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https://www.readbyqxmd.com/read/27919863/applying-multiple-data-collection-tools-to-quantify-human-papillomavirus-vaccine-communication-on-twitter
#1
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/27919388/stage-specific-predictive-models-for-breast-cancer-survivability
#2
Rohit J Kate, Ramya Nadig
BACKGROUND: Survivability rates vary widely among various stages of breast cancer. Although machine learning models built in past to predict breast cancer survivability were given stage as one of the features, they were not trained or evaluated separately for each stage. OBJECTIVE: To investigate whether there are differences in performance of machine learning models trained and evaluated across different stages for predicting breast cancer survivability. METHODS: Using three different machine learning methods we built models to predict breast cancer survivability separately for each stage and compared them with the traditional joint models built for all the stages...
January 2017: International Journal of Medical Informatics
https://www.readbyqxmd.com/read/27917508/estimating-personalized-diagnostic-rules-depending-on-individualized-characteristics
#3
Ying Liu, Yuanjia Wang, Chaorui Huang, Donglin Zeng
There is an increasing demand for personalization of disease screening based on assessment of patient risk and other characteristics. For example, in breast cancer screening, advanced imaging technologies have made it possible to move away from 'one-size-fits-all' screening guidelines to targeted risk-based screening for those who are in need. Because diagnostic performance of various imaging modalities may vary across subjects, applying the most accurate modality to the patients who would benefit the most requires personalized strategy...
December 4, 2016: Statistics in Medicine
https://www.readbyqxmd.com/read/27913357/introducing-a-stable-bootstrap-validation-framework-for-reliable-genomic-signature-extraction
#4
Nikolaos-Kosmas Chlis, Ekaterini S Bei, Michael Zervakis
The application of machine learning methods for the identification of candidate genes responsible for phenotypes of interest, such as cancer, is a major challenge in the field of bioinformatics. These lists of genes are often called genomic signatures and their linkage to phenotype associations may form a significant step in discovering the causation between genotypes and phenotypes. Traditional methods that produce genomic signatures from DNA Microarray data tend to extract significantly different lists under relatively small variations of the training data...
November 29, 2016: IEEE/ACM Transactions on Computational Biology and Bioinformatics
https://www.readbyqxmd.com/read/27912731/predicting-the-recurrence-of-noncoding-regulatory-mutations-in-cancer
#5
Woojin Yang, Hyoeun Bang, Kiwon Jang, Min Kyung Sung, Jung Kyoon Choi
BACKGROUND: One of the greatest challenges in cancer genomics is to distinguish driver mutations from passenger mutations. Whereas recurrence is a hallmark of driver mutations, it is difficult to observe recurring noncoding mutations owing to a limited amount of whole-genome sequenced samples. Hence, it is required to develop a method to predict potentially recurrent mutations. RESULTS: In this work, we developed a random forest classifier that predicts regulatory mutations that may recur based on the features of the mutations repeatedly appearing in a given cohort...
December 3, 2016: BMC Bioinformatics
https://www.readbyqxmd.com/read/27911828/evaluating-the-evaluation-of-cancer-driver-genes
#6
Collin J Tokheim, Nickolas Papadopoulos, Kenneth W Kinzler, Bert Vogelstein, Rachel Karchin
Sequencing has identified millions of somatic mutations in human cancers, but distinguishing cancer driver genes remains a major challenge. Numerous methods have been developed to identify driver genes, but evaluation of the performance of these methods is hindered by the lack of a gold standard, that is, bona fide driver gene mutations. Here, we establish an evaluation framework that can be applied to driver gene prediction methods. We used this framework to compare the performance of eight such methods. One of these methods, described here, incorporated a machine-learning-based ratiometric approach...
November 22, 2016: Proceedings of the National Academy of Sciences of the United States of America
https://www.readbyqxmd.com/read/27908167/computer-aided-prognosis-for-cell-death-categorization-and-prediction-in-vivo-using-quantitative-ultrasound-and-machine-learning-techniques
#7
M J Gangeh, A Hashim, A Giles, L Sannachi, G J Czarnota
PURPOSE: At present, a one-size-fits-all approach is typically used for cancer therapy in patients. This is mainly because there is no current imaging-based clinical standard for the early assessment and monitoring of cancer treatment response. Here, the authors have developed, for the first time, a complete computer-aided-prognosis (CAP) system based on multiparametric quantitative ultrasound (QUS) spectroscopy methods in association with texture descriptors and advanced machine learning techniques...
December 2016: Medical Physics
https://www.readbyqxmd.com/read/27905893/computational-prediction-of-multidisciplinary-team-decision-making-for-adjuvant-breast-cancer-drug-therapies-a-machine-learning-approach
#8
Frank P Y Lin, Adrian Pokorny, Christina Teng, Rachel Dear, Richard J Epstein
BACKGROUND: Multidisciplinary team (MDT) meetings are used to optimise expert decision-making about treatment options, but such expertise is not digitally transferable between centres. To help standardise medical decision-making, we developed a machine learning model designed to predict MDT decisions about adjuvant breast cancer treatments. METHODS: We analysed MDT decisions regarding adjuvant systemic therapy for 1065 breast cancer cases over eight years. Machine learning classifiers with and without bootstrap aggregation were correlated with MDT decisions (recommended, not recommended, or discussable) regarding adjuvant cytotoxic, endocrine and biologic/targeted therapies, then tested for predictability using stratified ten-fold cross-validations...
December 1, 2016: BMC Cancer
https://www.readbyqxmd.com/read/27902695/text-mining-genotype-phenotype-relationships-from-biomedical-literature-for-database-curation-and-precision-medicine
#9
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/27889431/multiclass-cancer-classification-using-a-feature-subset-based-ensemble-from-microrna-expression-profiles
#10
Yongjun Piao, Minghao Piao, Keun Ho Ryu
Cancer classification has been a crucial topic of research in cancer treatment. In the last decade, messenger RNA (mRNA) expression profiles have been widely used to classify different types of cancers. With the discovery of a new class of small non-coding RNAs; known as microRNAs (miRNAs), various studies have shown that the expression patterns of miRNA can also accurately classify human cancers. Therefore, there is a great demand for the development of machine learning approaches to accurately classify various types of cancers using miRNA expression data...
November 21, 2016: Computers in Biology and Medicine
https://www.readbyqxmd.com/read/27876821/logic-models-to-predict-continuous-outputs-based-on-binary-inputs-with-an-application-to-personalized-cancer-therapy
#11
Theo A Knijnenburg, Gunnar W Klau, Francesco Iorio, Mathew J Garnett, Ultan McDermott, Ilya Shmulevich, Lodewyk F A Wessels
Mining large datasets using machine learning approaches often leads to models that are hard to interpret and not amenable to the generation of hypotheses that can be experimentally tested. We present 'Logic Optimization for Binary Input to Continuous Output' (LOBICO), a computational approach that infers small and easily interpretable logic models of binary input features that explain a continuous output variable. Applying LOBICO to a large cancer cell line panel, we find that logic combinations of multiple mutations are more predictive of drug response than single gene predictors...
November 23, 2016: Scientific Reports
https://www.readbyqxmd.com/read/27863190/human-papillomavirus-drives-tumor-development-throughout-the-head-and-neck-improved-prognosis-is-associated-with-an-immune-response-largely-restricted-to-the-oropharynx
#12
Ankur Chakravarthy, Stephen Henderson, Stephen M Thirdborough, Christian H Ottensmeier, Xiaoping Su, Matt Lechner, Andrew Feber, Gareth J Thomas, Tim R Fenton
Purpose In squamous cell carcinomas of the head and neck (HNSCC), the increasing incidence of oropharyngeal squamous cell carcinomas (OPSCCs) is attributable to human papillomavirus (HPV) infection. Despite commonly presenting at late stage, HPV-driven OPSCCs are associated with improved prognosis compared with HPV-negative disease. HPV DNA is also detectable in nonoropharyngeal (non-OPSCC), but its pathogenic role and clinical significance are unclear. The objectives of this study were to determine whether HPV plays a causal role in non-OPSCC and to investigate whether HPV confers a survival benefit in these tumors...
December 2016: Journal of Clinical Oncology: Official Journal of the American Society of Clinical Oncology
https://www.readbyqxmd.com/read/27848884/understanding-the-structural-basis-for-inhibition-of-cyclin-dependent-kinases-new-pieces-in-the-molecular-puzzle
#13
Nayara M Bernhardt Levin, Val Oliveira Pintro, Maurício Boff de Ávila, Bruna Boldrini de Mattos, Walter F De Azevedo
BACKGROUND: Cyclin-dependent kinases (CDKs) comprise an important protein family for development of drugs, mostly aimed for use in treatment of cancer but there is also potential for development of drugs for neurodegenerative diseases and diabetes. Since the early 1990s, structural studies have been carried out on CDKs, in order to determine the structural basis for inhibition of this protein target. OBJECTIVE: Our goal here is to review recent structural studies focused on CDKs...
November 16, 2016: Current Drug Targets
https://www.readbyqxmd.com/read/27842494/snooper-a-machine-learning-based-method-for-somatic-variant-identification-from-low-pass-next-generation-sequencing
#14
Jean-François Spinella, Pamela Mehanna, Ramon Vidal, Virginie Saillour, Pauline Cassart, Chantal Richer, Manon Ouimet, Jasmine Healy, Daniel Sinnett
BACKGROUND: Next-generation sequencing (NGS) allows unbiased, in-depth interrogation of cancer genomes. Many somatic variant callers have been developed yet accurate ascertainment of somatic variants remains a considerable challenge as evidenced by the varying mutation call rates and low concordance among callers. Statistical model-based algorithms that are currently available perform well under ideal scenarios, such as high sequencing depth, homogeneous tumor samples, high somatic variant allele frequency (VAF), but show limited performance with sub-optimal data such as low-pass whole-exome/genome sequencing data...
November 14, 2016: BMC Genomics
https://www.readbyqxmd.com/read/27829431/radiomics-based-targeted-radiotherapy-planning-rad-trap-a-computational-framework-for-prostate-cancer-treatment-planning-with-mri
#15
Rakesh Shiradkar, Tarun K Podder, Ahmad Algohary, Satish Viswanath, Rodney J Ellis, Anant Madabhushi
BACKGROUND: Radiomics or computer - extracted texture features have been shown to achieve superior performance than multiparametric MRI (mpMRI) signal intensities alone in targeting prostate cancer (PCa) lesions. Radiomics along with deformable co-registration tools can be used to develop a framework to generate targeted focal radiotherapy treatment plans. METHODS: The Rad-TRaP framework comprises three distinct modules. Firstly, a module for radiomics based detection of PCa lesions on mpMRI via a feature enabled machine learning classifier...
November 10, 2016: Radiation Oncology
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/27807826/introduction-cancer-gene-networks
#17
Robert Clarke
Constructing, evaluating, and interpreting gene networks generally sits within the broader field of systems biology, which continues to emerge rapidly, particular with respect to its application to understanding the complexity of signaling in the context of cancer biology. For the purposes of this volume, we take a broad definition of systems biology. Considering an organism or disease within an organism as a system, systems biology is the study of the integrated and coordinated interactions of the network(s) of genes, their variants both natural and mutated (e...
2017: Methods in Molecular Biology
https://www.readbyqxmd.com/read/27807108/development-of-a-prognostic-survival-algorithm-for-patients-with-metastatic-spine-disease
#18
Nuno Rui Paulino Pereira, Stein J Janssen, Eva van Dijk, Mitchel B Harris, Francis J Hornicek, Marco L Ferrone, Joseph H Schwab
BACKGROUND: Current prognostication models for survival estimation in patients with metastatic spine disease lack accuracy. Identifying new risk factors could improve existing models. We assessed factors associated with survival in patients surgically treated for spine metastases, created a classic scoring algorithm, nomogram, and boosting algorithm, and tested the predictive accuracy of the three created algorithms at estimating survival. METHODS: We included 649 patients from two tertiary care referral centers in this retrospective study (2002 to 2014)...
November 2, 2016: Journal of Bone and Joint Surgery. American Volume
https://www.readbyqxmd.com/read/27796791/automatic-segmentation-of-airway-tree-based-on-local-intensity-filter-and-machine-learning-technique-in-3d-chest-ct-volume
#19
Qier Meng, Takayuki Kitasaka, Yukitaka Nimura, Masahiro Oda, Junji Ueno, Kensaku Mori
PURPOSE: Airway segmentation plays an important role in analyzing chest computed tomography (CT) volumes for computerized lung cancer detection, emphysema diagnosis and pre- and intra-operative bronchoscope navigation. However, obtaining a complete 3D airway tree structure from a CT volume is quite a challenging task. Several researchers have proposed automated airway segmentation algorithms basically based on region growing and machine learning techniques. However, these methods fail to detect the peripheral bronchial branches, which results in a large amount of leakage...
October 28, 2016: International Journal of Computer Assisted Radiology and Surgery
https://www.readbyqxmd.com/read/27784247/current-trends-in-drug-sensitivity-prediction
#20
Isidro Cortés-Ciriano, Lewis H Mervin, Andreas Bender
Cancer cell line panels have proved useful disease models to, among others, identify genomic markers of drug sensitivity and to develop new anticancer drugs. The increasing availability of in vitro sensitivity and cell line profiling data sets raises the question of whether this information could be used, and to which extent, to predict the activity of drugs in cancer cell lines and, ultimately, in patients tumors. Drug sensitivity prediction embraces those approaches aiming at predicting in vitro drug activity on cancer cell lines by integrating genomic and/or chemical information using machine learning models...
October 26, 2016: Current Pharmaceutical Design
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