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https://www.readbyqxmd.com/read/28521821/intratumoral-and-peritumoral-radiomics-for-the-pretreatment-prediction-of-pathological-complete-response-to-neoadjuvant-chemotherapy-based-on-breast-dce-mri
#1
Nathaniel M Braman, Maryam Etesami, Prateek Prasanna, Christina Dubchuk, Hannah Gilmore, Pallavi Tiwari, Donna Pletcha, Anant Madabhushi
BACKGROUND: In this study, we evaluated the ability of radiomic textural analysis of intratumoral and peritumoral regions on pretreatment breast cancer dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) to predict pathological complete response (pCR) to neoadjuvant chemotherapy (NAC). METHODS: A total of 117 patients who had received NAC were retrospectively analyzed. Within the intratumoral and peritumoral regions of T1-weighted contrast-enhanced MRI scans, a total of 99 radiomic textural features were computed at multiple phases...
May 18, 2017: Breast Cancer Research: BCR
https://www.readbyqxmd.com/read/28521616/a-10-gene-classifier-for-indeterminate-thyroid-nodules-development-and-multicenter-accuracy-study
#2
Hernan E Gonzalez, Jose R Martínez, Sergio Vargas, Antonieta Solar, Loreto Pamela Véliz, Francisco Cruz, Tatiana Arias, Soledad Loyola, Eleonora Horvath, Hernán Tala, Eufrosina Traipe, Manuel Meneses, Luis Marin, Nelson Wohllk, Rene Eduardo Diaz, Jesús Véliz, Pedro Pineda, Patricia Arroyo, Natalia Mena, Milagros Bracamonte, Giovanna Miranda, Elsa Bruce, Soledad Urra
BACKGROUND: In most of the world, diagnostic surgery remains as the most frequent approach for indeterminate thyroid cytology. Although several molecular tests are available for central-lab testing in the US, there are no available kits for local laboratory testing. The aim of this study was to develop a prototype in-vitro diagnostic (IVD) gene classifier for diagnosis of indeterminate thyroid cytology. METHODS: In a first stage, the expression of 18 genes was determined by qPCR in a broad histopathological spectrum of fresh tissue biopsies (114)...
May 18, 2017: Thyroid: Official Journal of the American Thyroid Association
https://www.readbyqxmd.com/read/28500765/developing-bayesian-networks-from-a-dependency-layered-ontology-a-proof-of-concept-in-radiation-oncology
#3
Alan M Kalet, Jason N Doctor, John H Gennari, Mark H Phillips
PURPOSE: Bayesian networks (BNs) are graphical representations of probabilistic knowledge that offer normative reasoning under uncertainty and are well suited for use in medical domains. Traditional knowledge-based network development of BN topology requires that modeling experts establish relevant dependency links between domain concepts by searching and translating published literature, querying domain experts, or applying machine learning algorithms on data. For initial development these methods are time-intensive and this cost hinders the growth of BN applications in medical decision making...
May 13, 2017: Medical Physics
https://www.readbyqxmd.com/read/28484602/ppimpred-a-web-server-for-high-throughput-screening-of-small-molecules-targeting-protein-protein-interaction
#4
Tanmoy Jana, Abhirupa Ghosh, Sukhen Das Mandal, Raja Banerjee, Sudipto Saha
PPIMpred is a web server that allows high-throughput screening of small molecules for targeting specific protein-protein interactions, namely Mdm2/P53, Bcl2/Bak and c-Myc/Max. Three different kernels of support vector machine (SVM), namely, linear, polynomial and radial basis function (RBF), and two other machine learning techniques including Naive Bayes and Random Forest were used to train the models. A fivefold cross-validation technique was used to measure the performance of these classifiers. The RBF kernel of SVM outperformed and/or was comparable with all other methods with accuracy values of 83%, 79% and 90% for Mdm2/P53, Bcl2/Bak and c-Myc/Max, respectively...
April 2017: Royal Society Open Science
https://www.readbyqxmd.com/read/28482049/soft-sweeps-are-the-dominant-mode-of-adaptation-in-the-human-genome
#5
Daniel R Schrider, Andrew D Kern
The degree to which adaptation in recent human evolution shapes genetic variation remains controversial. This is in part due to the limited evidence in humans for classic "hard selective sweeps," wherein a novel beneficial mutation rapidly sweeps through a population to fixation. However, positive selection may often proceed via "soft sweeps" acting on mutations already present within a population. Here we examine recent positive selection across six human populations using a powerful machine learning approach that is sensitive to both hard and soft sweeps...
May 8, 2017: Molecular Biology and Evolution
https://www.readbyqxmd.com/read/28475101/binary-markov-random-fields-and-interpretable-mass-spectra-discrimination
#6
Ao Kong, Robert Azencott
For mass spectra acquired from cancer patients by MALDI or SELDI techniques, automated discrimination between cancer types or stages has often been implemented by machine learning algorithms. Nevertheless, these techniques typically lack interpretability in terms of biomarkers. In this paper, we propose a new mass spectra discrimination algorithm by parameterized Markov Random Fields to automatically generate interpretable classifiers with small groups of scored biomarkers. A dataset of 238 MALDI colorectal mass spectra and two datasets of 216 and 253 SELDI ovarian mass spectra respectively were used to test our approach...
February 11, 2017: Statistical Applications in Genetics and Molecular Biology
https://www.readbyqxmd.com/read/28472832/accuracy-of-diagnosing-invasive-colorectal-cancer-using-computer-aided-endocytoscopy
#7
Kenichi Takeda, Shin-Ei Kudo, Yuichi Mori, Masashi Misawa, Toyoki Kudo, Kunihiko Wakamura, Atsushi Katagiri, Toshiyuki Baba, Eiji Hidaka, Fumio Ishida, Haruhiro Inoue, Masahiro Oda, Kensaku Mori
Background and study aims Invasive cancer carries the risk of metastasis, and therefore, the ability to distinguish between invasive cancerous lesions and less-aggressive lesions is important. We evaluated a computer-aided diagnosis system that uses ultra-high (approximately × 400) magnification endocytoscopy (EC-CAD). Patients and methods We generated an image database from a consecutive series of 5843 endocytoscopy images of 375 lesions. For construction of a diagnostic algorithm, 5543 endocytoscopy images from 238 lesions were randomly extracted from the database for machine learning...
May 4, 2017: Endoscopy
https://www.readbyqxmd.com/read/28472273/ebt-a-statistic-test-identifying-moderate-size-of-significant-features-with-balanced-power-and-precision-for-genome-wide-rate-comparisons
#8
Xinjie Hui, Yueming Hu, Ming-An Sun, Xingsheng Shu, Rongfei Han, Qinggang Ge, Yejun Wang
Motivation: In genome-wide rate comparison studies, there is a big challenge for effective identification of an appropriate number of significant features objectively, since traditional statistical comparisons without multi-testing correction can generate a large number of false positives while multi-testing correction tremendously decrease the statistic power. Results: In this study, we proposed a new exact test based on the translation of rate comparison to two binomial distributions...
May 3, 2017: Bioinformatics
https://www.readbyqxmd.com/read/28472204/will-machine-learning-tip-the-balance-in-breast-cancer-screening
#9
Andrew D Trister, Diana S M Buist, Christoph I Lee
No abstract text is available yet for this article.
May 4, 2017: JAMA Oncology
https://www.readbyqxmd.com/read/28472185/a-computational-method-for-prediction-of-matrix-proteins-in-endogenous-retroviruses
#10
Yucheng Ma, Ruiling Liu, Hongqiang Lv, Jiuqiang Han, Dexing Zhong, Xinman Zhang
Human endogenous retroviruses (HERVs) encode active retroviral proteins, which may be involved in the progression of cancer and other diseases. Matrix protein (MA), in group-specific antigen genes (gag) of retroviruses, is associated with the virus envelope glycoproteins in most mammalian retroviruses and may be involved in virus particle assembly, transport and budding. However, the amount of annotated MAs in ERVs is still at a low level so far. No computational method to predict the exact start and end coordinates of MAs in gags has been proposed yet...
2017: PloS One
https://www.readbyqxmd.com/read/28464746/autodelineation-of-cervical-cancers-using-multiparametric-magnetic-resonance-imaging-and-machine-learning
#11
Turid Torheim, Eirik Malinen, Knut Håkon Hole, Kjersti Vassmo Lund, Ulf G Indahl, Heidi Lyng, Knut Kvaal, Cecilia M Futsaether
BACKGROUND: Tumour delineation is a challenging, time-consuming and complex part of radiotherapy planning. In this study, an automatic method for delineating locally advanced cervical cancers was developed using a machine learning approach. MATERIALS AND METHODS: A method for tumour segmentation based on image voxel classification using Fisher?s Linear Discriminant Analysis (LDA) was developed. This was applied to magnetic resonance (MR) images of 78 patients with locally advanced cervical cancer...
June 2017: Acta Oncologica
https://www.readbyqxmd.com/read/28451550/improving-the-prediction-of-survival-in-cancer-patients-by-using-machine-learning-techniques-experience-of-gene-expression-data-a-narrative-review
#12
REVIEW
Azadeh Bashiri, Marjan Ghazisaeedi, Reza Safdari, Leila Shahmoradi, Hamide Ehtesham
BACKGROUND: Today, despite the many advances in early detection of diseases, cancer patients have a poor prognosis and the survival rates in them are low. Recently, microarray technologies have been used for gathering thousands data about the gene expression level of cancer cells. These types of data are the main indicators in survival prediction of cancer. This study highlights the improvement of survival prediction based on gene expression data by using machine learning techniques in cancer patients...
February 2017: Iranian Journal of Public Health
https://www.readbyqxmd.com/read/28440912/performance-of-in-silico-tools-for-the-evaluation-of-p16ink4a-cdkn2a-variants-in-cagi
#13
Marco Carraro, Giovanni Minervini, Manuel Giollo, Yana Bromberg, Emidio Capriotti, Rita Casadio, Roland Dunbrack, Lisa Elefanti, Pietro Fariselli, Carlo Ferrari, Julian Gough, Panagiotis Katsonis, Emanuela Leonardi, Olivier Lichtarge, Chiara Menin, Pier Luigi Martelli, Abhishek Niroula, Lipika R Pal, Susanna Repo, Maria Chiara Scaini, Mauno Vihinen, Qiong Wei, Qifang Xu, Yuedong Yang, Yizhou Yin, Jan Zaucha, Huiying Zhao, Yaoqi Zhou, Steven E Brenner, John Moult, Silvio C E Tosatto
Correct phenotypic interpretation of variants of unknown significance for cancer-associated genes is a diagnostic challenge as genetic screenings gain in popularity in the next-generation sequencing era. The Critical Assessment of Genome Interpretation (CAGI) experiment aims to test and define the state of the art of genotype-phenotype interpretation. Here, we present the assessment of the CAGI p16INK4a challenge. Participants were asked to predict the effect on cellular proliferation of 10 variants for the p16INK4a tumor suppressor, a cyclin-dependent kinase inhibitor encoded by the CDKN2A gene...
April 25, 2017: Human Mutation
https://www.readbyqxmd.com/read/28440283/quantitative-diagnosis-of-breast-tumors-by-morphometric-classification-of-microenvironmental-myoepithelial-cells-using-a-machine-learning-approach
#14
Yoichiro Yamamoto, Akira Saito, Ayako Tateishi, Hisashi Shimojo, Hiroyuki Kanno, Shinichi Tsuchiya, Ken-Ichi Ito, Eric Cosatto, Hans Peter Graf, Rodrigo R Moraleda, Roland Eils, Niels Grabe
Machine learning systems have recently received increased attention for their broad applications in several fields. In this study, we show for the first time that histological types of breast tumors can be classified using subtle morphological differences of microenvironmental myoepithelial cell nuclei without any direct information about neoplastic tumor cells. We quantitatively measured 11661 nuclei on the four histological types: normal cases, usual ductal hyperplasia and low/high grade ductal carcinoma in situ (DCIS)...
April 25, 2017: Scientific Reports
https://www.readbyqxmd.com/read/28439832/computational-and-experimental-identification-of-tissue-specific-microrna-targets
#15
Raheleh Amirkhah, Hojjat Naderi Meshkin, Ali Farazmand, John E J Rasko, Ulf Schmitz
In this chapter we discuss computational methods for the prediction of microRNA (miRNA) targets. More specifically, we consider machine learning-based approaches and explain why these methods have been relatively unsuccessful in reducing the number of false positive predictions. Further we suggest approaches designed to improve their performance by considering tissue-specific target regulation. We argue that the miRNA targetome differs depending on the tissue type and introduce a novel algorithm that predicts miRNA targets specifically for colorectal cancer...
2017: Methods in Molecular Biology
https://www.readbyqxmd.com/read/28438725/analyzing-and-predicting-user-participations-in-online-health-communities-a-social-support-perspective
#16
Xi Wang, Kang Zhao, Nick Street
BACKGROUND: Online health communities (OHCs) have become a major source of social support for people with health problems. Members of OHCs interact online with similar peers to seek, receive, and provide different types of social support, such as informational support, emotional support, and companionship. As active participations in an OHC are beneficial to both the OHC and its users, it is important to understand factors related to users' participations and predict user churn for user retention efforts...
April 24, 2017: Journal of Medical Internet Research
https://www.readbyqxmd.com/read/28437615/experimental-demonstration-of-feature-extraction-and-dimensionality-reduction-using-memristor-networks
#17
Shinhyun Choi, Jong Hoon Shin, Jihang Lee, Patrick Sheridan, Wei D Lu
Memristors have been considered as a leading candidate for a number of critical applications ranging from nonvolatile memory to non-Von Neumann computing systems. Feature extraction, which aims to transform input data from a high-dimensional space to a space with fewer dimensions, is an important technique widely used in machine learning and pattern recognition applications. Here, we experimentally demonstrate that memristor arrays can be used to perform principal component analysis, one of the most commonly used feature extraction techniques, through online, unsupervised learning...
May 1, 2017: Nano Letters
https://www.readbyqxmd.com/read/28437602/holography-machine-learning-and-cancer-cells
#18
Christopher B Raub, George Nehmetallah
No abstract text is available yet for this article.
April 24, 2017: Cytometry. Part A: the Journal of the International Society for Analytical Cytology
https://www.readbyqxmd.com/read/28432182/toward-a-direct-and-scalable-identification-of-reduced-models-for-categorical-processes
#19
Susanne Gerber, Illia Horenko
The applicability of many computational approaches is dwelling on the identification of reduced models defined on a small set of collective variables (colvars). A methodology for scalable probability-preserving identification of reduced models and colvars directly from the data is derived-not relying on the availability of the full relation matrices at any stage of the resulting algorithm, allowing for a robust quantification of reduced model uncertainty and allowing us to impose a priori available physical information...
April 21, 2017: Proceedings of the National Academy of Sciences of the United States of America
https://www.readbyqxmd.com/read/28426134/metastasis-detection-from-whole-slide-images-using-local-features-and-random-forests
#20
Mira Valkonen, Kimmo Kartasalo, Kaisa Liimatainen, Matti Nykter, Leena Latonen, Pekka Ruusuvuori
Digital pathology has led to a demand for automated detection of regions of interest, such as cancerous tissue, from scanned whole slide images. With accurate methods using image analysis and machine learning, significant speed-up, and savings in costs through increased throughput in histological assessment could be achieved. This article describes a machine learning approach for detection of cancerous tissue from scanned whole slide images. Our method is based on feature engineering and supervised learning with a random forest model...
April 20, 2017: Cytometry. Part A: the Journal of the International Society for Analytical Cytology
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