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https://www.readbyqxmd.com/read/28222363/very-short-term-reactive-forecasting-of-the-solar-ultraviolet-index-using-an-extreme-learning-machine-integrated-with-the-solar-zenith-angle
#1
Ravinesh C Deo, Nathan Downs, Alfio V Parisi, Jan F Adamowski, John M Quilty
Exposure to erythemally-effective solar ultraviolet radiation (UVR) that contributes to malignant keratinocyte cancers and associated health-risk is best mitigated through innovative decision-support systems, with global solar UV index (UVI) forecast necessary to inform real-time sun-protection behaviour recommendations. It follows that the UVI forecasting models are useful tools for such decision-making. In this study, a model for computationally-efficient data-driven forecasting of diffuse and global very short-term reactive (VSTR) (10-min lead-time) UVI, enhanced by drawing on the solar zenith angle (θs) data, was developed using an extreme learning machine (ELM) algorithm...
February 18, 2017: Environmental Research
https://www.readbyqxmd.com/read/28222333/immunoprofiling-as-a-predictor-of-patient-s-response-to-cancer-therapy-promises-and-challenges
#2
REVIEW
Daniel Bethmann, Zipei Feng, Bernard A Fox
Immune cell infiltration is common to many tumors and has been recognized by pathologists for more than 100 years. The application of digital imaging and objective assessment software allowed a concise determination of the type and quantity of immune cells and their location relative to the tumor and, in the case of colon cancer, characterized overall survival better than AJCC TNM staging. Subsequently, expression of PD-L1, by 50% or more tumor cells, identified NSCLC patients with double the response rate to anti-PD-1...
February 18, 2017: Current Opinion in Immunology
https://www.readbyqxmd.com/read/28192639/a-deep-learning-based-strategy-for-identifying-and-associating-mitotic-activity-with-gene-expression-derived-risk-categories-in-estrogen-receptor-positive-breast-cancers
#3
David Romo-Bucheli, Andrew Janowczyk, Hannah Gilmore, Eduardo Romero, Anant Madabhushi
The treatment and management of early stage estrogen receptor positive (ER+) breast cancer is hindered by the difficulty in identifying patients who require adjuvant chemotherapy in contrast to those that will respond to hormonal therapy. To distinguish between the more and less aggressive breast tumors, which is a fundamental criterion for the selection of an appropriate treatment plan, Oncotype DX (ODX) and other gene expression tests are typically employed. While informative, these gene expression tests are expensive, tissue destructive, and require specialized facilities...
February 13, 2017: Cytometry. Part A: the Journal of the International Society for Analytical Cytology
https://www.readbyqxmd.com/read/28185575/integration-of-metabolomics-lipidomics-and-clinical-data-using-a-machine-learning-method
#4
Animesh Acharjee, Zsuzsanna Ament, James A West, Elizabeth Stanley, Julian L Griffin
BACKGROUND: The recent pandemic of obesity and the metabolic syndrome (MetS) has led to the realisation that new drug targets are needed to either reduce obesity or the subsequent pathophysiological consequences associated with excess weight gain. Certain nuclear hormone receptors (NRs) play a pivotal role in lipid and carbohydrate metabolism and have been highlighted as potential treatments for obesity. This realisation started a search for NR agonists in order to understand and successfully treat MetS and associated conditions such as insulin resistance, dyslipidaemia, hypertension, hypertriglyceridemia, obesity and cardiovascular disease...
November 22, 2016: BMC Bioinformatics
https://www.readbyqxmd.com/read/28185545/mirnatip-a-som-based-mirna-target-interactions-predictor
#5
Antonino Fiannaca, Massimo La Rosa, Laura La Paglia, Riccardo Rizzo, Alfonso Urso
BACKGROUND: MicroRNAs (miRNAs) are small non-coding RNA sequences with regulatory functions to post-transcriptional level for several biological processes, such as cell disease progression and metastasis. MiRNAs interact with target messenger RNA (mRNA) genes by base pairing. Experimental identification of miRNA target is one of the major challenges in cancer biology because miRNAs can act as tumour suppressors or oncogenes by targeting different type of targets. The use of machine learning methods for the prediction of the target genes is considered a valid support to investigate miRNA functions and to guide related wet-lab experiments...
September 22, 2016: BMC Bioinformatics
https://www.readbyqxmd.com/read/28182647/performance-analysis-of-a-machine-learning-flagging-system-used-to-identify-a-group-of-individuals-at-a-high-risk-for-colorectal-cancer
#6
Yaron Kinar, Pinchas Akiva, Eran Choman, Revital Kariv, Varda Shalev, Bernard Levin, Steven A Narod, Ran Goshen
Individuals with colorectal cancer (CRC) have a tendency to intestinal bleeding which may result in mild to severe iron deficiency anemia, but for many colon cancer patients hematological abnormalities are subtle. The fecal occult blood test (FOBT) is used as a pre-screening test whereby those with a positive FOBT are referred to colonscopy. We sought to determine if information contained in the complete blood count (CBC) report coud be processed automatically and used to predict the presence of occult colorectal cancer (CRC) in the setting of a large health services plan...
2017: PloS One
https://www.readbyqxmd.com/read/28182277/improving-computer-aided-detection-assistance-in-breast-cancer-screening-by-removal-of-obviously-false-positive-findings
#7
Jan-Jurre Mordang, Albert Gubern-Mérida, Alessandro Bria, Francesco Tortorella, Gerard den Heeten, Nico Karssemeijer
PURPOSE: Computer-Aided Detection (CADe) systems for mammography screening still mark many false positives. This can cause that radiologists lose confidence in CADe, especially when many false positive are obviously not suspicious to them. In this study we focus on obvious false positives generated by microcalcification detection algorithms. METHODS: We aim at reducing the number of obvious false positive findings by adding an additional step in the detection method...
February 9, 2017: Medical Physics
https://www.readbyqxmd.com/read/28178889/v-elmpirnapred-identification-of-human-pirnas-by-the-voting-based-extreme-learning-machine-v-elm-with-a-new-hybrid-feature
#8
Cong Pian, Yuan-Yuan Chen, Jin Zhang, Zhi Chen, Guang-Le Zhang, Qiang Li, Tao Yang, Liang-Yun Zhang
Piwi-interacting RNAs (piRNAs) were recently discovered as endogenous small noncoding RNAs. Some recent research suggests that piRNAs may play an important role in cancer. So the precise identification of human piRNAs is a significant work. In this paper, we introduce a series of new features with 80 dimension called short sequence motifs (SSM). A hybrid feature vector with 1444 dimension can be formed by combining 1364 features of [Formula: see text]-mer strings and 80 features of SSM features. We optimize the 1444 dimension features using the feature score criterion (FSC) and list them in descending order according to the scores...
January 9, 2017: Journal of Bioinformatics and Computational Biology
https://www.readbyqxmd.com/read/28177885/aminopeptidase-a-initiates-tumorigenesis-and-enhances-tumor-cell-stemness-via-twist1-upregulation-in-colorectal-cancer
#9
Hui-Yu Chuang, Jeng-Kae Jiang, Muh-Hwa Yang, Hsei-Wei Wang, Ming-Chun Li, Chan-Yen Tsai, Yau-Yun Jhang, Jason C Huang
Metastasis accounts for the high mortality rate associated with colorectal cancer (CRC), but metastasis regulators are not fully understood. To identify a novel gene involved in tumor metastasis, we used oligonucleotide microarrays, transcriptome distance analyses, and machine learning algorithms to determine links between primary and metastatic colorectal cancers. Aminopeptidase A (APA; also known as ENPEP) was selected as our focus because its relationship with colorectal cancer requires clarification. Higher APA mRNA levels were observed in patients in advanced stages of cancer, suggesting a correlation between ENPEP and degree of malignancy...
February 3, 2017: Oncotarget
https://www.readbyqxmd.com/read/28166733/a-machine-learning-classifier-trained-on-cancer-transcriptomes-detects-nf1-inactivation-signal-in-glioblastoma
#10
Gregory P Way, Robert J Allaway, Stephanie J Bouley, Camilo E Fadul, Yolanda Sanchez, Casey S Greene
BACKGROUND: We have identified molecules that exhibit synthetic lethality in cells with loss of the neurofibromin 1 (NF1) tumor suppressor gene. However, recognizing tumors that have inactivation of the NF1 tumor suppressor function is challenging because the loss may occur via mechanisms that do not involve mutation of the genomic locus. Degradation of the NF1 protein, independent of NF1 mutation status, phenocopies inactivating mutations to drive tumors in human glioma cell lines. NF1 inactivation may alter the transcriptional landscape of a tumor and allow a machine learning classifier to detect which tumors will benefit from synthetic lethal molecules...
February 6, 2017: BMC Genomics
https://www.readbyqxmd.com/read/28155657/a-machine-learning-approach-for-the-identification-of-key-markers-involved-in-brain-development-from-single-cell-transcriptomic-data
#11
Yongli Hu, Takeshi Hase, Hui Peng Li, Shyam Prabhakar, Hiroaki Kitano, See Kiong Ng, Samik Ghosh, Lawrence Jin Kiat Wee
BACKGROUND: The ability to sequence the transcriptomes of single cells using single-cell RNA-seq sequencing technologies presents a shift in the scientific paradigm where scientists, now, are able to concurrently investigate the complex biology of a heterogeneous population of cells, one at a time. However, till date, there has not been a suitable computational methodology for the analysis of such intricate deluge of data, in particular techniques which will aid the identification of the unique transcriptomic profiles difference between the different cellular subtypes...
December 22, 2016: BMC Genomics
https://www.readbyqxmd.com/read/28152264/prediction-of-malignancy-by-a-radiomic-signature-from-contrast-agent-free-diffusion-mri-in-suspicious-breast-lesions-found-on-screening-mammography
#12
Sebastian Bickelhaupt, Daniel Paech, Philipp Kickingereder, Franziska Steudle, Wolfgang Lederer, Heidi Daniel, Michael Götz, Nils Gählert, Diana Tichy, Manuel Wiesenfarth, Frederik B Laun, Klaus H Maier-Hein, Heinz-Peter Schlemmer, David Bonekamp
PURPOSE: To assess radiomics as a tool to determine how well lesions found suspicious on breast cancer screening X-ray mammography can be categorized into malignant and benign with unenhanced magnetic resonance (MR) mammography with diffusion-weighted imaging and T2 -weighted sequences. MATERIALS AND METHODS: From an asymptomatic screening cohort, 50 women with mammographically suspicious findings were examined with contrast-enhanced breast MRI (ceMRI) at 1.5T. Out of this protocol an unenhanced, abbreviated diffusion-weighted imaging protocol (ueMRI) including T2 -weighted, (T2 w), diffusion-weighted imaging (DWI), and DWI with background suppression (DWIBS) sequences and corresponding apparent diffusion coefficient (ADC) maps were extracted...
February 2, 2017: Journal of Magnetic Resonance Imaging: JMRI
https://www.readbyqxmd.com/read/28141527/bosco-boosting-corrections-for-genome-wide-association-studies-with-imbalanced-samples
#13
Feng Bao, Yue Deng, Yanyu Zhao, Jinli Suo, Qionghai Dai
In genome-wide association studies (GWAS), the acquired sequential data may exhibit imbalance structure: abundant control vs. limited case samples. Such sample imbalance issue is particularly serious when investigating rare diseases or common diseases on rare populations. Conventional GWAS methods may suffer from severe statistic biases to the major group, leading to power losses in uncovering true suspicious loci. We introduce a boosting correction method termed as Bosco to deal with such imbalanced problem...
January 27, 2017: IEEE Transactions on Nanobioscience
https://www.readbyqxmd.com/read/28135256/characterizing-cell-subsets-using-marker-enrichment-modeling
#14
Kirsten E Diggins, Allison R Greenplate, Nalin Leelatian, Cara E Wogsland, Jonathan M Irish
Learning cell identity from high-content single-cell data presently relies on human experts. We present marker enrichment modeling (MEM), an algorithm that objectively describes cells by quantifying contextual feature enrichment and reporting a human- and machine-readable text label. MEM outperforms traditional metrics in describing immune and cancer cell subsets from fluorescence and mass cytometry. MEM provides a quantitative language to communicate characteristics of new and established cytotypes observed in complex tissues...
January 30, 2017: Nature Methods
https://www.readbyqxmd.com/read/28130689/comparison-of-machine-learning-methods-for-classifying-mediastinal-lymph-node-metastasis-of-non-small-cell-lung-cancer-from-18-f-fdg-pet-ct-images
#15
Hongkai Wang, Zongwei Zhou, Yingci Li, Zhonghua Chen, Peiou Lu, Wenzhi Wang, Wanyu Liu, Lijuan Yu
BACKGROUND: This study aimed to compare one state-of-the-art deep learning method and four classical machine learning methods for classifying mediastinal lymph node metastasis of non-small cell lung cancer (NSCLC) from (18)F-FDG PET/CT images. Another objective was to compare the discriminative power of the recently popular PET/CT texture features with the widely used diagnostic features such as tumor size, CT value, SUV, image contrast, and intensity standard deviation. The four classical machine learning methods included random forests, support vector machines, adaptive boosting, and artificial neural network...
December 2017: EJNMMI Research
https://www.readbyqxmd.com/read/28122525/an-approach-to-forecast-human-cancer-by-profiling-microrna-expressions-from-ngs-data
#16
A Salim, R Amjesh, S S Vinod Chandra
BACKGROUND: microRNAs are single-stranded non-coding RNA sequences of 18 - 24 nucleotides in length. They play an important role in post-transcriptional regulation of gene expression. Evidences of microRNA acting as promoter/suppressor of several diseases including cancer are being unveiled. Recent studies have shown that microRNAs are differentially expressed in disease states when compared with that of normal states. Profiling of microRNA is a good measure to estimate the differences in expression levels, which can be further utilized to understand the progression of any associated disease...
January 25, 2017: BMC Cancer
https://www.readbyqxmd.com/read/28119391/quantifying-queensland-patients-with-cancer-health-service-usage-and-costs-study-protocol
#17
Emily Callander, Stephanie M Topp, Sarah Larkins, Sabe Sabesan, Nicole Bates
INTRODUCTION: The overall mortality rate for cancer has declined in Australia. However, socioeconomic inequalities exist and the out-of-pocket costs incurred by patients in Australia are high compared with some European countries. There is currently no readily available data set to provide a systematic means of measuring the out-of-pocket costs incurred by patients with cancer within Australia. The primary aim of the project is to quantify the direct out-of-pocket healthcare expenditure of individuals in the state of Queensland, who are diagnosed with cancer...
January 24, 2017: BMJ Open
https://www.readbyqxmd.com/read/28113887/aggnet-deep-learning-from-crowds-for-mitosis-detection-in-breast-cancer-histology-images
#18
Shadi Albarqouni, Christoph Baur, Felix Achilles, Vasileios Belagiannis, Stefanie Demirci, Nassir Navab
The lack of publicly available ground-truth data has been identified as the major challenge for transferring recent developments in deep learning to the biomedical imaging domain. Though crowdsourcing has enabled annotation of large scale databases for real world images, its application for biomedical purposes requires a deeper understanding and hence, more precise definition of the actual annotation task. The fact that expert tasks are being outsourced to non-expert users may lead to noisy annotations introducing disagreement between users...
February 11, 2016: IEEE Transactions on Medical Imaging
https://www.readbyqxmd.com/read/28113800/computer-aided-classification-of-gastrointestinal-lesions-in-regular-colonoscopy
#19
Pablo Mesejo, Daniel Pizarro, Armand Abergel, Olivier Rouquette, Sylvain Beorchia, Laurent Poincloux, Adrien Bartoli
We have developed a technique to study how good computers can be at diagnosing gastrointestinal lesions from regular (white light and narrow banded) colonoscopic videos compared to two levels of clinical knowledge (expert and beginner). Our technique includes a novel tissue classification approach which may save clinician's time by avoiding chromoendoscopy, a time-consuming staining procedure using indigo carmine. Our technique also discriminates the severity of individual lesions in patients with many polyps, so that the gastroenterologist can directly focus on those requiring polypectomy...
March 29, 2016: IEEE Transactions on Medical Imaging
https://www.readbyqxmd.com/read/28107365/parenclitic-network-analysis-of-methylation-data-for-cancer-identification
#20
Alexander Karsakov, Thomas Bartlett, Artem Ryblov, Iosif Meyerov, Mikhail Ivanchenko, Alexey Zaikin
We make use of ideas from the theory of complex networks to implement a machine learning classification of human DNA methylation data, that carry signatures of cancer development. The data were obtained from patients with various kinds of cancers and represented as parenclictic networks, wherein nodes correspond to genes, and edges are weighted according to pairwise variation from control group subjects. We demonstrate that for the 10 types of cancer under study, it is possible to obtain a high performance of binary classification between cancer-positive and negative samples based on network measures...
2017: PloS One
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