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https://www.readbyqxmd.com/read/28440912/performance-of-in-silico-tools-for-the-evaluation-of-p16ink4a-cdkn2a-variants-in-cagi
#1
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 ten 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
#2
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
#3
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
#4
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
#5
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 non-volatile 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 (PCA), one of the most commonly-used feature extraction techniques, through online, unsupervised learning...
April 24, 2017: Nano Letters
https://www.readbyqxmd.com/read/28437602/holography-machine-learning-and-cancer-cells
#6
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
#7
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
#8
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
https://www.readbyqxmd.com/read/28426133/quantitative-phase-microscopy-spatial-signatures-of-cancer-cells
#9
Darina Roitshtain, Lauren Wolbromsky, Evgeny Bal, Hayit Greenspan, Lisa L Satterwhite, Natan T Shaked
We present cytometric classification of live healthy and cancerous cells by using the spatial morphological and textural information found in the label-free quantitative phase images of the cells. We compare both healthy cells to primary tumor cells and primary tumor cells to metastatic cancer cells, where tumor biopsies and normal tissues were isolated from the same individuals. To mimic analysis of liquid biopsies by flow cytometry, the cells were imaged while unattached to the substrate. We used low-coherence off-axis interferometric phase microscopy setup, which allows a single-exposure acquisition mode, and thus is suitable for quantitative imaging of dynamic cells during flow...
April 20, 2017: Cytometry. Part A: the Journal of the International Society for Analytical Cytology
https://www.readbyqxmd.com/read/28422152/towards-automatic-pulmonary-nodule-management-in-lung-cancer-screening-with-deep-learning
#10
Francesco Ciompi, Kaman Chung, Sarah J van Riel, Arnaud Arindra Adiyoso Setio, Paul K Gerke, Colin Jacobs, Ernst Th Scholten, Cornelia Schaefer-Prokop, Mathilde M W Wille, Alfonso Marchianò, Ugo Pastorino, Mathias Prokop, Bram van Ginneken
The introduction of lung cancer screening programs will produce an unprecedented amount of chest CT scans in the near future, which radiologists will have to read in order to decide on a patient follow-up strategy. According to the current guidelines, the workup of screen-detected nodules strongly relies on nodule size and nodule type. In this paper, we present a deep learning system based on multi-stream multi-scale convolutional networks, which automatically classifies all nodule types relevant for nodule workup...
April 19, 2017: Scientific Reports
https://www.readbyqxmd.com/read/28420510/gene-set-based-analysis-of-mucinous-ovarian-carcinoma
#11
Chia-Ming Chang, Peng-Hui Wang, Huann-Cheng Horng
OBJECTIVE: Mucinous ovarian carcinoma (MOC) is an uncommon subtype of epithelial ovarian cancers, and the pathogenesis is still poorly understood because of its rarity. We conducted a gene set-based analysis to investigate the pathogenesis of MOC by integrating microarray gene expression datasets based on the regularity of functions defined by gene ontology or canonical pathway databases. MATERIALS AND METHODS: Forty-five pairs of MOC and normal ovarian tissue sample gene expression profiles were downloaded from the National Center for Biotechnology Information Gene Expression Omnibus database...
April 2017: Taiwanese Journal of Obstetrics & Gynecology
https://www.readbyqxmd.com/read/28415579/metabolite-marker-discovery-for-the-detection-of-bladder-cancer-by-comparative-metabolomics
#12
Chi-Hung Shao, Chien-Lun Chen, Jia-You Lin, Chao-Jung Chen, Shu-Hsuan Fu, Yi-Ting Chen, Yu-Sun Chang, Jau-Song Yu, Ke-Hung Tsui, Chiun-Gung Juo, Kun-Pin Wu
Bladder cancer is one of the most common urinary tract carcinomas in the world. Urine metabolomics is a promising approach for bladder cancer detection and marker discovery since urine is in direct contact with bladder epithelia cells; metabolites released from bladder cancer cells may be enriched in urine samples. In this study, we applied ultra-performance liquid chromatography time-of-flight mass spectrometry to profile metabolite profiles of 87 samples from bladder cancer patients and 65 samples from hernia patients...
March 21, 2017: Oncotarget
https://www.readbyqxmd.com/read/28414908/experimental-computational-study-of-carbon-nanotube-effects-on-mitochondrial-respiration-in-silico-nano-qspr-machine-learning-models-based-on-new-raman-spectra-transform-with-markov-shannon-entropy-invariants
#13
Michael González-Durruthy, Luciane C Alberici, Carlos Curti, Zeki Naal, David T Atique-Sawazaki, José M Vázquez-Naya, Humberto González-Díaz, Cristian R Munteanu
The study of selective toxicity of carbon nanotubes (CNTs) on mitochondria (CNT-mitotoxicity) is of major interest for future biomedical applications. In the current work, the mitochondrial oxygen consumption (E3) is measured under three experimental conditions by exposure to pristine and oxidized CNTs (hydroxylated and carboxylated). Respiratory functional assays showed that the information on the CNT Raman spectroscopy could be useful to predict structural parameters of mitotoxicity induced by CNTs. The in vitro functional assays show that the mitochondrial oxidative phosphorylation by ATP-synthase (or state V3 of respiration) was not perturbed in isolated rat-liver mitochondria...
April 25, 2017: Journal of Chemical Information and Modeling
https://www.readbyqxmd.com/read/28405516/assessment-of-tumor-infiltrating-tcrv%C3%AE-9v%C3%AE-2-%C3%AE-%C3%AE-lymphocyte-abundance-by-deconvolution-of-human-cancers-microarrays
#14
Marie Tosolini, Frédéric Pont, Mary Poupot, François Vergez, Marie-Laure Nicolau-Travers, David Vermijlen, Jean-Emmanuel Sarry, Francesco Dieli, Jean-Jacques Fournié
Most human blood γδ cells are cytolytic TCRVγ9Vδ2(+) lymphocytes with antitumor activity. They are currently investigated in several clinical trials of cancer immunotherapy but so far, their tumor infiltration has not been systematically explored across human cancers. Novel algorithms allowing the deconvolution of bulk tumor transcriptomes to find the relative proportions of infiltrating leucocytes, such as CIBERSORT, should be appropriate for this aim but in practice they fail to accurately recognize γδ T lymphocytes...
2017: Oncoimmunology
https://www.readbyqxmd.com/read/28400994/pathological-diagnosis-of-gastric-cancers-with-a-novel-computerized-analysis-system
#15
Kosuke Oikawa, Akira Saito, Tomoharu Kiyuna, Hans Peter Graf, Eric Cosatto, Masahiko Kuroda
BACKGROUND: Recent studies of molecular biology have provided great advances for diagnostic molecular pathology. Automated diagnostic systems with computerized scanning for sampled cells in fluids or smears are now widely utilized. Automated analysis of tissue sections is, however, very difficult because they exhibit a complex mixture of overlapping malignant tumor cells, benign host-derived cells, and extracellular materials. Thus, traditional histological diagnosis is still the most powerful method for diagnosis of diseases...
2017: Journal of Pathology Informatics
https://www.readbyqxmd.com/read/28382314/training-a-cell-level-classifier-for-detecting-basal-cell-carcinoma-by-combining-human-visual-attention-maps-with-low-level-handcrafted-features
#16
Germán Corredor, Jon Whitney, Viviana Arias, Anant Madabhushi, Eduardo Romero
Computational histomorphometric approaches typically use low-level image features for building machine learning classifiers. However, these approaches usually ignore high-level expert knowledge. A computational model (M_im) combines low-, mid-, and high-level image information to predict the likelihood of cancer in whole slide images. Handcrafted low- and mid-level features are computed from area, color, and spatial nuclei distributions. High-level information is implicitly captured from the recorded navigations of pathologists while exploring whole slide images during diagnostic tasks...
April 2017: Journal of Medical Imaging
https://www.readbyqxmd.com/read/28382223/identification-of-histological-correlates-of-overall-survival-in-lower-grade-gliomas-using-a-bag-of-words-paradigm-a-preliminary-analysis-based-on-hematoxylin-eosin-stained-slides-from-the-lower-grade-glioma-cohort-of-the-cancer-genome-atlas
#17
Reid Trenton Powell, Adriana Olar, Shivali Narang, Ganesh Rao, Erik Sulman, Gregory N Fuller, Arvind Rao
BACKGROUND: Glioma, the most common primary brain neoplasm, describes a heterogeneous tumor of multiple histologic subtypes and cellular origins. At clinical presentation, gliomas are graded according to the World Health Organization guidelines (WHO), which reflect the malignant characteristics of the tumor based on histopathological and molecular features. Lower grade diffuse gliomas (LGGs) (WHO Grade II-III) have fewer malignant characteristics than high-grade gliomas (WHO Grade IV), and a better clinical prognosis, however, accurate discrimination of overall survival (OS) remains a challenge...
2017: Journal of Pathology Informatics
https://www.readbyqxmd.com/read/28374077/machine-learning-based-analysis-of-mr-radiomics-can-help-to-improve-the-diagnostic-performance-of-pi-rads-v2-in-clinically-relevant-prostate-cancer
#18
Jing Wang, Chen-Jiang Wu, Mei-Ling Bao, Jing Zhang, Xiao-Ning Wang, Yu-Dong Zhang
OBJECTIVE: To investigate whether machine learning-based analysis of MR radiomics can help improve the performance PI-RADS v2 in clinically relevant prostate cancer (PCa). METHODS: This IRB-approved study included 54 patients with PCa undergoing multi-parametric (mp) MRI before prostatectomy. Imaging analysis was performed on 54 tumours, 47 normal peripheral (PZ) and 48 normal transitional (TZ) zone based on histological-radiological correlation. Mp-MRI was scored via PI-RADS, and quantified by measuring radiomic features...
April 3, 2017: European Radiology
https://www.readbyqxmd.com/read/28372104/restoring-speech-following-total-removal-of-the-larynx-by-a-learned-transformation-from-sensor-data-to-acoustics
#19
James M Gilbert, Jose A Gonzalez, Lam A Cheah, Stephen R Ell, Phil Green, Roger K Moore, Ed Holdsworth
Total removal of the larynx may be required to treat laryngeal cancer: speech is lost. This article shows that it may be possible to restore speech by sensing movement of the remaining speech articulators and use machine learning algorithms to derive a transformation to convert this sensor data into an acoustic signal. The resulting "silent speech," which may be delivered in real time, is intelligible and sounds natural. The identity of the speaker is recognisable. The sensing technique involves attaching small, unobtrusive magnets to the lips and tongue and monitoring changes in the magnetic field induced by their movement...
March 2017: Journal of the Acoustical Society of America
https://www.readbyqxmd.com/read/28365546/joint-multiple-fully-connected-convolutional-neural-network-with-extreme-learning-machine-for-hepatocellular-carcinoma-nuclei-grading
#20
Siqi Li, Huiyan Jiang, Wenbo Pang
Accurate cell grading of cancerous tissue pathological image is of great importance in medical diagnosis and treatment. This paper proposes a joint multiple fully connected convolutional neural network with extreme learning machine (MFC-CNN-ELM) architecture for hepatocellular carcinoma (HCC) nuclei grading. First, in preprocessing stage, each grayscale image patch with the fixed size is obtained using center-proliferation segmentation (CPS) method and the corresponding labels are marked under the guidance of three pathologists...
March 22, 2017: Computers in Biology and Medicine
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