Read by QxMD icon Read

Cancer Informatics

Anton Buzdin, Maxim Sorokin, Elena Poddubskaya, Nicolas Borisov
We recently reviewed the current progress in the use of high-throughput molecular "omics" data for the quantitative analysis of molecular pathway activation. These quantitative metrics may be used in many ways, and we focused on their application as tumor biomarkers. Here, we provide an update of the most recent conceptual findings related to pathway analysis in tumor biology, which were not included in the previous review. The major novelties include a method enabling calculation of pathway-scale tumor mutation burden termed "Pathway Instability" and its application for scoring of anticancer target drugs...
2019: Cancer Informatics
Ophir Gal, Noam Auslander, Yu Fan, Daoud Meerzaman
Machine learning (ML) is a useful tool for advancing our understanding of the patterns and significance of biomedical data. Given the growing trend on the application of ML techniques in precision medicine, here we present an ML technique which predicts the likelihood of complete remission (CR) in patients diagnosed with acute myeloid leukemia (AML). In this study, we explored the question of whether ML algorithms designed to analyze gene-expression patterns obtained through RNA sequencing (RNA-seq) can be used to accurately predict the likelihood of CR in pediatric AML patients who have received induction therapy...
2019: Cancer Informatics
Aaron Galaznik, Christian Reich, Greg Klebanov, Yuriy Khoma, Eldar Allakhverdiiev, Greg Hather, Yaping Shou
In diffuse large B-cell lymphoma (DLBCL), predictive modeling may contribute to targeted drug development by enrichment of the study populations enrolled in clinical trials of DLBCL investigational drugs to include patients with lower likelihood of responding to standard of care. In clinical practice, predictive modeling has the potential to optimize therapy choices in DLBCL. The objectives of this study were to create a model for predicting health outcomes in patients with DLBCL treated with standard of care and determine informative predictors of health outcomes for patients with DLBCL...
2019: Cancer Informatics
Zhonglin Qu, Chng Wei Lau, Quang Vinh Nguyen, Yi Zhou, Daniel R Catchpoole
Visual analytics and visualisation can leverage the human perceptual system to interpret and uncover hidden patterns in big data. The advent of next-generation sequencing technologies has allowed the rapid production of massive amounts of genomic data and created a corresponding need for new tools and methods for visualising and interpreting these data. Visualising genomic data requires not only simply plotting of data but should also offer a decision or a choice about what the message should be conveyed in the particular plot; which methodologies should be used to represent the results must provide an easy, clear, and accurate way to the clinicians, experts, or researchers to interact with the data...
2019: Cancer Informatics
Abedalrhman Alkhateeb, Iman Rezaeian, Siva Singireddy, Dora Cavallo-Medved, Lisa A Porter, Luis Rueda
Prostate cancer is one of the most common types of cancer among Canadian men. Next-generation sequencing using RNA-Seq provides large amounts of data that may reveal novel and informative biomarkers. We introduce a method that uses machine learning techniques to identify transcripts that correlate with prostate cancer development and progression. We have isolated transcripts that have the potential to serve as prognostic indicators and may have tremendous value in guiding treatment decisions. Analysis of normal versus malignant prostate cancer data sets indicates differential expression of the genes HEATR5B, DDC, and GABPB1-AS1 as potential prostate cancer biomarkers...
2019: Cancer Informatics
Peter Utnes, Cecilie Løkke, Trond Flægstad, Christer Einvik
Neuroblastoma is a pediatric cancer of the developing sympathetic nervous system. High-risk neuroblastoma patients typically undergo an initial remission in response to treatment, followed by recurrence of aggressive tumors that have become refractory to further treatment. The need for biomarkers that can select patients not responding well to therapy in an early phase is therefore needed. In this study, we used next generation sequencing technology to determine the expression profiles in high-risk neuroblastoma cell lines established before and after therapy...
2019: Cancer Informatics
John Cg Spainhour, Hong Seo Lim, Soojin V Yi, Peng Qiu
Background: DNA methylation is a form of epigenetic modification that has been shown to play a significant role in gene regulation. In cancer, DNA methylation plays an important role by regulating the expression of oncogenes. The role of DNA methylation in the onset and progression of various cancer types is now being elucidated as more large-scale data become available. The Cancer Genome Atlas (TCGA) provides a wealth of information for the analysis of various molecular aspects of cancer genetics...
2019: Cancer Informatics
Themis Liolios, Stavroula Lila Kastora, Giorgia Colombo
MicroRNAs (miRNAs) are endogenous 22-nucleotide RNAs that can play a fundamental regulatory role in the gene expression of various organisms. Current research suggests that miRNAs can assume pivotal roles in carcinogenesis. In this article, through bioinformatics mining and computational analysis, we determine a single miRNA commonly involved in the development of breast, cervical, endometrial, ovarian, and vulvar cancer, whereas we underline the existence of 7 more miRNAs common in all examined malignancies with the exception of vulvar cancer...
2019: Cancer Informatics
Chase Cockrell, David E Axelrod
Cancer chemotherapy dose schedules are conventionally applied intermittently, with dose duration of the order of hours, intervals between doses of days or weeks, and cycles repeated for weeks. The large number of possible combinations of values of duration, interval, and lethality has been an impediment to empirically determine the optimal set of treatment conditions. The purpose of this project was to determine the set of parameters for duration, interval, and lethality that would be most effective for treating early colon cancer...
2019: Cancer Informatics
Melissa Zhao, Yushi Tang, Hyunkyung Kim, Kohei Hasegawa
Objective: Despite existing prognostic markers, breast cancer prognosis remains a difficult subject due to the complex relationships between many contributing factors and survival. This study seeks to integrate multiple clinicopathological and genomic factors with dimensional reduction across machine learning algorithms to compare survival predictions. Methods: This is a secondary analysis of the data from a prospective cohort study of female patients with breast cancer enrolled in the Molecular Taxonomy of Breast Cancer International Consortium (METABRIC)...
2018: Cancer Informatics
Katie Gao, Dayong Wang, Yi Huang
Conventional cancer drug development has long been limited to organ- or tissue-specific cancer types. However, it has become increasingly known that specific genetic abnormalities are responsible for the carcinogenesis of multiple cancers. The recent US Food and Drug Administration (FDA) approval of the first multi-cancer drug, Keytruda, has demonstrated the feasibility of developing new drugs that target multiple cancers. Despite a promising future, methodological development for identifying multi-cancer molecular targets remains encumbered...
2018: Cancer Informatics
Srikanth Kuthuru, William Deaderick, Harrison Bai, Chang Su, Tiep Vu, Vishal Monga, Arvind Rao
Radiomics is a rapidly growing field in which sophisticated imaging features are extracted from radiology images to predict clinical outcomes/responses, genetic alterations, and other outcomes relevant to a patient's prognosis or response to therapy. This approach can effectively capture intratumor phenotypic heterogeneity by interrogating the "larger" image field, which is not possible with traditional biopsy procedures that interrogate specific subregions alone. Most models in radiomics derive numerous imaging features (eg, texture, shape, size) from a radiology data set and then learn complex nonlinear hypotheses to solve a given prediction task...
2018: Cancer Informatics
Jovan Cejovic, Jelena Radenkovic, Vladimir Mladenovic, Adam Stanojevic, Milica Miletic, Stevan Radanovic, Dragan Bajcic, Dragan Djordjevic, Filip Jelic, Milos Nesic, Jessica Lau, Patrick Grady, Nick Groves-Kirkby, Deniz Kural, Brandi Davis-Dusenbery
Increased efforts in cancer genomics research and bioinformatics are producing tremendous amounts of data. These data are diverse in origin, format, and content. As the amount of available sequencing data increase, technologies that make them discoverable and usable are critically needed. In response, we have developed a Semantic Web-based Data Browser, a tool allowing users to visually build and execute ontology-driven queries. This approach simplifies access to available data and improves the process of using them in analyses on the Seven Bridges Cancer Genomics Cloud (CGC; www...
2018: Cancer Informatics
(no author information available yet)
[This corrects the article DOI: 10.1177/1176935118786260.].
2018: Cancer Informatics
Abdallah K Alameddine, Frederick Conlin, Brian Binnall
Background: Frequently occurring in cancer are the aberrant alterations of regulatory onco-metabolites, various oncogenes/epigenetic stochasticity, and suppressor genes, as well as the deficient mismatch repair mechanism, chronic inflammation, or those deviations belonging to the other cancer characteristics. How these aberrations that evolve overtime determine the global phenotype of malignant tumors remains to be completely understood. Dynamic analysis may have potential to reveal the mechanism of carcinogenesis and can offer new therapeutic intervention...
2018: Cancer Informatics
Mahdi Imani, Roozbeh Dehghannasiri, Ulisses M Braga-Neto, Edward R Dougherty
Scientists are attempting to use models of ever-increasing complexity, especially in medicine, where gene-based diseases such as cancer require better modeling of cell regulation. Complex models suffer from uncertainty and experiments are needed to reduce this uncertainty. Because experiments can be costly and time-consuming, it is desirable to determine experiments providing the most useful information. If a sequence of experiments is to be performed, experimental design is needed to determine the order. A classical approach is to maximally reduce the overall uncertainty in the model, meaning maximal entropy reduction...
2018: Cancer Informatics
Wasiu Opeyemi Oduola, Xiangfang Li
Effective cancer treatment strategy requires an understanding of cancer behavior and development across multiple temporal and spatial scales. This has resulted into a growing interest in developing multiscale mathematical models that can simulate cancer growth, development, and response to drug treatments. This study thus investigates multiscale tumor modeling that integrates drug pharmacokinetic and pharmacodynamic (PK/PD) information using stochastic hybrid system modeling framework. Specifically, (1) pathways modeled by differential equations are adopted for gene regulations at the molecular level; (2) cellular automata (CA) model is proposed for the cellular and multicellular scales...
2018: Cancer Informatics
Kashyap Nagaraja, Ulisses Braga-Neto
Selected reaction monitoring (SRM) has become one of the main methods for low-mass-range-targeted proteomics by mass spectrometry (MS). However, in most SRM-MS biomarker validation studies, the sample size is very small, and in particular smaller than the number of proteins measured in the experiment. Moreover, the data can be noisy due to a low number of ions detected per peptide by the instrument. In this article, those issues are addressed by a model-based Bayesian method for classification of SRM-MS data...
2018: Cancer Informatics
Gokhan Kirlik, Rao Gullapalli, Warren D'Souza, Gazi Md Daud Iqbal, Michael Naslund, Jade Wong, John Papadimitrou, Steve Roys, Nilesh Mistry, Hao Zhang
Prostate cancer is the most frequently diagnosed cancer in men in the United States. The current main methods for diagnosing prostate cancer include prostate-specific antigen test and transrectal biopsy. Prostate-specific antigen screening has been criticized for overdiagnosis and unnecessary treatment, and transrectal biopsy is an invasive procedure with low sensitivity for diagnosis. We provided a quantitative tool using supervised learning with multiparametric imaging to be able to accurately detect cancer foci and its aggressiveness...
2018: Cancer Informatics
Shinuk Kim
Motivation: Uncovering the relationship between micro-RNAs (miRNAs) and their target messenger RNAs (mRNAs) can provide critical information regarding the mechanisms underlying certain types of cancers. In this context, we have proposed a computational method, referred to as prediction analysis by optimization method (PAOM), to predict miRNA-mRNA relations using data from normal and cancer tissues, and then applying the relevant algorithms to colon and breast cancers. Specifically, we used 26 miRNAs and 26 mRNAs with 676 (= 26 × 26) relationships to be recovered as unknown parameters...
2018: Cancer Informatics
Fetch more papers »
Fetching more papers... Fetching...
Read by QxMD. Sign in or create an account to discover new knowledge that matter to you.
Remove bar
Read by QxMD icon Read

Search Tips

Use Boolean operators: AND/OR

diabetic AND foot
diabetes OR diabetic

Exclude a word using the 'minus' sign

Virchow -triad

Use Parentheses

water AND (cup OR glass)

Add an asterisk (*) at end of a word to include word stems

Neuro* will search for Neurology, Neuroscientist, Neurological, and so on

Use quotes to search for an exact phrase

"primary prevention of cancer"
(heart or cardiac or cardio*) AND arrest -"American Heart Association"