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https://www.readbyqxmd.com/read/28636361/a-machine-learning-assisted-approach-for-discovering-novel-inhibitors-targeting-bromodomain-containing-protein-4
#1
Jing Xing, Wenchao Lu, Rongfeng Liu, Yulan Wang, Yiqian Xie, Hao Zhang, Zhe Shi, Hao Jiang, Yu-Chih Liu, Kaixian Chen, Hualiang Jiang, Cheng Luo, Mingyue Zheng
Bromodomain-containing protein 4 (BRD4) is implicated in the pathogenesis of a number of different cancers, inflammatory diseases and heart failure. Much effort has been dedicated toward discovering novel scaffold BRD4 inhibitors (BRD4is) with different selectivity profiles and potential anti-resistance properties. Structure-based drug design (SBDD) and virtual screening (VS) are the most frequently used approaches. Here, we demonstrate a novel, structure-based VS approach that uses machine-learning algorithms trained on the priori structure and activity knowledge to predict the likelihood that a compound is a BRD4i based on its binding pattern with BRD4...
June 21, 2017: Journal of Chemical Information and Modeling
https://www.readbyqxmd.com/read/28630883/detection-of-prostate-cancer-in-multiparametric-mri-using-random-forest-with-instance-weighting
#2
Nathan Lay, Yohannes Tsehay, Matthew D Greer, Baris Turkbey, Jin Tae Kwak, Peter L Choyke, Peter Pinto, Bradford J Wood, Ronald M Summers
A prostate computer-aided diagnosis (CAD) based on random forest to detect prostate cancer using a combination of spatial, intensity, and texture features extracted from three sequences, T2W, ADC, and B2000 images, is proposed. The random forest training considers instance-level weighting for equal treatment of small and large cancerous lesions as well as small and large prostate backgrounds. Two other approaches, based on an AutoContext pipeline intended to make better use of sequence-specific patterns, were considered...
April 2017: Journal of Medical Imaging
https://www.readbyqxmd.com/read/28628331/quantum-cascade-laser-spectral-histopathology-breast-cancer-diagnostics-using-high-throughput-chemical-imaging
#3
Michael John Pilling, Alex Henderson, Peter Gardner
Fourier Transform Infrared (FT-IR) microscopy, coupled with machine learning approaches, has been demonstrated to be a powerful technique for identifying abnormalities in human tissue. The ability to objectively identify the pre-diseased state, and diagnose cancer with high levels of accuracy, has the potential to revolutionise current histopathological practice. Despite recent technological advances in FT-IR microscopy, sample throughput and speed of acquisition are key barriers to clinical translation. Wide-field quantum cascade laser (QCL) infrared imaging systems with large focal plane array detectors and utilising discrete frequency imaging, have demonstrated that large tissue microarrays (TMA) can be imaged in a matter of minutes...
June 19, 2017: Analytical Chemistry
https://www.readbyqxmd.com/read/28624626/alternative-polyadenylation-patterns-for-novel-gene-discovery-and-classification-in-cancer
#4
Oguzhan Begik, Merve Oyken, Tuna Cinkilli Alican, Tolga Can, Ayse Elif Erson-Bensan
Certain aspects of diagnosis, prognosis, and treatment of cancer patients are still important challenges to be addressed. Therefore, we propose a pipeline to uncover patterns of alternative polyadenylation (APA), a hidden complexity in cancer transcriptomes, to further accelerate efforts to discover novel cancer genes and pathways. Here, we analyzed expression data for 1045 cancer patients and found a significant shift in usage of poly(A) signals in common tumor types (breast, colon, lung, prostate, gastric, and ovarian) compared to normal tissues...
June 15, 2017: Neoplasia: An International Journal for Oncology Research
https://www.readbyqxmd.com/read/28624625/a-formalin-fixed-paraffin-embedded-ffpe-based-prognostic-signature-to-predict-metastasis-in-clinically-low-risk-stage-i-ii-microsatellite-stable-colorectal-cancer
#5
Yee Syuen Low, Christopher Blöcker, John R McPherson, See Aik Tang, Ying Ying Cheng, Joyner Y S Wong, Clarinda Chua, Tony K H Lim, Choong Leong Tang, Min Hoe Chew, Patrick Tan, Iain B Tan, Steven G Rozen, Peh Yean Cheah
Approximately 20% early-stage (I/II) colorectal cancer (CRC) patients develop metastases despite curative surgery. We aim to develop a formalin-fixed and paraffin-embedded (FFPE)-based predictor of metastases in early-stage, clinically-defined low risk, microsatellite-stable (MSS) CRC patients. We considered genome-wide mRNA and miRNA expression and mutation status of 20 genes assayed in 150 fresh-frozen tumours with known metastasis status. We selected 193 genes for further analysis using NanoString nCounter arrays on corresponding FFPE tumours...
June 15, 2017: Cancer Letters
https://www.readbyqxmd.com/read/28613390/autoihc-scoring-a-machine-learning-framework-for-automated-allred-scoring-of-molecular-expression-in-er-and-pr-stained-breast-cancer-tissue
#6
S Tewary, I Arun, R Ahmed, S Chatterjee, C Chakraborty
In prognostic evaluation of breast cancer Immunohistochemical (IHC) markers namely, oestrogen receptor (ER) and progesterone receptor (PR) are widely used. The expert pathologist investigates qualitatively the stained tissue slide under microscope to provide the Allred score; which is clinically used for therapeutic decision making. Such qualitative judgment is time-consuming, tedious and more often suffers from interobserver variability. As a result, it leads to imprecise IHC score for ER and PR. To overcome this, there is an urgent need of developing a reliable and efficient IHC quantifier for high throughput decision making...
June 14, 2017: Journal of Microscopy
https://www.readbyqxmd.com/read/28612037/large-scale-image-region-documentation-for-fully-automated-image-biomarker-algorithm-development-and-evaluation
#7
Anthony P Reeves, Yiting Xie, Shuang Liu
With the advent of fully automated image analysis and modern machine learning methods, there is a need for very large image datasets having documented segmentations for both computer algorithm training and evaluation. This paper presents a method and implementation for facilitating such datasets that addresses the critical issue of size scaling for algorithm validation and evaluation; current evaluation methods that are usually used in academic studies do not scale to large datasets. This method includes protocols for the documentation of many regions in very large image datasets; the documentation may be incrementally updated by new image data and by improved algorithm outcomes...
April 2017: Journal of Medical Imaging
https://www.readbyqxmd.com/read/28611203/detection-of-head-and-neck-cancer-in-surgical-specimens-using-quantitative-hyperspectral-imaging
#8
Guolan Lu, James V Little, Xu Wang, Hongzheng Zhang, Mihir Patel, Christopher C Griffith, Mark El-Deiry, Amy Y Chen, Baowei Fei
This study intends to investigate the feasibility of using hyperspectral imaging (HSI) to detect and delineate cancers in fresh, surgical specimens of patients with head and neck cancers.<br /><br />Experimental Design: A clinical study was conducted in order to collect and image fresh, surgical specimens from patients (N = 36) with head and neck cancers undergoing surgical resection. A set of machine-learning tools were developed to quantify hyperspectral images of the resected tissue in order to detect and delineate cancerous regions which were validated by histopathological diagnosis...
June 13, 2017: Clinical Cancer Research: An Official Journal of the American Association for Cancer Research
https://www.readbyqxmd.com/read/28607456/an-advanced-deep-learning-approach-for-ki-67-stained-hotspot-detection-and-proliferation-rate-scoring-for-prognostic-evaluation-of-breast-cancer
#9
Monjoy Saha, Chandan Chakraborty, Indu Arun, Rosina Ahmed, Sanjoy Chatterjee
Being a non-histone protein, Ki-67 is one of the essential biomarkers for the immunohistochemical assessment of proliferation rate in breast cancer screening and grading. The Ki-67 signature is always sensitive to radiotherapy and chemotherapy. Due to random morphological, color and intensity variations of cell nuclei (immunopositive and immunonegative), manual/subjective assessment of Ki-67 scoring is error-prone and time-consuming. Hence, several machine learning approaches have been reported; nevertheless, none of them had worked on deep learning based hotspots detection and proliferation scoring...
June 12, 2017: Scientific Reports
https://www.readbyqxmd.com/read/28604368/predictive-modeling-of-outcomes-following-definitive-chemoradiotherapy-for-oropharyngeal-cancer-based-on-fdg-pet-image-characteristics
#10
Michael R Folkert, Jeremy Setton, Aditya P Apte, Milan Grkovski, Robert J Young, Heiko Schöder, Wade L Thorstad, Nancy Y Lee, Joseph O Deasy, Jung Hun Oh
In this study, we investigate the use of imaging feature-based outcomes research ('radiomics') combined with machine learning techniques to develop robust predictive models for the risk of all-cause mortality (ACM), local failure (LF), and distant metastasis (DM) following definitive chemoradiation therapy (CRT). One hundred seventy four patients with stage III-IV oropharyngeal cancer (OC) treated at our institution with CRT with retrievable pre- and post-treatment 18F-fluorodeoxyglucose positron emission tomography (FDG-PET) scans were identified...
July 7, 2017: Physics in Medicine and Biology
https://www.readbyqxmd.com/read/28584683/training-nuclei-detection-algorithms-with-simple-annotations
#11
Henning Kost, André Homeyer, Jesper Molin, Claes Lundström, Horst Karl Hahn
BACKGROUND: Generating good training datasets is essential for machine learning-based nuclei detection methods. However, creating exhaustive nuclei contour annotations, to derive optimal training data from, is often infeasible. METHODS: We compared different approaches for training nuclei detection methods solely based on nucleus center markers. Such markers contain less accurate information, especially with regard to nuclear boundaries, but can be produced much easier and in greater quantities...
2017: Journal of Pathology Informatics
https://www.readbyqxmd.com/read/28582921/grouped-fuzzy-svm-with-em-based-partition-of-sample-space-for-clustered-microcalcification-detection
#12
Huiya Wang, Jun Feng, Hongyu Wang
BACKGROUND: Detection of clustered microcalcification (MC) from mammograms plays essential roles in computer-aided diagnosis for early stage breast cancer. OBJECTIVE: To tackle problems associated with the diversity of data structures of MC lesions and the variability of normal breast tissues, multi-pattern sample space learning is required. METHODS: In this paper, a novel grouped fuzzy Support Vector Machine (SVM) algorithm with sample space partition based on Expectation-Maximization (EM) (called G-FSVM) is proposed for clustered MC detection...
May 26, 2017: Technology and Health Care: Official Journal of the European Society for Engineering and Medicine
https://www.readbyqxmd.com/read/28570557/classification-of-breast-cancer-histology-images-using-convolutional-neural-networks
#13
Teresa Araújo, Guilherme Aresta, Eduardo Castro, José Rouco, Paulo Aguiar, Catarina Eloy, António Polónia, Aurélio Campilho
Breast cancer is one of the main causes of cancer death worldwide. The diagnosis of biopsy tissue with hematoxylin and eosin stained images is non-trivial and specialists often disagree on the final diagnosis. Computer-aided Diagnosis systems contribute to reduce the cost and increase the efficiency of this process. Conventional classification approaches rely on feature extraction methods designed for a specific problem based on field-knowledge. To overcome the many difficulties of the feature-based approaches, deep learning methods are becoming important alternatives...
2017: PloS One
https://www.readbyqxmd.com/read/28556024/cancer-of-the-esophagus-and-esophagogastric-junction-major-changes-in-the-american-joint-committee-on-cancer-eighth-edition-cancer-staging-manual
#14
Thomas W Rice, Donna M Gress, Deepa T Patil, Wayne L Hofstetter, David P Kelsen, Eugene H Blackstone
Answer questions and earn CME/CNE New to the eighth edition of the American Joint Committee on Cancer (AJCC) Cancer Staging Manual for epithelial cancers of the esophagus and esophagogastric junction are separate, temporally related cancer classifications: 1) before treatment decision (clinical); 2) after esophagectomy alone (pathologic); and 3) after preresection therapy followed by esophagectomy (postneoadjuvant pathologic). The addition of clinical and postneoadjuvant pathologic stage groupings was driven by a lack of correspondence of survival, and thus prognosis, between both clinical and postneoadjuvant pathologic cancer categories (facts about the cancer) and pathologic categories...
May 26, 2017: CA: a Cancer Journal for Clinicians
https://www.readbyqxmd.com/read/28545021/quantitative-prediction-of-oral-cancer-risk-in-patients-with-oral-leukoplakia
#15
Yao Liu, Yicheng Li, Yue Fu, Tong Liu, Xiaoyong Liu, Xinyan Zhang, Jie Fu, Xiaobing Guan, Tong Chen, Xiaoxin Chen, Zheng Sun
Exfoliative cytology has been widely used for early diagnosis of oral squamous cell carcinoma. We have developed an oral cancer risk index using DNA index value to quantitatively assess cancer risk in patients with oral leukoplakia, but with limited success. In order to improve the performance of the risk index, we collected exfoliative cytology, histopathology, and clinical follow-up data from two independent cohorts of normal, leukoplakia and cancer subjects (training set and validation set). Peaks were defined on the basis of first derivatives with positives, and modern machine learning techniques were utilized to build statistical prediction models on the reconstructed data...
May 2, 2017: Oncotarget
https://www.readbyqxmd.com/read/28541743/rarevar-a-framework-for-detecting-low-frequency-single-nucleotide-variants
#16
Yangyang Hao, Xiaoling Xuei, Lang Li, Harikrishna Nakshatri, Howard J Edenberg, Yunlong Liu
Accurate identification of low-frequency somatic point mutations in tumor samples has important clinical utilities. Although high-throughput sequencing technology enables capturing such variants while sequencing primary tumor samples, our ability for accurate detection is compromised when the variant frequency is close to the sequencer error rate. Most current experimental and bioinformatic strategies target mutations with ≥5% allele frequency, which limits our ability to understand the cancer etiology and tumor evolution...
May 25, 2017: Journal of Computational Biology: a Journal of Computational Molecular Cell Biology
https://www.readbyqxmd.com/read/28540688/machine-learning-techniques-in-exploring-microrna-gene-discovery-targets-and-functions
#17
Sumi Singh, Ryan G Benton, Anurag Singh, Anshuman Singh
In recent years, the role of miRNAs in post-transcriptional gene regulation has provided new insights into the understanding of several types of cancers and neurological disorders. Although miRNA research has gathered great momentum since its discovery, traditional biological methods for finding miRNA genes and targets continue to remain a huge challenge due to the laborious tasks and extensive time involved. Fortunately, advances in computational methods have yielded considerable improvements in miRNA studies...
2017: Methods in Molecular Biology
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
#18
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
#19
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
#20
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
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