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https://www.readbyqxmd.com/read/28332438/quantitative-structure-activity-relationship-analysis-and-virtual-screening-studies-for-identifying-hdac2-inhibitors-from-known-hdac-bioactive-chemical-libraries
#1
H Pham-The, G Casañola-Martin, K Diéguez-Santana, N Nguyen-Hai, N T Ngoc, L Vu-Duc, H Le-Thi-Thu
Histone deacetylases (HDAC) are emerging as promising targets in cancer, neuronal diseases and immune disorders. Computational modelling approaches have been widely applied for the virtual screening and rational design of novel HDAC inhibitors. In this study, different machine learning (ML) techniques were applied for the development of models that accurately discriminate HDAC2 inhibitors form non-inhibitors. The obtained models showed encouraging results, with the global accuracy in the external set ranging from 0...
March 2017: SAR and QSAR in Environmental Research
https://www.readbyqxmd.com/read/28325604/early-prediction-of-radiotherapy-induced-parotid-shrinkage-and-toxicity-based-on-ct-radiomics-and-fuzzy-classification
#2
Marco Pota, Elisa Scalco, Giuseppe Sanguineti, Alessia Farneti, Giovanni Mauro Cattaneo, Giovanna Rizzo, Massimo Esposito
MOTIVATION: Patients under radiotherapy for head-and-neck cancer often suffer of long-term xerostomia, and/or consistent shrinkage of parotid glands. In order to avoid these drawbacks, adaptive therapy can be planned for patients at risk, if the prediction is obtained timely, before or during the early phase of treatment. Artificial intelligence can address the problem, by learning from examples and building classification models. In particular, fuzzy logic has shown its suitability for medical applications, in order to manage uncertain data, and to build transparent rule-based classifiers...
March 18, 2017: Artificial Intelligence in Medicine
https://www.readbyqxmd.com/read/28319275/mrf-ann-a-machine-learning-approach-for-automated-er-scoring-of-breast-cancer-immunohistochemical-images
#3
T Mungle, S Tewary, D K DAS, I Arun, B Basak, S Agarwal, R Ahmed, S Chatterjee, C Chakraborty
Molecular pathology, especially immunohistochemistry, plays an important role in evaluating hormone receptor status along with diagnosis of breast cancer. Time-consumption and inter-/intraobserver variability are major hindrances for evaluating the receptor score. In view of this, the paper proposes an automated Allred Scoring methodology for estrogen receptor (ER). White balancing is used to normalize the colour image taking into consideration colour variation during staining in different labs. Markov random field model with expectation-maximization optimization is employed to segment the ER cells...
March 20, 2017: Journal of Microscopy
https://www.readbyqxmd.com/read/28317907/analysis-of-spatial-heterogeneity-in-normal-epithelium-and-preneoplastic-alterations-in-mouse-prostate-tumor-models
#4
Mira Valkonen, Pekka Ruusuvuori, Kimmo Kartasalo, Matti Nykter, Tapio Visakorpi, Leena Latonen
Cancer involves histological changes in tissue, which is of primary importance in pathological diagnosis and research. Automated histological analysis requires ability to computationally separate pathological alterations from normal tissue with all its variables. On the other hand, understanding connections between genetic alterations and histological attributes requires development of enhanced analysis methods suitable also for small sample sizes. Here, we set out to develop computational methods for early detection and distinction of prostate cancer-related pathological alterations...
March 20, 2017: Scientific Reports
https://www.readbyqxmd.com/read/28295386/an-integrated-segmentation-and-shape-based-classification-scheme-for-distinguishing-adenocarcinomas-from-granulomas-on-lung-ct
#5
Mehdi Alilou, Niha Beig, Mahdi Orooji, Anant Madabhushi, Prabhakar Rajiah, Michael Yang, Robert Gilkeson, Philip Linden, Vamsidhar Velcheti, Sagar Rakshit, Niyoti Reddy, Frank Jacono
PURPOSE: Distinguishing between benign granulmoas and adenocarcinomas is confounded by their similar visual appearance on routine CT scans. Unfortunately, owing to the inability to discriminate these lesions radigraphically, many patients with benign granulomas are subjected to unnecessary surgical wedge resections and biopsies for pathologic confirmation of cancer presence or absence. This suggests the need for improved computerized characterization of these nodules in order to distinguish between these two classes of lesions on CT scans...
March 14, 2017: Medical Physics
https://www.readbyqxmd.com/read/28292312/comprehensive-discovery-of-subsample-gene-expression-components-by-information-explanation-therapeutic-implications-in-cancer
#6
Shirley Pepke, Greg Ver Steeg
BACKGROUND: De novo inference of clinically relevant gene function relationships from tumor RNA-seq remains a challenging task. Current methods typically either partition patient samples into a few subtypes or rely upon analysis of pairwise gene correlations that will miss some groups in noisy data. Leveraging higher dimensional information can be expected to increase the power to discern targetable pathways, but this is commonly thought to be an intractable computational problem. METHODS: In this work we adapt a recently developed machine learning algorithm for sensitive detection of complex gene relationships...
March 15, 2017: BMC Medical Genomics
https://www.readbyqxmd.com/read/28292266/microrna-categorization-using-sequence-motifs-and-k-mers
#7
Malik Yousef, Waleed Khalifa, İlhan Erkin Acar, Jens Allmer
BACKGROUND: Post-transcriptional gene dysregulation can be a hallmark of diseases like cancer and microRNAs (miRNAs) play a key role in the modulation of translation efficiency. Known pre-miRNAs are listed in miRBase, and they have been discovered in a variety of organisms ranging from viruses and microbes to eukaryotic organisms. The computational detection of pre-miRNAs is of great interest, and such approaches usually employ machine learning to discriminate between miRNAs and other sequences...
March 14, 2017: BMC Bioinformatics
https://www.readbyqxmd.com/read/28292252/prediction-and-identification-of-kr%C3%A3-ppel-like-transcription-factors-by-machine-learning-method
#8
Zhijun Liao, Xinrui Wang, Xingyong Chend, Quan Zoub
The Krüppel-like factors (KLFs)are a family of containing zinc finger(ZF) motif transcription factors with 18 members in human genome.KLFs possess various physiological functionrelating withnumerous cancers and other diseases. Here we perform a binary-class classification of KLFs and non-KLFs and conserved motifs analysis of human KLFs. We search and cluster the protein sequences andseparate them into training datasetand test dataset(containing only negative samples), after extracting the 188-dimensional(188D) feature vectors we carry out category with four classifiers(GBDT, libSVM, RF, and k-NN), and use 10-fold cross-validation...
March 13, 2017: Combinatorial Chemistry & High Throughput Screening
https://www.readbyqxmd.com/read/28290067/expert-system-classifier-for-adaptive-radiation-therapy-in-prostate-cancer
#9
Gabriele Guidi, Nicola Maffei, Claudio Vecchi, Giovanni Gottardi, Alberto Ciarmatori, Grazia Maria Mistretta, Ercole Mazzeo, Patrizia Giacobazzi, Frank Lohr, Tiziana Costi
A classifier-based expert system was developed to compare delivered and planned radiation therapy in prostate cancer patients. Its aim is to automatically identify patients that can benefit from an adaptive treatment strategy. The study predominantly addresses dosimetric uncertainties and critical issues caused by motion of hollow organs. 1200 MVCT images of 38 prostate adenocarcinoma cases were analyzed. An automatic daily re-contouring of structures (i.e. rectum, bladder and femoral heads), rigid/deformable registration and dose warping was carried out to simulate dose and volume variations during therapy...
March 13, 2017: Australasian Physical & Engineering Sciences in Medicine
https://www.readbyqxmd.com/read/28283691/-modulation-of-the-intestinal-microbiota-by-nutritional-interventions
#10
S Derer, H Lehnert, C Sina, A E Wagner
Humans live in symbiosis with billions of commensal bacteria. The so-called microbiota live on different biological interfaces such as the skin, the urogenital tract and the gastrointestinal tract. Commensal bacteria replace potentially pathogenic microbes, synthesize vitamins and ferment dietary fibre. An imbalance in the bacterial composition of the intestinal microbiota has been associated with various diseases including gut-associated disorders such as inflammatory bowel diseases, colorectal cancer and nonalcoholic fatty liver disease...
March 10, 2017: Der Internist
https://www.readbyqxmd.com/read/28282591/a-novel-computer-aided-diagnosis-system-for-breast-mri-based-on-feature-selection-and-ensemble-learning
#11
Wei Lu, Zhe Li, Jinghui Chu
Breast cancer is a common cancer among women. With the development of modern medical science and information technology, medical imaging techniques have an increasingly important role in the early detection and diagnosis of breast cancer. In this paper, we propose an automated computer-aided diagnosis (CADx) framework for magnetic resonance imaging (MRI). The scheme consists of an ensemble of several machine learning-based techniques, including ensemble under-sampling (EUS) for imbalanced data processing, the Relief algorithm for feature selection, the subspace method for providing data diversity, and Adaboost for improving the performance of base classifiers...
March 6, 2017: Computers in Biology and Medicine
https://www.readbyqxmd.com/read/28278461/automatic-quantification-of-tumour-hypoxia-from-multi-modal-microscopy-images-using-weakly-supervised-learning-methods
#12
Gustavo Carneiro, Tingying Peng, Christine Bayer, Nassir Navab
In recently published clinical trial results, hypoxia-modified therapies have shown to provide more positive outcomes to cancer patients, compared with standard cancer treatments. The development and validation of these hypoxia-modified therapies depend on an effective way of measuring tumour hypoxia, but a standardised measurement is currently unavailable in clinical practice. Different types of manual measurements have been proposed in clinical research, but in this paper we focus on a recently published approach that quantifies the number and proportion of hypoxic regions using high resolution (immuno- ) fluorescence (IF) and hematoxylin and eosin (HE) stained images of a histological specimen of a tumour...
March 2, 2017: IEEE Transactions on Medical Imaging
https://www.readbyqxmd.com/read/28273809/proteome-analysis-of-human-follicular-thyroid-cancer-cells-exposed-to-the-random-positioning-machine
#13
Johann Bauer, Sascha Kopp, Elisabeth Maria Schlagberger, Jirka Grosse, Jayashree Sahana, Stefan Riwaldt, Markus Wehland, Ronald Luetzenberg, Manfred Infanger, Daniela Grimm
Several years ago, we detected the formation of multicellular spheroids in experiments with human thyroid cancer cells cultured on the Random Positioning Machine (RPM), a ground-based model to simulate microgravity by continuously changing the orientation of samples. Since then, we have studied cellular mechanisms triggering the cells to leave a monolayer and aggregate to spheroids. Our work focused on spheroid-related changes in gene expression patterns, in protein concentrations, and in factors secreted to the culture supernatant during the period when growth is altered...
March 3, 2017: International Journal of Molecular Sciences
https://www.readbyqxmd.com/read/28272488/early-spatiotemporal-specific-changes-in-intermediate-signals-are-predictive-of-cytotoxic-sensitivity-to-tnf%C3%AE-and-co-treatments
#14
Lit-Hsin Loo, Nicola Michelle Bougen-Zhukov, Wei-Ling Cecilia Tan
Signaling pathways can generate different cellular responses to the same cytotoxic agents. Current quantitative models for predicting these differential responses are usually based on large numbers of intracellular gene products or signals at different levels of signaling cascades. Here, we report a study to predict cellular sensitivity to tumor necrosis factor alpha (TNFα) using high-throughput cellular imaging and machine-learning methods. We measured and compared 1170 protein phosphorylation events in a panel of human lung cancer cell lines based on different signals, subcellular regions, and time points within one hour of TNFα treatment...
March 8, 2017: Scientific Reports
https://www.readbyqxmd.com/read/28269155/automatic-classification-of-cancer-cells-in-multispectral-microscopic-images-of-lymph-node-samples
#15
Gali Zimmerman-Moreno, Irina Marin, Moshe Lindner, Iris Barshack, Yuval Garini, Eli Konen, Arnaldo Mayer
Histopathological analysis is crucial for the diagnosis of a large number of cancer types. A lot of progress has been made in the development of molecular based assays, but many of the cases still require the careful analysis of the stained tissue under a bright-field microscope and its analysis. This procedure is costly and time-consuming. We present a novel method for classification of cancer cells in lymph node images. It is based on the measurement of the spectral image of hematoxylin and eosin stained sample under the microscope and the analysis of the acquired data using state of the art machine learning techniques...
August 2016: Conference Proceedings: Annual International Conference of the IEEE Engineering in Medicine and Biology Society
https://www.readbyqxmd.com/read/28269014/learning-approaches-to-improve-prediction-of-drug-sensitivity-in-breast-cancer-patients
#16
Turki Turki, Zhi Wei
Predicting drug response to cancer disease is an important problem in modern clinical oncology that attracted increasing recent attention from various domains such as computational biology, machine learning, and data mining. Cancer patients respond differently to each cancer therapy owing to disease diversity, genetic factors, and environmental causes. Thus, oncologists aim to identify the effective therapies for cancer patients and avoid adverse drug reactions in patients. By predicting the drug response to cancer, oncologists gain full understanding of the effective treatments on each patient, which leads to better personalized treatment...
August 2016: Conference Proceedings: Annual International Conference of the IEEE Engineering in Medicine and Biology Society
https://www.readbyqxmd.com/read/28268817/detection-of-mitotic-nuclei-in-breast-histopathology-images-using-localized-acm-and-random-kitchen-sink-based-classifier
#17
K Sabeena Beevi, Madhu S Nair, G R Bindu
The exact measure of mitotic nuclei is a crucial parameter in breast cancer grading and prognosis. This can be achieved by improving the mitotic detection accuracy by careful design of segmentation and classification techniques. In this paper, segmentation of nuclei from breast histopathology images are carried out by Localized Active Contour Model (LACM) utilizing bio-inspired optimization techniques in the detection stage, in order to handle diffused intensities present along object boundaries. Further, the application of a new optimal machine learning algorithm capable of classifying strong non-linear data such as Random Kitchen Sink (RKS), shows improved classification performance...
August 2016: Conference Proceedings: Annual International Conference of the IEEE Engineering in Medicine and Biology Society
https://www.readbyqxmd.com/read/28254081/learning-mri-based-classification-models-for-mgmt-methylation-status-prediction-in-glioblastoma
#18
Vasileios G Kanas, Evangelia I Zacharaki, Ginu A Thomas, Pascal O Zinn, Vasileios Megalooikonomou, Rivka R Colen
BACKGROUND AND OBJECTIVE: The O(6)-methylguanine-DNA-methyltransferase (MGMT) promoter methylation has been shown to be associated with improved outcomes in patients with glioblastoma (GBM) and may be a predictive marker of sensitivity to chemotherapy. However, determination of the MGMT promoter methylation status requires tissue obtained via surgical resection or biopsy. The aim of this study was to assess the ability of quantitative and qualitative imaging variables in predicting MGMT methylation status noninvasively...
March 2017: Computer Methods and Programs in Biomedicine
https://www.readbyqxmd.com/read/28228010/selective-fusion-of-heterogeneous-classifiers-for-predicting-substrates-of-membrane-transporters
#19
Naeem Shaikh, Mahesh Sharma, Prabha Garg
Membrane transporters play a crucial role in determining fate of administered drugs in a biological system. Early identification of plausible transporters for a drug molecule can provide insights into its therapeutic, pharmacokinetic, and toxicological profiles. In the present study, predictive models for classifying small molecules into substrates and nonsubstrates of various pharmaceutically important membrane transporters were developed using quantitative structure-activity relationship (QSAR) and proteochemometric (PCM) approaches...
March 6, 2017: Journal of Chemical Information and Modeling
https://www.readbyqxmd.com/read/28227384/automatic-classification-of-cancer-cells-in-multispectral-microscopic-images-of-lymph-node-samples
#20
Gali Zimmerman-Moreno, Irina Marin, Moshe Lindner, Iris Barshack, Yuval Garini, Eli Konen, Arnaldo Mayer, Gali Zimmerman-Moreno, Irina Marin, Moshe Lindner, Iris Barshack, Yuval Garini, Eli Konen, Arnaldo Mayer, Moshe Lindner, Yuval Garini, Gali Zimmerman-Moreno, Arnaldo Mayer, Eli Konen, Iris Barshack, Irina Marin
Histopathological analysis is crucial for the diagnosis of a large number of cancer types. A lot of progress has been made in the development of molecular based assays, but many of the cases still require the careful analysis of the stained tissue under a bright-field microscope and its analysis. This procedure is costly and time-consuming. We present a novel method for classification of cancer cells in lymph node images. It is based on the measurement of the spectral image of hematoxylin and eosin stained sample under the microscope and the analysis of the acquired data using state of the art machine learning techniques...
August 2016: Conference Proceedings: Annual International Conference of the IEEE Engineering in Medicine and Biology Society
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