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https://www.readbyqxmd.com/read/29440404/multivariate-resting-state-functional-connectivity-predicts-response-to-cognitive-behavioral-therapy-in-obsessive-compulsive-disorder
#1
Nicco Reggente, Teena D Moody, Francesca Morfini, Courtney Sheen, Jesse Rissman, Joseph O'Neill, Jamie D Feusner
Cognitive behavioral therapy (CBT) is an effective treatment for many with obsessive-compulsive disorder (OCD). However, response varies considerably among individuals. Attaining a means to predict an individual's potential response would permit clinicians to more prudently allocate resources for this often stressful and time-consuming treatment. We collected resting-state functional magnetic resonance imaging from adults with OCD before and after 4 weeks of intensive daily CBT. We leveraged machine learning with cross-validation to assess the power of functional connectivity (FC) patterns to predict individual posttreatment OCD symptom severity...
February 12, 2018: Proceedings of the National Academy of Sciences of the United States of America
https://www.readbyqxmd.com/read/29438494/artificial-intelligence-in-drug-combination-therapy
#2
Igor F Tsigelny
Currently, the development of medicines for complex diseases requires the development of combination drug therapies. It is necessary because in many cases, one drug cannot target all necessary points of intervention. For example, in cancer therapy, a physician often meets a patient having a genomic profile including more than five molecular aberrations. Drug combination therapy has been an area of interest for a while, for example the classical work of Loewe devoted to the synergism of drugs was published in 1928-and it is still used in calculations for optimal drug combinations...
February 9, 2018: Briefings in Bioinformatics
https://www.readbyqxmd.com/read/29410461/in-silico-clinical-trials-for-pediatric-orphan-diseases
#3
A Carlier, A Vasilevich, M Marechal, J de Boer, L Geris
To date poor treatment options are available for patients with congenital pseudarthrosis of the tibia (CPT), a pediatric orphan disease. In this study we have performed an in silico clinical trial on 200 virtual subjects, generated from a previously established model of murine bone regeneration, to tackle the challenges associated with the small, pediatric patient population. Each virtual subject was simulated to receive no treatment and bone morphogenetic protein (BMP) treatment. We have shown that the degree of severity of CPT is significantly reduced with BMP treatment, although the effect is highly subject-specific...
February 6, 2018: Scientific Reports
https://www.readbyqxmd.com/read/29409442/semantic-annotation-of-consumer-health-questions
#4
Halil Kilicoglu, Asma Ben Abacha, Yassine Mrabet, Sonya E Shooshan, Laritza Rodriguez, Kate Masterton, Dina Demner-Fushman
BACKGROUND: Consumers increasingly use online resources for their health information needs. While current search engines can address these needs to some extent, they generally do not take into account that most health information needs are complex and can only fully be expressed in natural language. Consumer health question answering (QA) systems aim to fill this gap. A major challenge in developing consumer health QA systems is extracting relevant semantic content from the natural language questions (question understanding)...
February 6, 2018: BMC Bioinformatics
https://www.readbyqxmd.com/read/29398494/machine-learning-in-medical-imaging
#5
Maryellen L Giger
Advances in both imaging and computers have synergistically led to a rapid rise in the potential use of artificial intelligence in various radiological imaging tasks, such as risk assessment, detection, diagnosis, prognosis, and therapy response, as well as in multi-omics disease discovery. A brief overview of the field is given here, allowing the reader to recognize the terminology, the various subfields, and components of machine learning, as well as the clinical potential. Radiomics, an expansion of computer-aided diagnosis, has been defined as the conversion of images to minable data...
February 2, 2018: Journal of the American College of Radiology: JACR
https://www.readbyqxmd.com/read/29396321/conceptual-endophentoypes-a-strategy-to-advance-the-impact-of-psychoneuroendocrinology-in-precision-medicine
#6
REVIEW
Dirk Hellhammer, Gunther Meinlschmidt, Jens C Pruessner
Psychobiological research has generated a tremendous amount of findings on the psychological, neuroendocrine, molecular and environmental processes that are directly relevant for mental and physical health, but have overwhelmed our capacity to meaningfully absorb, integrate, and utilize this knowledge base. Here, we reflect about suitable strategies to improve the translational success of psychoneuroendocrinological research in the era of precision medicine. Following a strategy advocated by the National Research Council and the tradition of endophenotype-based research, we advance here a new approach, termed "conceptual endophenotypes"...
January 9, 2018: Psychoneuroendocrinology
https://www.readbyqxmd.com/read/29395627/challenges-and-promises-of-pet-radiomics
#7
Gary J R Cook, Gurdip Azad, Kasia Owczarczyk, Musib Siddique, Vicky Goh
PURPOSE: Radiomics describes the extraction of multiple, otherwise invisible, features from medical images that, with bioinformatic approaches, can be used to provide additional information that can predict underlying tumor biology and behavior. METHODS AND MATERIALS: Radiomic signatures can be used alone or with other patient-specific data to improve tumor phenotyping, treatment response prediction, and prognosis, noninvasively. The data describing 18F-fluorodeoxyglucose positron emission tomography radiomics, often using texture or heterogeneity parameters, are increasing rapidly...
January 30, 2018: International Journal of Radiation Oncology, Biology, Physics
https://www.readbyqxmd.com/read/29393678/on-the-virtues-of-automated-quantitative-structure-activity-relationship-the-new-kid-on-the-block
#8
Marcelo T de Oliveira, Edson Katekawa
Quantitative structure-activity relationship (QSAR) has proved to be an invaluable tool in medicinal chemistry. Data availability at unprecedented levels through various databases have collaborated to a resurgence in the interest for QSAR. In this context, rapid generation of quality predictive models is highly desirable for hit identification and lead optimization. We showcase the application of an automated QSAR approach, which randomly selects multiple training/test sets and utilizes machine-learning algorithms to generate predictive models...
February 1, 2018: Future Medicinal Chemistry
https://www.readbyqxmd.com/read/29391026/machine-learning-and-medicine-book-review-and-commentary
#9
Robert Koprowski, Kenneth R Foster
This article is a review of the book "Master machine learning algorithms, discover how they work and implement them from scratch" (ISBN: not available, 37 USD, 163 pages) edited by Jason Brownlee published by the Author, edition, v1.10 http://MachineLearningMastery.com . An accompanying commentary discusses some of the issues that are involved with use of machine learning and data mining techniques to develop predictive models for diagnosis or prognosis of disease, and to call attention to additional requirements for developing diagnostic and prognostic algorithms that are generally useful in medicine...
February 1, 2018: Biomedical Engineering Online
https://www.readbyqxmd.com/read/29388313/deep-learning-approaches-for-detection-and-removal-of-ghosting-artifacts-in-mr-spectroscopy
#10
Sreenath P Kyathanahally, André Döring, Roland Kreis
PURPOSE: To make use of deep learning (DL) methods to detect and remove ghosting artifacts in clinical magnetic resonance spectra of human brain. METHODS: Deep learning algorithms, including fully connected neural networks, deep-convolutional neural networks, and stacked what-where auto encoders, were implemented to detect and correct MR spectra containing spurious echo ghost signals. The DL methods were trained on a huge database of simulated spectra with and without ghosting artifacts that represent complex variations of ghost-ridden spectra, transformed to time-frequency spectrograms...
February 1, 2018: Magnetic Resonance in Medicine: Official Journal of the Society of Magnetic Resonance in Medicine
https://www.readbyqxmd.com/read/29373082/artificial-intelligence-physiological-genomics-and-precision-medicine
#11
Anna Marie Williams, Yong Liu, Kevin R Regner, Fabrice Jotterand, Pengyuan Liu, Mingyu Liang
Big data is a major driver in the development of precision medicine. Efficient analysis methods are needed to transform big data into clinically-actionable knowledge. To accomplish this, many researchers are turning towards machine learning (ML), an approach of artificial intelligence (AI) that utilizes modern algorithms to give computers the ability to learn. Much of the effort to advance ML for precision medicine has been focused on the development and implementation of algorithms and the generation of ever larger quantities of genomic sequence data and electronic health records...
January 26, 2018: Physiological Genomics
https://www.readbyqxmd.com/read/29368597/deep-learning-of-mutation-gene-drug-relations-from-the-literature
#12
Kyubum Lee, Byounggun Kim, Yonghwa Choi, Sunkyu Kim, Wonho Shin, Sunwon Lee, Sungjoon Park, Seongsoon Kim, Aik Choon Tan, Jaewoo Kang
BACKGROUND: Molecular biomarkers that can predict drug efficacy in cancer patients are crucial components for the advancement of precision medicine. However, identifying these molecular biomarkers remains a laborious and challenging task. Next-generation sequencing of patients and preclinical models have increasingly led to the identification of novel gene-mutation-drug relations, and these results have been reported and published in the scientific literature. RESULTS: Here, we present two new computational methods that utilize all the PubMed articles as domain specific background knowledge to assist in the extraction and curation of gene-mutation-drug relations from the literature...
January 25, 2018: BMC Bioinformatics
https://www.readbyqxmd.com/read/29352006/machine-learning-in-cardiovascular-medicine-are-we-there-yet
#13
REVIEW
Khader Shameer, Kipp W Johnson, Benjamin S Glicksberg, Joel T Dudley, Partho P Sengupta
Artificial intelligence (AI) broadly refers to analytical algorithms that iteratively learn from data, allowing computers to find hidden insights without being explicitly programmed where to look. These include a family of operations encompassing several terms like machine learning, cognitive learning, deep learning and reinforcement learning-based methods that can be used to integrate and interpret complex biomedical and healthcare data in scenarios where traditional statistical methods may not be able to perform...
January 19, 2018: Heart: Official Journal of the British Cardiac Society
https://www.readbyqxmd.com/read/29348728/systems-level-mechanisms-of-action-of-panax-ginseng-a-network-pharmacological-approach
#14
REVIEW
Sa-Yoon Park, Ji-Hun Park, Hyo-Su Kim, Choong-Yeol Lee, Hae-Jeung Lee, Ki Sung Kang, Chang-Eop Kim
Panax ginseng has been used since ancient times based on the traditional Asian medicine theory and clinical experiences, and currently, is one of the most popular herbs in the world. To date, most of the studies concerning P. ginseng have focused on specific mechanisms of action of individual constituents. However, in spite of many studies on the molecular mechanisms of P. ginseng, it still remains unclear how multiple active ingredients of P. ginseng interact with multiple targets simultaneously, giving the multidimensional effects on various conditions and diseases...
January 2018: Journal of Ginseng Research
https://www.readbyqxmd.com/read/29346031/2016-new-horizons-lecture-beyond-imaging-radiology-of-tomorrow
#15
Hedvig Hricak
This article is based on the New Horizons lecture delivered at the 2016 Radiological Society of North America Annual Meeting. It addresses looming changes for radiology, many of which stem from the disruptive effects of the Fourth Industrial Revolution. This is an emerging era of unprecedented rapid innovation marked by the integration of diverse disciplines and technologies, including data science, machine learning, and artificial intelligence-technologies that narrow the gap between man and machine. Technologic advances and the convergence of life sciences, physical sciences, and bioengineering are creating extraordinary opportunities in diagnostic radiology, image-guided therapy, targeted radionuclide therapy, and radiology informatics, including radiologic image analysis...
January 18, 2018: Radiology
https://www.readbyqxmd.com/read/29344895/bioinformatics-approaches-to-predict-drug-responses-from-genomic-sequencing
#16
Neel S Madhukar, Olivier Elemento
Fulfilling the promises of precision medicine will depend on our ability to create patient-specific treatment regimens. Therefore, being able to translate genomic sequencing into predicting how a patient will respond to a given drug is critical. In this chapter, we review common bioinformatics approaches that aim to use sequencing data to predict sample-specific drug susceptibility. First, we explain the importance of customized drug regimens to the future of medical care. Second, we discuss the different public databases and community efforts that can be leveraged to develop new methods for identifying new predictive biomarkers...
2018: Methods in Molecular Biology
https://www.readbyqxmd.com/read/29336344/-the-urologist-of-the-future-and-new-technologies
#17
Francois Peinado, Atanasio Fernández, Fernando Teba, Guillermo Celada, Marco Antonio Acosta
The last 25 years have brought about revolutionary changes for medicine and in particular for urology: internet was only in its infancy, medical records were written on paper, searches for medical information were done in the hospital library, medical articles were photocopied and our relationship with patients only existed face to face. Social networks had not yet appeared and even Google did not exist. Just imagine what might happen during the next 25 years, we're going to see even more radical changes. The urologist of the future is going to see the arrival of artificial intelligence, collaborative medicine, telemedicine, machine learning, the Internet of Things and personalized robotics; in the meantime, social media will continue to transform the interaction between physician and patient...
January 2018: Archivos Españoles de Urología
https://www.readbyqxmd.com/read/29327356/gene-specific-variant-classifier-dpyd-varifier-to-identify-deleterious-alleles-of-dihydropyrimidine-dehydrogenase
#18
Shikshya Shrestha, Cheng Zhang, Calvin R Jerde, Qian Nie, Hu Li, Steven M Offer, Robert B Diasio
Deleterious variants in dihydropyrimidine dehydrogenase (DPD, DPYD gene) can be highly predictive of clinical toxicity to the widely prescribed chemotherapeutic 5-fluorouracil (5-FU). However, there is very limited data pertaining to the functional consequences of the >450 reported non-synonymous DPYD variants. We developed a DPYD-specific variant classifier (DPYD-Varifier) using machine learning and in vitro functional data for 156 missense DPYD variants. The developed model showed 85% accuracy and outperformed other in silico prediction tools...
January 12, 2018: Clinical Pharmacology and Therapeutics
https://www.readbyqxmd.com/read/29298978/a-machine-learning-approach-to-integrate-big-data-for-precision-medicine-in-acute-myeloid-leukemia
#19
Su-In Lee, Safiye Celik, Benjamin A Logsdon, Scott M Lundberg, Timothy J Martins, Vivian G Oehler, Elihu H Estey, Chris P Miller, Sylvia Chien, Jin Dai, Akanksha Saxena, C Anthony Blau, Pamela S Becker
Cancers that appear pathologically similar often respond differently to the same drug regimens. Methods to better match patients to drugs are in high demand. We demonstrate a promising approach to identify robust molecular markers for targeted treatment of acute myeloid leukemia (AML) by introducing: data from 30 AML patients including genome-wide gene expression profiles and in vitro sensitivity to 160 chemotherapy drugs, a computational method to identify reliable gene expression markers for drug sensitivity by incorporating multi-omic prior information relevant to each gene's potential to drive cancer...
January 3, 2018: Nature Communications
https://www.readbyqxmd.com/read/29285887/hit-dexter-a-machine-learning-model-for-the-prediction-of-frequent-hitters
#20
Conrad Stork, Johannes Wagner, Nils-Ole Friedrich, Christina de Bruyn Kops, Martin Šícho, Johannes Kirchmair
False-positive assay readouts caused by badly behaving compounds (frequent hitters, pan-assay interference compounds - PAINS, aggregators and others) continue to pose a major challenge to experimental screening. Few in silico methods exist that allow the prediction of such problematic compounds. We report on the development of Hit Dexter, two extremely randomized trees classifiers for the prediction of compounds likely to trigger positive assay readouts either by true promiscuity or by assay interference. The models were trained on a well-prepared dataset extracted from the PubChem Bioassay database, consisting of approximately 311k compounds tested for activity on at least 50 proteins...
December 29, 2017: ChemMedChem
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