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https://www.readbyqxmd.com/read/28734529/ultrasound-based-differentiation-of-malignant-and-benign-thyroid-nodules-an-extreme-learning-machine-approach
#1
Jianfu Xia, Huiling Chen, Qiang Li, Minda Zhou, Limin Chen, Zhennao Cai, Yang Fang, Hong Zhou
BACKGROUND AND OBJECTIVES: It is important to be able to accurately distinguish between benign and malignant thyroid nodules in order to make appropriate clinical decisions. The purpose of this study was to improve the effectiveness and efficiency for discriminating the malignant from benign thyroid cancers based on the Ultrasonography (US) features. METHODS: There were 114 benign nodules in 106 patients (82 women and 24 men) and 89 malignant nodules in 81 patients (69 women and 12 men) included in this study...
August 2017: Computer Methods and Programs in Biomedicine
https://www.readbyqxmd.com/read/28727228/actin-gamma-1-a-new-skin-cancer-pathogenic-gene-identified-by-the-biological-feature-based-classification
#2
Xinqian Dong, Yingsheng Han, Zhen Sun, Junlong Xu
Skin cancer is the most common form of cancer that accounting for at least 40% of cancer cases around the world. This study aimed to identify skin cancer-related biological features and predict skin cancer candidate genes by employing machine learning based on biological features of known skin cancer genes. The known skin cancer-related genes were fetched from database and encoded by the enrichment scores of gene ontology and pathways. The optimal features of the skin cancer related genes were selected with a series of feature selection methods, such as mRMR, IFS, and Random Forest algorithm...
July 20, 2017: Journal of Cellular Biochemistry
https://www.readbyqxmd.com/read/28723659/data-driven-analysis-of-immune-infiltrate-in-a-large-cohort-of-breast-cancer-and-its-association-with-disease-progression-er-activity-and-genomic-complexity
#3
Ruth Dannenfelser, Marianne Nome, Andliena Tahiri, Josie Ursini-Siegel, Hans Kristian Moen Vollan, Vilde D Haakensen, Åslaug Helland, Bjørn Naume, Carlos Caldas, Anne-Lise Børresen-Dale, Vessela N Kristensen, Olga G Troyanskaya
The tumor microenvironment is now widely recognized for its role in tumor progression, treatment response, and clinical outcome. The intratumoral immunological landscape, in particular, has been shown to exert both pro-tumorigenic and anti-tumorigenic effects. Identifying immunologically active or silent tumors may be an important indication for administration of therapy, and detecting early infiltration patterns may uncover factors that contribute to early risk. Thus far, direct detailed studies of the cell composition of tumor infiltration have been limited; with some studies giving approximate quantifications using immunohistochemistry and other small studies obtaining detailed measurements by isolating cells from excised tumors and sorting them using flow cytometry...
July 7, 2017: Oncotarget
https://www.readbyqxmd.com/read/28710752/ultrasound-imaging-of-apoptosis-spectroscopic-detection-of-dna-damage-effects-in-vivo
#4
Hadi Tadayyon, Mehrdad J Gangeh, Roxana Vlad, Michael C Kolios, Gregory J Czarnota
In this chapter, we describe two new methodologies: (1) application of high-frequency ultrasound spectroscopy for in vivo detection of cancer cell death in small animal models, and (2) extension of ultrasound spectroscopy to the lower frequency range (i.e., 1-10 MHz range) for the detection of cell death in vivo in preclinical and clinical settings. Experiments using tumor xenografts in mice and cancer treatments based on chemotherapy are described. Finally, we describe how one can detect cancer response to treatment in patients noninvasively early (within 1 week of treatment initiation) using low-frequency ultrasound spectroscopic imaging and advanced machine learning techniques...
2017: Methods in Molecular Biology
https://www.readbyqxmd.com/read/28705738/identification-of-novel-potential-scaffold-for-class-i-hdacs-inhibition-an-in-silico-protocol-based-on-virtual-screening-molecular-dynamics-mathematical-analysis-and-machine-learning
#5
Cong Fan, Yanxin Huang
Histone deacetylases (HDACs) family has been widely reported as an important class of enzyme targets for cancer therapy. Much effort has been made in discovery of novel scaffolds for HDACs inhibition besides existing hydroxamic acids, cyclic peptides, benzamides, and short-chain fatty acids. Herein we set up an in-silico protocol which not only could detect potential Zn(2+) chelation bonds but also still adopted non-bonded model to be effective in discovery of Class I HDACs inhibitors, with little human's subjective visual judgment involved...
July 10, 2017: Biochemical and Biophysical Research Communications
https://www.readbyqxmd.com/read/28690670/sparse-representation-based-multi-instance-learning-for-breast-ultrasound-image-classification
#6
Lu Bing, Wei Wang
We propose a novel method based on sparse representation for breast ultrasound image classification under the framework of multi-instance learning (MIL). After image enhancement and segmentation, concentric circle is used to extract the global and local features for improving the accuracy in diagnosis and prediction. The classification problem of ultrasound image is converted to sparse representation based MIL problem. Each instance of a bag is represented as a sparse linear combination of all basis vectors in the dictionary, and then the bag is represented by one feature vector which is obtained via sparse representations of all instances within the bag...
2017: Computational and Mathematical Methods in Medicine
https://www.readbyqxmd.com/read/28690394/a-software-application-for-mining-and-presenting-relevant-cancer-clinical-trials-per-cancer-mutation
#7
Lisa M Gandy, Jordan Gumm, Amanda L Blackford, Elana J Fertig, Luis A Diaz
ClinicalTrials.org is a popular portal which physicians use to find clinical trials for their patients. However, the current setup of ClinicalTrials.org makes it difficult for oncologists to locate clinical trials for patients based on mutational status. We present CTMine, a system that mines ClinicalTrials.org for clinical trials per cancer mutation and displays the trials in a user-friendly Web application. The system currently lists clinical trials for 6 common genes (ALK, BRAF, ERBB2, EGFR, KIT, and KRAS)...
2017: Cancer Informatics
https://www.readbyqxmd.com/read/28686281/identification-of-microrna-precursors-using-reduced-and-hybrid-features
#8
Asad Khan, Sajid Shah, Fazli Wahid, Fiaz Gul Khan, Saima Jabeen
MicroRNAs (also called miRNAs) are a group of short non-coding RNA molecules. They play a vital role in the gene expression of transcriptional and post-transcriptional processes. However, abnormality of their expression has been observed in cancer, heart diseases and nervous system disorders. Therefore for basic research and microRNA based therapy, it is imperative to separate real pre-miRNAs from false ones (hairpin sequences similar to pre-miRNA stem loops). Different conservation and machine learning methods have been applied for the identification of miRNAs...
July 25, 2017: Molecular BioSystems
https://www.readbyqxmd.com/read/28685785/identification-of-human-flap-endonuclease-1-fen1-inhibitors-using-a-machine-learning-based-consensus-virtual-screening
#9
Amit Laxmikant Deshmukh, Sharat Chandra, Deependra Kumar Singh, Mohammad Imran Siddiqi, Dibyendu Banerjee
Human Flap endonuclease1 (FEN1) is an enzyme that is indispensable for DNA replication and repair processes and inhibition of its Flap cleavage activity results in increased cellular sensitivity to DNA damaging agents (cisplatin, temozolomide, MMS, etc.), with the potential to improve cancer prognosis. Reports of the high expression levels of FEN1 in several cancer cells support the idea that FEN1 inhibitors may target cancer cells with minimum side effects to normal cells. In this study, we used large publicly available, high-throughput screening data of small molecule compounds targeted against FEN1...
July 25, 2017: Molecular BioSystems
https://www.readbyqxmd.com/read/28678778/an-immunogenic-personal-neoantigen-vaccine-for-patients-with-melanoma
#10
Patrick A Ott, Zhuting Hu, Derin B Keskin, Sachet A Shukla, Jing Sun, David J Bozym, Wandi Zhang, Adrienne Luoma, Anita Giobbie-Hurder, Lauren Peter, Christina Chen, Oriol Olive, Todd A Carter, Shuqiang Li, David J Lieb, Thomas Eisenhaure, Evisa Gjini, Jonathan Stevens, William J Lane, Indu Javeri, Kaliappanadar Nellaiappan, Andres M Salazar, Heather Daley, Michael Seaman, Elizabeth I Buchbinder, Charles H Yoon, Maegan Harden, Niall Lennon, Stacey Gabriel, Scott J Rodig, Dan H Barouch, Jon C Aster, Gad Getz, Kai Wucherpfennig, Donna Neuberg, Jerome Ritz, Eric S Lander, Edward F Fritsch, Nir Hacohen, Catherine J Wu
Effective anti-tumour immunity in humans has been associated with the presence of T cells directed at cancer neoantigens, a class of HLA-bound peptides that arise from tumour-specific mutations. They are highly immunogenic because they are not present in normal tissues and hence bypass central thymic tolerance. Although neoantigens were long-envisioned as optimal targets for an anti-tumour immune response, their systematic discovery and evaluation only became feasible with the recent availability of massively parallel sequencing for detection of all coding mutations within tumours, and of machine learning approaches to reliably predict those mutated peptides with high-affinity binding of autologous human leukocyte antigen (HLA) molecules...
July 13, 2017: Nature
https://www.readbyqxmd.com/read/28676295/deep-convolutional-neural-networks-for-automatic-classification-of-gastric-carcinoma-using-whole-slide-images-in-digital-histopathology
#11
Harshita Sharma, Norman Zerbe, Iris Klempert, Olaf Hellwich, Peter Hufnagl
Deep learning using convolutional neural networks is an actively emerging field in histological image analysis. This study explores deep learning methods for computer-aided classification in H&E stained histopathological whole slide images of gastric carcinoma. An introductory convolutional neural network architecture is proposed for two computerized applications, namely, cancer classification based on immunohistochemical response and necrosis detection based on the existence of tumor necrosis in the tissue...
June 16, 2017: Computerized Medical Imaging and Graphics: the Official Journal of the Computerized Medical Imaging Society
https://www.readbyqxmd.com/read/28675381/matched-computed-tomography-segmentation-and-demographic-data-for-oropharyngeal-cancer-radiomics-challenges
#12
(no author information available yet)
Cancers arising from the oropharynx have become increasingly more studied in the past few years, as they are now epidemic domestically. These tumors are treated with definitive (chemo)radiotherapy, and have local recurrence as a primary mode of clinical failure. Recent data suggest that 'radiomics', or extraction of image texture analysis to generate mineable quantitative data from medical images, can reflect phenotypes for various cancers. Several groups have shown that developed radiomic signatures, in head and neck cancers, can be correlated with survival outcomes...
July 4, 2017: Scientific Data
https://www.readbyqxmd.com/read/28666416/rgife-a-ranked-guided-iterative-feature-elimination-heuristic-for-the-identification-of-biomarkers
#13
Nicola Lazzarini, Jaume Bacardit
BACKGROUND: Current -omics technologies are able to sense the state of a biological sample in a very wide variety of ways. Given the high dimensionality that typically characterises these data, relevant knowledge is often hidden and hard to identify. Machine learning methods, and particularly feature selection algorithms, have proven very effective over the years at identifying small but relevant subsets of variables from a variety of application domains, including -omics data. Many methods exist with varying trade-off between the size of the identified variable subsets and the predictive power of such subsets...
June 30, 2017: BMC Bioinformatics
https://www.readbyqxmd.com/read/28665416/a-gene-expression-signature-distinguishes-innate-response-and-resistance-to-proteasome-inhibitors-in-multiple-myeloma
#14
A K Mitra, T Harding, U K Mukherjee, J S Jang, Y Li, R HongZheng, J Jen, P Sonneveld, S Kumar, W M Kuehl, V Rajkumar, B Van Ness
Extensive interindividual variation in response to chemotherapy is a major stumbling block in achieving desirable efficacy in the treatment of cancers, including multiple myeloma (MM). In this study, our goal was to develop a gene expression signature that predicts response specific to proteasome inhibitor (PI) treatment in MM. Using a well-characterized panel of human myeloma cell lines (HMCLs) representing the biological and genetic heterogeneity of MM, we created an in vitro chemosensitivity profile in response to treatment with the four PIs bortezomib, carfilzomib, ixazomib and oprozomib as single agents...
June 30, 2017: Blood Cancer Journal
https://www.readbyqxmd.com/read/28663073/a-study-of-the-suitability-of-autoencoders-for-preprocessing-data-in-breast-cancer-experimentation
#15
Laura Macías-García, José María Luna-Romera, Jorge García-Gutiérrez, María Del Mar Martínez-Ballesteros, José C Riquelme-Santos, Ricardo González-Cámpora
Breast cancer is the most common cause of cancer death in women. Today, post-transcriptional protein products of the genes involved in breast cancer can be identified by immunohistochemistry. However, this method has problems arising from the intra-observer and inter-observer variability in the assessment of pathologic variables, which may result in misleading conclusions. Using an optimal selection of preprocessing techniques may help to reduce observer variability. Deep learning has emerged as a powerful technique for any tasks related to machine learning such as classification and regression...
June 26, 2017: Journal of Biomedical Informatics
https://www.readbyqxmd.com/read/28659176/isown-accurate-somatic-mutation-identification-in-the-absence-of-normal-tissue-controls
#16
Irina Kalatskaya, Quang M Trinh, Melanie Spears, John D McPherson, John M S Bartlett, Lincoln Stein
BACKGROUND: A key step in cancer genome analysis is the identification of somatic mutations in the tumor. This is typically done by comparing the genome of the tumor to the reference genome sequence derived from a normal tissue taken from the same donor. However, there are a variety of common scenarios in which matched normal tissue is not available for comparison. RESULTS: In this work, we describe an algorithm to distinguish somatic single nucleotide variants (SNVs) in next-generation sequencing data from germline polymorphisms in the absence of normal samples using a machine learning approach...
June 29, 2017: Genome Medicine
https://www.readbyqxmd.com/read/28659051/prediction-of-pathologic-femoral-fractures-in-patients-with-lung-cancer-using-machine-learning-algorithms-comparison-of-computed-tomography-based-radiological-features-with-clinical-features-versus-without-clinical-features
#17
Eunsun Oh, Sung Wook Seo, Young Cheol Yoon, Dong Wook Kim, Sunyoung Kwon, Sungroh Yoon
PURPOSE: The purpose of this article is to compare the predictive power of two models trained with computed tomography (CT)-based radiological features and both CT-based radiological and clinical features for pathologic femoral fractures in patients with lung cancer using machine learning algorithms. METHODS: Between January 2010 and December 2014, 315 lung cancer patients with metastasis to the femur were included. Among them, 84 patients who underwent CT scan and were followed up for more than 3 months were enrolled...
May 2017: Journal of Orthopaedic Surgery
https://www.readbyqxmd.com/read/28656455/malignancy-detection-on-mammography-using-dual-deep-convolutional-neural-networks-and-genetically-discovered-false-color-input-enhancement
#18
Philip Teare, Michael Fishman, Oshra Benzaquen, Eyal Toledano, Eldad Elnekave
Breast cancer is the most prevalent malignancy in the US and the third highest cause of cancer-related mortality worldwide. Regular mammography screening has been attributed with doubling the rate of early cancer detection over the past three decades, yet estimates of mammographic accuracy in the hands of experienced radiologists remain suboptimal with sensitivity ranging from 62 to 87% and specificity from 75 to 91%. Advances in machine learning (ML) in recent years have demonstrated capabilities of image analysis which often surpass those of human observers...
June 27, 2017: Journal of Digital Imaging: the Official Journal of the Society for Computer Applications in Radiology
https://www.readbyqxmd.com/read/28652331/dna-methylation-markers-for-diagnosis-and-prognosis-of-common-cancers
#19
Xiaoke Hao, Huiyan Luo, Michal Krawczyk, Wei Wei, Wenqiu Wang, Juan Wang, Ken Flagg, Jiayi Hou, Heng Zhang, Shaohua Yi, Maryam Jafari, Danni Lin, Christopher Chung, Bennett A Caughey, Gen Li, Debanjan Dhar, William Shi, Lianghong Zheng, Rui Hou, Jie Zhu, Liang Zhao, Xin Fu, Edward Zhang, Charlotte Zhang, Jian-Kang Zhu, Michael Karin, Rui-Hua Xu, Kang Zhang
The ability to identify a specific cancer using minimally invasive biopsy holds great promise for improving the diagnosis, treatment selection, and prediction of prognosis in cancer. Using whole-genome methylation data from The Cancer Genome Atlas (TCGA) and machine learning methods, we evaluated the utility of DNA methylation for differentiating tumor tissue and normal tissue for four common cancers (breast, colon, liver, and lung). We identified cancer markers in a training cohort of 1,619 tumor samples and 173 matched adjacent normal tissue samples...
July 11, 2017: Proceedings of the National Academy of Sciences of the United States of America
https://www.readbyqxmd.com/read/28650678/an-integrative-approach-for-identifying-network-biomarkers-of-breast-cancer-subtypes-using-genomic-interactomic-and-transcriptomic-data
#20
Forough Firoozbakht, Iman Rezaeian, Michele D'agnillo, Lisa Porter, Luis Rueda, Alioune Ngom
Breast cancer is a complex disease that can be classified into at least 10 different molecular subtypes. Appropriate diagnosis of specific subtypes is critical for ensuring the best possible patient treatment and response to therapy. Current computational methods for determining the subtypes are based on identifying differentially expressed genes (i.e., biomarkers) that can best discriminate the subtypes. Such approaches, however, are known to be unreliable since they yield different biomarker sets when applied to data sets from different studies...
August 2017: Journal of Computational Biology: a Journal of Computational Molecular Cell Biology
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