daniel J Brat, Lee A D Cooper
Translating the vast data generated by genomic platforms into accurate predictions of clinical outcomes is a fundamental challenge in genomic medicine. Many prediction methods face limitations in learning from the high-dimensional profiles generated by these platforms, and rely on experts to hand-select a small number of features for training prediction models. In this paper, we demonstrate how deep learning and Bayesian optimization methods that have been remarkably successful in general high-dimensional prediction tasks can be adapted to the problem of predicting cancer outcomes...
September 15, 2017: Scientific Reports
https://www.readbyqxmd.com/read/28915974/rapid-and-accurate-intraoperative-pathological-diagnosis-by-artificial-intelligence-with-deep-learning-technology
#2
Jing Zhang, Yanlin Song, Fan Xia, Chenjing Zhu, Yingying Zhang, Wenpeng Song, Jianguo Xu, Xuelei Ma
Frozen section is widely used for intraoperative pathological diagnosis (IOPD), which is essential for intraoperative decision making. However, frozen section suffers from some drawbacks, such as time consuming and high misdiagnosis rate. Recently, artificial intelligence (AI) with deep learning technology has shown bright future in medicine. We hypothesize that AI with deep learning technology could help IOPD, with a computer trained by a dataset of intraoperative lesion images. Evidences supporting our hypothesis included the successful use of AI with deep learning technology in diagnosing skin cancer, and the developed method of deep-learning algorithm...
September 2017: Medical Hypotheses
https://www.readbyqxmd.com/read/28902409/binswanger-s-disease-biomarkers-in-the-inflammatory-form-of-vascular-cognitive-impairment-and-dementia
#3
REVIEW
Gary A Rosenberg
Vascular cognitive impairment and dementia (VCID) is a major public health concern because of the increased incidence of vascular disease in the aging population and the impact of vascular disease on Alzheimer's disease. VCID is a heterogeneous group of diseases for which there are no proven treatments. Biomarkers can be used to select more homogeneous populations. Small vessel disease is the most prevalent form of VCID and is the optimal form for treatment trials because there is a progressive course with characteristic pathological changes...
September 13, 2017: Journal of Neurochemistry
https://www.readbyqxmd.com/read/28899847/enhancing-seasonal-influenza-surveillance-topic-analysis-of-widely-used-medicinal-drugs-using-twitter-data
#4
Ireneus Kagashe, Zhijun Yan, Imran Suheryani
BACKGROUND: Uptake of medicinal drugs (preventive or treatment) is among the approaches used to control disease outbreaks, and therefore, it is of vital importance to be aware of the counts or frequencies of most commonly used drugs and trending topics about these drugs from consumers for successful implementation of control measures. Traditional survey methods would have accomplished this study, but they are too costly in terms of resources needed, and they are subject to social desirability bias for topics discovery...
September 12, 2017: Journal of Medical Internet Research
https://www.readbyqxmd.com/read/28884186/correction-an-integrated-anti-arrhythmic-target-network-of-compound-chinese-medicine-wenxin-keli-revealed-by-combined-machine-learning-and-molecular-pathway-analysis
#5
Taiyi Wang, Ming Lu, Qunqun Du, Xi Yao, Peng Zhang, Xiaonan Chen, Weiwei Xie, Zheng Li, Yuling Ma, Yan Zhu
Correction for 'An integrated anti-arrhythmic target network of a Chinese medicine compound, Wenxin Keli, revealed by combined machine learning and molecular pathway analysis' by Taiyi Wang et al., Mol. BioSyst., 2017, 13, 1018-1030.
September 8, 2017: Molecular BioSystems
https://www.readbyqxmd.com/read/28873754/combination-of-mass-spectrometry-based-targeted-lipidomics-and-supervised-machine-learning-algorithms-in-detecting-adulterated-admixtures-of-white-rice
#6
Dong Kyu Lim, Nguyen Phuoc Long, Changyeun Mo, Ziyuan Dong, Lingmei Cui, Giyoung Kim, Sung Won Kwon
The mixing of extraneous ingredients with original products is a common adulteration practice in food and herbal medicines. In particular, authenticity of white rice and its corresponding blended products has become a key issue in food industry. Accordingly, our current study aimed to develop and evaluate a novel discrimination method by combining targeted lipidomics with powerful supervised learning methods, and eventually introduce a platform to verify the authenticity of white rice. A total of 30 cultivars were collected, and 330 representative samples of white rice from Korea and China as well as seven mixing ratios were examined...
October 2017: Food Research International
https://www.readbyqxmd.com/read/28864056/classification-of-patients-with-sepsis-according-to-blood-genomic-endotype-a-prospective-cohort-study
#7
Brendon P Scicluna, Lonneke A van Vught, Aeilko H Zwinderman, Maryse A Wiewel, Emma E Davenport, Katie L Burnham, Peter N├╝rnberg, Marcus J Schultz, Janneke Horn, Olaf L Cremer, Marc J Bonten, Charles J Hinds, Hector R Wong, Julian C Knight, Tom van der Poll
BACKGROUND: Host responses during sepsis are highly heterogeneous, which hampers the identification of patients at high risk of mortality and their selection for targeted therapies. In this study, we aimed to identify biologically relevant molecular endotypes in patients with sepsis. METHODS: This was a prospective observational cohort study that included consecutive patients admitted for sepsis to two intensive care units (ICUs) in the Netherlands between Jan 1, 2011, and July 20, 2012 (discovery and first validation cohorts) and patients admitted with sepsis due to community-acquired pneumonia to 29 ICUs in the UK (second validation cohort)...
August 29, 2017: Lancet Respiratory Medicine
https://www.readbyqxmd.com/read/28851378/in-silico-prediction-of-novel-therapeutic-targets-using-gene-disease-association-data
#8
Enrico Ferrero, Ian Dunham, Philippe Sanseau
BACKGROUND: Target identification and validation is a pressing challenge in the pharmaceutical industry, with many of the programmes that fail for efficacy reasons showing poor association between the drug target and the disease. Computational prediction of successful targets could have a considerable impact on attrition rates in the drug discovery pipeline by significantly reducing the initial search space. Here, we explore whether gene-disease association data from the Open Targets platform is sufficient to predict therapeutic targets that are actively being pursued by pharmaceutical companies or are already on the market...
August 29, 2017: Journal of Translational Medicine
https://www.readbyqxmd.com/read/28841747/-psychotherapy-quo-vadis
#9
Gunther Meinlschmidt, Marion Tegethoff
Background: The science and practice of psychotherapy is continuously developing. The goal of this article is to describe new impulses, guiding current advancements in the field. Methods: This paper provides a selective narrative review, synthesizing and condensing relevant literature identified through various sources, including MEDLINE, EMBASE, PsycINFO, and "Web of Science", as well as citation tracking, to elaborate key developments in the field of psychotherapy Results: We describe several dynamics: 1) Following up the so-called "third wave of cognitive behavioral therapy", new interventions arise that have at their core fostering interpersonal virtues, such as compassion, forgiveness, and gratitude; 2) Based on technological quantum leaps, new interventions arise that exploit current developments in the field of new media, information, and communication technologies, as well as brain imaging, such as digital interventions for mental disorders and new forms of neurofeedback; 3) Inspired by the field of positive psychology, there is a revival of the promotion of strength and resilience in therapeutic contexts; 4) In light of the new paradigm "precision medicine", the issue of differential and adaptive indication of psychotherapy, addressed with new methods, regains relevance and drives a new field of "precision psychotherapy"...
August 2017: Fortschritte der Neurologie-Psychiatrie
https://www.readbyqxmd.com/read/28832506/determination-and-visualization-of-peimine-and-peiminine-content-in-fritillaria-thunbergii-bulbi-treated-by-sulfur-fumigation-using-hyperspectral-imaging-with-chemometrics
#10
Juan He, Yong He, And Chu Zhang
Rapid, non-destructive, and accurate quantitative determination of the effective components in traditional Chinese medicine (TCM) is required by industries, planters, and regulators. In this study, near-infrared hyperspectral imaging was applied for determining the peimine and peiminine content in Fritillaria thunbergii bulbi under sulfur fumigation. Spectral data were extracted from the hyperspectral images. High-performance liquid chromatography (HPLC) was conducted to determine the reference peimine and peiminine content...
August 23, 2017: Molecules: a Journal of Synthetic Chemistry and Natural Product Chemistry
https://www.readbyqxmd.com/read/28812013/intelligent-techniques-using-molecular-data-analysis-in-leukaemia-an-opportunity-for-personalized-medicine-support-system
#11
REVIEW
Haneen Banjar, David Adelson, Fred Brown, Naeem Chaudhri
The use of intelligent techniques in medicine has brought a ray of hope in terms of treating leukaemia patients. Personalized treatment uses patient's genetic profile to select a mode of treatment. This process makes use of molecular technology and machine learning, to determine the most suitable approach to treating a leukaemia patient. Until now, no reviews have been published from a computational perspective concerning the development of personalized medicine intelligent techniques for leukaemia patients using molecular data analysis...
2017: BioMed Research International
https://www.readbyqxmd.com/read/28758431/prodige-prediction-models-in-prostate-cancer-for-personalized-medicine-challenge
#12
A R Alitto, R Gatta, Bgl Vanneste, M Vallati, E Meldolesi, A Damiani, V Lanzotti, G C Mattiucci, V Frascino, C Masciocchi, F Catucci, A Dekker, P Lambin, V Valentini, G Mantini
AIM: Identifying the best care for a patient can be extremely challenging. To support the creation of multifactorial Decision Support Systems (DSSs), we propose an Umbrella Protocol, focusing on prostate cancer. MATERIALS & METHODS: The PRODIGE project consisted of a workflow for standardizing data, and procedures, to create a consistent dataset useful to elaborate DSSs. Techniques from classical statistics and machine learning will be adopted. The general protocol accepted by our Ethical Committee can be downloaded from cancerdata...
July 31, 2017: Future Oncology
https://www.readbyqxmd.com/read/28757882/desktop-genetics
#13
EDITORIAL
Soren H Hough, Ayokunmi Ajetunmobi, Leigh Brody, Neil Humphryes-Kirilov, Edward Perello
Desktop Genetics is a bioinformatics company building a gene-editing platform for personalized medicine. The company works with scientists around the world to design and execute state-of-the-art clustered regularly interspaced short palindromic repeats (CRISPR) experiments. Desktop Genetics feeds the lessons learned about experimental intent, single-guide RNA design and data from international genomics projects into a novel CRISPR artificial intelligence system. We believe that machine learning techniques can transform this information into a cognitive therapeutic development tool that will revolutionize medicine...
November 2016: Personalized Medicine
https://www.readbyqxmd.com/read/28756159/automatic-identification-of-high-impact-articles-in-pubmed-to-support-clinical-decision-making
#14
Jiantao Bian, Mohammad Amin Morid, Siddhartha Jonnalagadda, Gang Luo, Guilherme Del Fiol
OBJECTIVES: The practice of evidence-based medicine involves integrating the latest best available evidence into patient care decisions. Yet, critical barriers exist for clinicians' retrieval of evidence that is relevant for a particular patient from primary sources such as randomized controlled trials and meta-analyses. To help address those barriers, we investigated machine learning algorithms that find clinical studies with high clinical impact from PubMed®. METHODS: Our machine learning algorithms use a variety of features including bibliometric features (e...
September 2017: Journal of Biomedical Informatics
https://www.readbyqxmd.com/read/28750905/automated-problem-list-generation-and-physicians-perspective-from-a-pilot-study
#15
Murthy V Devarakonda, Neil Mehta, Ching-Huei Tsou, Jennifer J Liang, Amy S Nowacki, John Eric Jelovsek
OBJECTIVE: An accurate, comprehensive and up-to-date problem list can help clinicians provide patient-centered care. Unfortunately, problem lists created and maintained in electronic health records by providers tend to be inaccurate, duplicative and out of date. With advances in machine learning and natural language processing, it is possible to automatically generate a problem list from the data in the EHR and keep it current. In this paper, we describe an automated problem list generation method and report on insights from a pilot study of physicians' assessment of the generated problem lists compared to existing providers-curated problem lists in an institution's EHR system...
September 2017: International Journal of Medical Informatics
https://www.readbyqxmd.com/read/28728997/sepsis-reconsidered-identifying-novel-metrics-for-behavioral-landscape-characterization-with-a-high-performance-computing-implementation-of-an-agent-based-model
#16
Chase Cockrell, Gary An
OBJECTIVES: Sepsis affects nearly 1 million people in the United States per year, has a mortality rate of 28-50% and requires more than $20 billion a year in hospital costs. Over a quarter century of research has not yielded a single reliable diagnostic test or a directed therapeutic agent for sepsis. Central to this insufficiency is the fact that sepsis remains a clinical/physiological diagnosis representing a multitude of molecularly heterogeneous pathological trajectories. Advances in computational capabilities offered by High Performance Computing (HPC) platforms call for an evolution in the investigation of sepsis to attempt to define the boundaries of traditional research (bench, clinical and computational) through the use of computational proxy models...
July 18, 2017: Journal of Theoretical Biology
https://www.readbyqxmd.com/read/28727867/unintended-consequences-of-machine-learning-in-medicine
#17
Federico Cabitza, Raffaele Rasoini, Gian Franco Gensini
No abstract text is available yet for this article.
August 8, 2017: JAMA: the Journal of the American Medical Association
https://www.readbyqxmd.com/read/28715343/deep-belief-networks-for-electroencephalography-a-review-of-recent-contributions-and-future-outlooks
#18
Faezeh Movahedi, James L Coyle, Ervin Sejdic
Deep learning, a relatively new branch of machine learning, has been investigated for use in a variety of biomedical applications. Deep learning algorithms have been used to analyze different physiological signals and gain a better understanding of human physiology for automated diagnosis of abnormal conditions. In this manuscript, we provide an overview of deep learning approaches with a focus on deep belief networks in electroencephalography applications. We investigate the state of- the-art algorithms for deep belief networks and then cover the application of these algorithms and their performances in electroencephalographic applications...
July 14, 2017: IEEE Journal of Biomedical and Health Informatics
https://www.readbyqxmd.com/read/28715190/chemical-topic-modeling-exploring-molecular-data-sets-using-a-common-text-mining-approach
#19
Nadine Schneider, Nikolas Fechner, Gregory A Landrum, Nikolaus Stiefl
Big data is one of the key transformative factors which increasingly influences all aspects of modern life. Although this transformation brings vast opportunities it also generates novel challenges, not the least of which is organizing and searching this data deluge. The field of medicinal chemistry is not different: more and more data are being generated, for instance, by technologies such as DNA encoded libraries, peptide libraries, text mining of large literature corpora, and new in silico enumeration methods...
August 9, 2017: Journal of Chemical Information and Modeling
https://www.readbyqxmd.com/read/28713439/predicting-future-biomass-yield-in-miscanthus-using-the-carbohydrate-metabolic-profile-as-a-biomarker
#20
Anne L Maddison, Anyela Camargo-Rodriguez, Ian M Scott, Charlotte M Jones, Dafydd M O Elias, Sarah Hawkins, Alice Massey, John Clifton-Brown, Niall P McNamara, Iain S Donnison, Sarah J Purdy
In perennial energy crop breeding programmes, it can take several years before a mature yield is reached when potential new varieties can be scored. Modern plant breeding technologies have focussed on molecular markers, but for many crop species, this technology is unavailable. Therefore, prematurity predictors of harvestable yield would accelerate the release of new varieties. Metabolic biomarkers are routinely used in medicine, but they have been largely overlooked as predictive tools in plant science. We aimed to identify biomarkers of productivity in the bioenergy crop, Miscanthus, that could be used prognostically to predict future yields...
July 2017: Global Change Biology. Bioenergy
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