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https://www.readbyqxmd.com/read/27933461/factors-associated-with-hiv-testing-among-participants-from-substance-use-disorder-treatment-programs-in-the-us-a-machine-learning-approach
#1
Yue Pan, Hongmei Liu, Lisa R Metsch, Daniel J Feaster
HIV testing is the foundation for consolidated HIV treatment and prevention. In this study, we aim to discover the most relevant variables for predicting HIV testing uptake among substance users in substance use disorder treatment programs by applying random forest (RF), a robust multivariate statistical learning method. We also provide a descriptive introduction to this method for those who are unfamiliar with it. We used data from the National Institute on Drug Abuse Clinical Trials Network HIV testing and counseling study (CTN-0032)...
December 8, 2016: AIDS and Behavior
https://www.readbyqxmd.com/read/27932665/statistical-analysis-of-a-low-cost-method-for-multiple-disease-prediction
#2
Mohsen Bayati, Sonia Bhaskar, Andrea Montanari
Early identification of individuals at risk for chronic diseases is of significant clinical value. Early detection provides the opportunity to slow the pace of a condition, and thus help individuals to improve or maintain their quality of life. Additionally, it can lessen the financial burden on health insurers and self-insured employers. As a solution to mitigate the rise in chronic conditions and related costs, an increasing number of employers have recently begun using wellness programs, which typically involve an annual health risk assessment...
December 8, 2016: Statistical Methods in Medical Research
https://www.readbyqxmd.com/read/27932531/development-of-type-2-diabetes-mellitus-phenotyping-framework-using-expert-knowledge-and-machine-learning-approach
#3
Rina Kagawa, Yoshimasa Kawazoe, Yusuke Ida, Emiko Shinohara, Katsuya Tanaka, Takeshi Imai, Kazuhiko Ohe
BACKGROUND: Phenotyping is an automated technique that can be used to distinguish patients based on electronic health records. To improve the quality of medical care and advance type 2 diabetes mellitus (T2DM) research, the demand for T2DM phenotyping has been increasing. Some existing phenotyping algorithms are not sufficiently accurate for screening or identifying clinical research subjects. OBJECTIVE: We propose a practical phenotyping framework using both expert knowledge and a machine learning approach to develop 2 phenotyping algorithms: one is for screening; the other is for identifying research subjects...
December 7, 2016: Journal of Diabetes Science and Technology
https://www.readbyqxmd.com/read/27932294/large-scale-structure-based-prediction-and-identification-of-novel-protease-substrates-using-computational-protein-design
#4
Manasi A Pethe, Aliza B Rubenstein, Sagar D Khare
Characterizing the substrate specificity of protease enzymes is critical for illuminating the molecular basis of their diverse and complex roles in a wide array of biological processes. Rapid and accurate prediction of their extended substrate specificity would also aid in the design of custom proteases capable of selectively and controllably cleaving biotechnologically or therapeutically relevant targets. However, current in silico approaches for protease specificity prediction, rely on, and are therefore limited by, machine learning of sequence patterns in known experimental data...
December 5, 2016: Journal of Molecular Biology
https://www.readbyqxmd.com/read/27926382/prediction-of-anti-cancer-drug-response-by-kernelized-multi-task-learning
#5
Mehmet Tan
MOTIVATION: Chemotherapy or targeted therapy are two of the main treatment options for many types of cancer. Due to the heterogeneous nature of cancer, the success of the therapeutic agents differs among patients. In this sense, determination of chemotherapeutic response of the malign cells is essential for establishing a personalized treatment protocol and designing new drugs. With the recent technological advances in producing large amounts of pharmacogenomic data, in silico methods have become important tools to achieve this aim...
October 2016: Artificial Intelligence in Medicine
https://www.readbyqxmd.com/read/27925593/a-review-of-machine-learning-and-statistical-approaches-for-detecting-snp-interactions-in-high-dimensional-genomic-data
#6
Suneetha Uppu, Aneesh Krishna, Raj Gopalan
In this era of genome-wide association studies (GWAS), the quest for understanding the genetic architecture of complex diseases is rapidly increasing more than ever before. The development of high throughput genotyping and next generation sequencing technologies enables genetic epidemiological analysis of large scale data. These advances have led to the identification of a number of single nucleotide polymorphisms (SNPs) responsible for disease susceptibility. The interactions between SNPs associated with complex diseases are increasingly being explored in the current literature...
December 2, 2016: IEEE/ACM Transactions on Computational Biology and Bioinformatics
https://www.readbyqxmd.com/read/27925589/manifold-regularized-experimental-design-for-active-learning
#7
Lining Zhang, Hubert P H Shum, Ling Shao
Various machine learning and data mining tasks in classification require abundant data samples to be labeled for training. Conventional active learning methods aim at labeling the most informative samples for alleviating the labor of the user. Many previous studies in active learning select one sample after another in a greedy manner. However, this is not very effective because the classification models has to be retrained for each newly labeled sample. Moreover, many popular active learning approaches utilize the most uncertain samples by leveraging the classification hyperplane of the classifier, which is not appropriate since the classification hyperplane is inaccurate when the training data are small-sized...
December 2, 2016: IEEE Transactions on Image Processing: a Publication of the IEEE Signal Processing Society
https://www.readbyqxmd.com/read/27925585/using-contact-forces-and-robot-arm-accelerations-to-automatically-rate-surgeon-skill-at-peg-transfer
#8
Jeremy Brown, Conor O'Brien, Sarah Leung, Kristoffel Dumon, David Lee, Katherine Kuchenbecker
OBJECTIVE: Most trainees begin learning robotic minimally invasive surgery by performing inanimate practice tasks with clinical robots such as the Intuitive Surgical da Vinci. Expert surgeons are commonly asked to evaluate these performances using standardized five-point rating scales, but doing such ratings is time consuming, tedious, and somewhat subjective. This article presents an automatic skill evaluation system that analyzes only the contact force with the task materials, the broad-bandwidth accelerations of the robotic instruments and camera, and the task completion time...
December 2, 2016: IEEE Transactions on Bio-medical Engineering
https://www.readbyqxmd.com/read/27924604/exploring-genome-wide-expression-profiles-using-machine-learning-techniques
#9
Moritz Kebschull, Panos N Papapanou
Although contemporary high-throughput -omics methods produce high-dimensional data, the resulting wealth of information is difficult to assess using traditional statistical procedures. Machine learning methods facilitate the detection of additional patterns, beyond the mere identification of lists of features that differ between groups.Here, we demonstrate the utility of (1) supervised classification algorithms in class validation, and (2) unsupervised clustering in class discovery. We use data from our previous work that described the transcriptional profiles of gingival tissue samples obtained from subjects suffering from chronic or aggressive periodontitis (1) to test whether the two diagnostic entities were also characterized by differences on the molecular level, and (2) to search for a novel, alternative classification of periodontitis based on the tissue transcriptomes...
2017: Methods in Molecular Biology
https://www.readbyqxmd.com/read/27924481/machine-learning-approaches-toward-building-predictive-models-for-small-molecule-modulators-of-mirna-and-its-utility-in-virtual-screening-of-molecular-databases
#10
Vinita Periwal, Vinod Scaria
The ubiquitous role of microRNAs (miRNAs) in a number of pathological processes has suggested that they could act as potential drug targets. RNA-binding small molecules offer an attractive means for modulating miRNA function. The availability of bioassay data sets for a variety of biological assays and molecules in public domain provides a new opportunity toward utilizing them to create models and further utilize them for in silico virtual screening approaches to prioritize or assign potential functions for small molecules...
2017: Methods in Molecular Biology
https://www.readbyqxmd.com/read/27924347/preprocessing-structured-clinical-data-for-predictive-modeling-and-decision-support-a-roadmap-to-tackle-the-challenges
#11
José Carlos Ferrão, Mónica Duarte Oliveira, Filipe Janela, Henrique M G Martins
BACKGROUND: EHR systems have high potential to improve healthcare delivery and management. Although structured EHR data generates information in machine-readable formats, their use for decision support still poses technical challenges for researchers due to the need to preprocess and convert data into a matrix format. During our research, we observed that clinical informatics literature does not provide guidance for researchers on how to build this matrix while avoiding potential pitfalls...
December 7, 2016: Applied Clinical Informatics
https://www.readbyqxmd.com/read/27924046/new-data-and-features-for-advanced-data-mining-in-manteia
#12
Olivier Tassy
Manteia is an integrative database available online at http://manteia.igbmc.fr which provides a large array of OMICs data related to the development of the mouse, chicken, zebrafish and human. The system is designed to use different types of data together in order to perform advanced datamining, test hypotheses or provide candidate genes involved in biological processes or responsible for human diseases. In this new version of the database, Manteia has been enhanced with new expression data originating from microarray and next generation sequencing experiments...
October 24, 2016: Nucleic Acids Research
https://www.readbyqxmd.com/read/27923525/using-clinical-information-to-make-individualized-prognostic-predictions-in-people-at-ultra-high-risk-for-psychosis
#13
Andrea Mechelli, Ashleigh Lin, Stephen Wood, Patrick McGorry, Paul Amminger, Stefania Tognin, Philip McGuire, Jonathan Young, Barnaby Nelson, Alison Yung
Recent studies have reported an association between psychopathology and subsequent clinical and functional outcomes in people at ultra-high risk (UHR) for psychosis. This has led to the suggestion that psychopathological information could be used to make prognostic predictions in this population. However, because the current literature is based on inferences at group level, the translational value of the findings for everyday clinical practice is unclear. Here we examined whether psychopathological information could be used to make individualized predictions about clinical and functional outcomes in people at UHR...
December 3, 2016: Schizophrenia Research
https://www.readbyqxmd.com/read/27922592/distributed-solar-photovoltaic-array-location-and-extent-dataset-for-remote-sensing-object-identification
#14
Kyle Bradbury, Raghav Saboo, Timothy L Johnson, Jordan M Malof, Arjun Devarajan, Wuming Zhang, Leslie M Collins, Richard G Newell
Earth-observing remote sensing data, including aerial photography and satellite imagery, offer a snapshot of the world from which we can learn about the state of natural resources and the built environment. The components of energy systems that are visible from above can be automatically assessed with these remote sensing data when processed with machine learning methods. Here, we focus on the information gap in distributed solar photovoltaic (PV) arrays, of which there is limited public data on solar PV deployments at small geographic scales...
December 6, 2016: Scientific Data
https://www.readbyqxmd.com/read/27922074/resistance-gene-identification-from-larimichthys-crocea-with-machine-learning-techniques
#15
Yinyin Cai, Zhijun Liao, Ying Ju, Juan Liu, Yong Mao, Xiangrong Liu
The research on resistance genes (R-gene) plays a vital role in bioinformatics as it has the capability of coping with adverse changes in the external environment, which can form the corresponding resistance protein by transcription and translation. It is meaningful to identify and predict R-gene of Larimichthys crocea (L.Crocea). It is friendly for breeding and the marine environment as well. Large amounts of L.Crocea's immune mechanisms have been explored by biological methods. However, much about them is still unclear...
December 6, 2016: Scientific Reports
https://www.readbyqxmd.com/read/27920952/a-methodology-for-the-design-of-experiments-in-computational-intelligence-with-multiple-regression-models
#16
Carlos Fernandez-Lozano, Marcos Gestal, Cristian R Munteanu, Julian Dorado, Alejandro Pazos
The design of experiments and the validation of the results achieved with them are vital in any research study. This paper focuses on the use of different Machine Learning approaches for regression tasks in the field of Computational Intelligence and especially on a correct comparison between the different results provided for different methods, as those techniques are complex systems that require further study to be fully understood. A methodology commonly accepted in Computational intelligence is implemented in an R package called RRegrs...
2016: PeerJ
https://www.readbyqxmd.com/read/27920762/artificial-intelligence-vs-statistical-modeling-and-optimization-of-continuous-bead-milling-process-for-bacterial-cell-lysis
#17
Shafiul Haque, Saif Khan, Mohd Wahid, Sajad A Dar, Nipunjot Soni, Raju K Mandal, Vineeta Singh, Dileep Tiwari, Mohtashim Lohani, Mohammed Y Areeshi, Thavendran Govender, Hendrik G Kruger, Arshad Jawed
For a commercially viable recombinant intracellular protein production process, efficient cell lysis and protein release is a major bottleneck. The recovery of recombinant protein, cholesterol oxidase (COD) was studied in a continuous bead milling process. A full factorial response surface methodology (RSM) design was employed and compared to artificial neural networks coupled with genetic algorithm (ANN-GA). Significant process variables, cell slurry feed rate (A), bead load (B), cell load (C), and run time (D), were investigated and optimized for maximizing COD recovery...
2016: Frontiers in Microbiology
https://www.readbyqxmd.com/read/27919863/applying-multiple-data-collection-tools-to-quantify-human-papillomavirus-vaccine-communication-on-twitter
#18
Philip M Massey, Amy Leader, Elad Yom-Tov, Alexandra Budenz, Kara Fisher, Ann C Klassen
BACKGROUND: Human papillomavirus (HPV) is the most common sexually transmitted infection in the United States. There are several vaccines that protect against strains of HPV most associated with cervical and other cancers. Thus, HPV vaccination has become an important component of adolescent preventive health care. As media evolves, more information about HPV vaccination is shifting to social media platforms such as Twitter. Health information consumed on social media may be especially influential for segments of society such as younger populations, as well as ethnic and racial minorities...
December 5, 2016: Journal of Medical Internet Research
https://www.readbyqxmd.com/read/27919732/learning-from-heterogeneous-temporal-data-in-electronic-health-records
#19
Jing Zhao, Panagiotis Papapetrou, Lars Asker, Henrik Boström
Electronic health records contain large amounts of longitudinal data that are valuable for biomedical informatics research. The application of machine learning is a promising alternative to manual analysis of such data. However, the complex structure of the data, which includes clinical events that are unevenly distributed over time, poses a challenge for standard learning algorithms. Some approaches to modeling temporal data rely on extracting single values from time series; however, this leads to the loss of potentially valuable sequential information...
December 2, 2016: Journal of Biomedical Informatics
https://www.readbyqxmd.com/read/27919552/decoding-the-ecological-function-of-accessory-genome
#20
Michelle Qiu Carter
Shiga toxin-producing Escherichia coli O157:H7 primarily resides in cattle asymptomatically, and can be transmitted to humans through food. A study by Lupolova et al. applied a machine-learning approach to complex pan-genome information and predicted that only a small subset of bovine isolates have the potential to cause diseases in humans.
December 2, 2016: Trends in Microbiology
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