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https://www.readbyqxmd.com/read/28812013/intelligent-techniques-using-molecular-data-analysis-in-leukaemia-an-opportunity-for-personalized-medicine-support-system
#1
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
#2
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
#3
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
#4
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...
July 26, 2017: Journal of Biomedical Informatics
https://www.readbyqxmd.com/read/28750905/automated-problem-list-generation-and-physicians-perspective-from-a-pilot-study
#5
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
#6
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
#7
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
#8
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
#9
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
#10
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
https://www.readbyqxmd.com/read/28710752/ultrasound-imaging-of-apoptosis-spectroscopic-detection-of-dna-damage-effects-in-vivo
#11
Hadi Tadayyon, Mehrdad J Gangeh, Roxana Vlad, Michael C Kolios, Gregory J Czarnota
In this chapter, we describe two new methodologies: (1) application of high-frequency ultrasound spectroscopy for in vivo detection of cancer cell death in small animal models, and (2) extension of ultrasound spectroscopy to the lower frequency range (i.e., 1-10 MHz range) for the detection of cell death in vivo in preclinical and clinical settings. Experiments using tumor xenografts in mice and cancer treatments based on chemotherapy are described. Finally, we describe how one can detect cancer response to treatment in patients noninvasively early (within 1 week of treatment initiation) using low-frequency ultrasound spectroscopic imaging and advanced machine learning techniques...
2017: Methods in Molecular Biology
https://www.readbyqxmd.com/read/28704574/a-computational-method-for-unveiling-the-target-promiscuity-of-pharmacologically-active-compounds
#12
Petra Schneider, Gisbert Schneider
Drug discovery is governed by the desire to find ligands with defined modes of action. It has been realized that even designated selective drugs may have more macromolecular targets than is commonly thought. Consequently, it will be mandatory to consider multitarget activity for the design of future medicines. Computational models assist medicinal chemists in this effort by helping to eliminate unsuitable lead structures and spot undesired drug effects early in the discovery process. Here, we present a straightforward computational method to find previously unknown targets of pharmacologically active compounds...
July 13, 2017: Angewandte Chemie
https://www.readbyqxmd.com/read/28682479/metabolomic-studies-of-indonesian-jamu-medicines-prediction-of-jamu-efficacy-and-identification-of-important-metabolites
#13
Sony Hartono Wijaya, Irmanida Batubara, Takaaki Nishioka, Md Altaf-Ul-Amin, Shigehiko Kanaya
In order to obtain a better understanding why some Jamu formulas can be used to treat a specific disease, we performed metabolomic studies of Jamu by taking into consideration the biologically active compounds existing in plants used as Jamu ingredients. A thorough integration of information from omics is expected to provide solid evidence-based scientific rationales for the development of modern phytomedicines. This study focused on prediction of Jamu efficacy based on its component metabolites and also identification of important metabolites related to each efficacy group...
July 6, 2017: Molecular Informatics
https://www.readbyqxmd.com/read/28681133/innovative-clinical-trial-designs-for-precision-medicine-in-heart-failure-with-preserved-ejection-fraction
#14
Sanjiv J Shah
A major challenge in the care of patients with heart failure and preserved ejection fraction (HFpEF) is the lack of proven therapies due to disappointing results from randomized controlled trials (RCTs). The heterogeneity of the HFpEF syndrome and the use of conventional RCT designs are possible reasons underlying the failure of these trials. There are several factors-including the widespread adoption of electronic health records, decreasing costs of obtaining high-dimensional data, and the availability of a wide variety of potential therapeutics-that have evolved to enable more innovative clinical trial designs in HFpEF...
June 2017: Journal of Cardiovascular Translational Research
https://www.readbyqxmd.com/read/28672838/recent-advances-in-conotoxin-classification-by-using-machine-learning-methods
#15
REVIEW
Fu-Ying Dao, Hui Yang, Zhen-Dong Su, Wuritu Yang, Yun Wu, Ding Hui, Wei Chen, Hua Tang, Hao Lin
Conotoxins are disulfide-rich small peptides, which are invaluable peptides that target ion channel and neuronal receptors. Conotoxins have been demonstrated as potent pharmaceuticals in the treatment of a series of diseases, such as Alzheimer's disease, Parkinson's disease, and epilepsy. In addition, conotoxins are also ideal molecular templates for the development of new drug lead compounds and play important roles in neurobiological research as well. Thus, the accurate identification of conotoxin types will provide key clues for the biological research and clinical medicine...
June 25, 2017: Molecules: a Journal of Synthetic Chemistry and Natural Product Chemistry
https://www.readbyqxmd.com/read/28670152/deep-learning-in-medical-imaging-general-overview
#16
REVIEW
June-Goo Lee, Sanghoon Jun, Young-Won Cho, Hyunna Lee, Guk Bae Kim, Joon Beom Seo, Namkug Kim
The artificial neural network (ANN)-a machine learning technique inspired by the human neuronal synapse system-was introduced in the 1950s. However, the ANN was previously limited in its ability to solve actual problems, due to the vanishing gradient and overfitting problems with training of deep architecture, lack of computing power, and primarily the absence of sufficient data to train the computer system. Interest in this concept has lately resurfaced, due to the availability of big data, enhanced computing power with the current graphics processing units, and novel algorithms to train the deep neural network...
July 2017: Korean Journal of Radiology: Official Journal of the Korean Radiological Society
https://www.readbyqxmd.com/read/28657906/radiogenomics-and-radiotherapy-response-modeling
#17
Issam El Naqa, Sarah L Kerns, James Coates, Yi Luo, Corey Speers, Catharine M L West, Barry S Rosenstein, Randall K Ten Haken
Advances in patient-specific information and biotechnology have contributed to a new era of computational medicine. Radiogenomics has emerged as a new field that investigates the role of genetics in treatment response to radiation therapy. Radiation oncology is currently attempting to embrace these recent advances and add to its rich history by maintaining its prominent role as a quantitative leader in oncologic response modeling. Here, we provide an overview of radiogenomics starting with genotyping, data aggregation, and application of different modeling approaches based on modifying traditional radiobiological methods or application of advanced machine learning techniques...
August 1, 2017: Physics in Medicine and Biology
https://www.readbyqxmd.com/read/28657867/machine-learning-and-prediction-in-medicine-beyond-the-peak-of-inflated-expectations
#18
Jonathan H Chen, Steven M Asch
Big data, we have all heard, promise to transform health care with the widespread capture of electronic health records and high-volume data streams from sources ranging from insurance claims and registries to personal genomics and biosensors. Artificial-intelligence and machine-learning predictive..
June 29, 2017: New England Journal of Medicine
https://www.readbyqxmd.com/read/28649444/reverse-engineering-highlights-potential-principles-of-large-gene-regulatory-network-design-and-learning
#19
Clément Carré, André Mas, Gabriel Krouk
Inferring transcriptional gene regulatory networks from transcriptomic datasets is a key challenge of systems biology, with potential impacts ranging from medicine to agronomy. There are several techniques used presently to experimentally assay transcription factors to target relationships, defining important information about real gene regulatory networks connections. These techniques include classical ChIP-seq, yeast one-hybrid, or more recently, DAP-seq or target technologies. These techniques are usually used to validate algorithm predictions...
2017: NPJ Systems Biology and Applications
https://www.readbyqxmd.com/read/28641555/supervised-machine-learning-methods-applied-to-predict-ligand-binding-affinity
#20
Gabriela Sehnem Heck, Val Oliveira Pintro, Richard Rene Pereira, Mauricio Boff de Ávila, Nayara Maria Bernhardt Levin, Walter Filgueira de Azevedo
BACKGROUND: Calculation of ligand-binding affinity is an open problem in computational medicinal chemistry. The ability to computationally predict affinities has a beneficial impact in the early stages of drug development, since it allows a mathematical model to assess protein-ligand interactions. Due to the availability of structural and binding information, machine learning methods have been applied to generate scoring functions with good predictive power. OBJECTIVE: Our goal here is to review recent developments in the application of machine learning methods to predict ligand- binding affinity...
June 22, 2017: Current Medicinal Chemistry
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