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Artykuły w czasopismach na temat „Cancer bioinformatics”

Autor: Grafiati

Data publikacji: 4 czerwca 2021

Data aktualizacji: 13 lutego 2022

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1

Desany, Brian, and Zemin Zhang. "Bioinformatics and cancer target discovery." Drug Discovery Today 9, no. 18 (September 2004): 795–802. http://dx.doi.org/10.1016/s1359-6446(04)03224-6.

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Brenner, Chad. "Applications of Bioinformatics in Cancer." Cancers 11, no. 11 (October 24, 2019): 1630. http://dx.doi.org/10.3390/cancers11111630.

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Blekherman, Grigoriy, Reinhard Laubenbacher, Diego F. Cortes, Pedro Mendes, Frank M. Torti, Steven Akman, Suzy V. Torti, and Vladimir Shulaev. "Bioinformatics tools for cancer metabolomics." Metabolomics 7, no. 3 (January 12, 2011): 329–43. http://dx.doi.org/10.1007/s11306-010-0270-3.

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Puig, Oscar, Eugene Joseph, Malgorzata Jaremko, Gregory Kellogg, Robert Wisotzkey, Roman Shraga, Bonny Patel, et al. "Comprehensive next generation sequencing assay and bioinformatic pipeline for identifying pathogenic variants associated with hereditary cancers." Journal of Clinical Oncology 35, no. 15_suppl (May 20, 2017): e13105-e13105. http://dx.doi.org/10.1200/jco.2017.35.15_suppl.e13105.

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e13105 Background: Diagnosis of hereditary cancer syndromes involves time-consuming comprehensive clinical and laboratory work-up, however, timely and accurate diagnosis is pivotal to the clinical management of cancer patients. Germline genetic testing has shown to facilitate the diagnostic process, allowing for identification and management of individuals at risk for inherited cancers. However, the laboratory diagnostics process requires not only development and validation of comprehensive gene panels to improve diagnostic yields, but a quality driven workflow including an end-to-end bioinfor

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UMAR, ASAD. "Applications of Bioinformatics in Cancer Detection: A Lexicon of Bioinformatics Terms." Annals of the New York Academy of Sciences 1020, no. 1 (May 2004): 263–76. http://dx.doi.org/10.1196/annals.1310.021.

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Fenstermacher, David A. "Book Review: Bioinformatics in Cancer and Cancer Therapy." Cancer Control 16, no. 4 (October 2009): 349. http://dx.doi.org/10.1177/107327480901600411.

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Xu, Chaobo, and Ming Liu. "Integrative bioinformatics analysis of KPNA2 in six major human cancers." Open Medicine 16, no. 1 (January 1, 2021): 498–511. http://dx.doi.org/10.1515/med-2021-0257.

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Abstract Background Malignant tumors were considered as the leading causes of cancer-related mortality globally. More and more studies found that dysregulated genes played an important role in carcinogenesis. The aim of this study was to explore the significance of KPNA2 in human six major cancers including non-small cell lung cancer (NSCLC), gastric cancer, colorectal cancer, breast cancer, hepatocellular carcinoma, and bladder cancer based on bioinformatics analysis. Methods The data were collected and comprehensively analyzed based on multiple databases. KPNA2 mRNA expression in six major c

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Van Neste, Leander, James G. Herman, Kornel E. Schuebel, Leslie Cope, Stephen B. Baylin, Wim Van Criekinge, and Nita Ahuja. "A Bioinformatics Pipeline for Cancer Epigenetics." Current Bioinformatics 5, no. 3 (September 1, 2010): 153–63. http://dx.doi.org/10.2174/157489310792006710.

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YANG, HOWARD H., and MAXWELL P. LEE. "Application of Bioinformatics in Cancer Epigenetics." Annals of the New York Academy of Sciences 1020, no. 1 (May 2004): 67–76. http://dx.doi.org/10.1196/annals.1310.008.

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Charoentong, Pornpimol, Mihaela Angelova, Mirjana Efremova, Ralf Gallasch, Hubert Hackl, Jerome Galon, and Zlatko Trajanoski. "Bioinformatics for cancer immunology and immunotherapy." Cancer Immunology, Immunotherapy 61, no. 11 (September 18, 2012): 1885–903. http://dx.doi.org/10.1007/s00262-012-1354-x.

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Olsen, Lars Rønn, Benito Campos, Mike Stein Barnkob, Ole Winther, Vladimir Brusic, and Mads Hald Andersen. "Bioinformatics for cancer immunotherapy target discovery." Cancer Immunology, Immunotherapy 63, no. 12 (October 26, 2014): 1235–49. http://dx.doi.org/10.1007/s00262-014-1627-7.

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Dopazo, Joaquín. "Bioinformatics and cancer: an essential alliance." Clinical and Translational Oncology 8, no. 6 (June 2006): 409–15. http://dx.doi.org/10.1007/s12094-006-0194-6.

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Marsili, Stefania, Ailone Tichon, Deepali Kundnani, and Francesca Storici. "Gene Co-Expression Analysis of Human RNASEH2A Reveals Functional Networks Associated with DNA Replication, DNA Damage Response, and Cell Cycle Regulation." Biology 10, no. 3 (March 13, 2021): 221. http://dx.doi.org/10.3390/biology10030221.

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Ribonuclease (RNase) H2 is a key enzyme for the removal of RNA found in DNA-RNA hybrids, playing a fundamental role in biological processes such as DNA replication, telomere maintenance, and DNA damage repair. RNase H2 is a trimer composed of three subunits, RNASEH2A being the catalytic subunit. RNASEH2A expression levels have been shown to be upregulated in transformed and cancer cells. In this study, we used a bioinformatics approach to identify RNASEH2A co-expressed genes in different human tissues to underscore biological processes associated with RNASEH2A expression. Our analysis shows fu

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Solmaz, Mustafa, Adam Lane, Bilal Gonen, Ogulsheker Akmamedova, Mehmet H. Gunes, and Kakajan Komurov. "Graphical data mining of cancer mechanisms with SEMA." Bioinformatics 35, no. 21 (May 9, 2019): 4413–18. http://dx.doi.org/10.1093/bioinformatics/btz303.

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Abstract Motivation An important goal of cancer genomics initiatives is to provide the research community with the resources for the unbiased query of cancer mechanisms. Several excellent web platforms have been developed to enable the visual analyses of molecular alterations in cancers from these datasets. However, there are few tools to allow the researchers to mine these resources for mechanisms of cancer processes and their functional interactions in an intuitive unbiased manner. Results To address this need, we developed SEMA, a web platform for building and testing of models of cancer me

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Li, Kening, Yuxin Du, Lu Li, and Dong-Qing Wei. "Bioinformatics Approaches for Anti-cancer Drug Discovery." Current Drug Targets 21, no. 1 (December 20, 2019): 3–17. http://dx.doi.org/10.2174/1389450120666190923162203.

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Drug discovery is important in cancer therapy and precision medicines. Traditional approaches of drug discovery are mainly based on in vivo animal experiments and in vitro drug screening, but these methods are usually expensive and laborious. In the last decade, omics data explosion provides an opportunity for computational prediction of anti-cancer drugs, improving the efficiency of drug discovery. High-throughput transcriptome data were widely used in biomarkers’ identification and drug prediction by integrating with drug-response data. Moreover, biological network theory and methodology wer

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16

Cheng, Phil F. "Medical bioinformatics in melanoma." Current Opinion in Oncology 30, no. 2 (March 2018): 113–17. http://dx.doi.org/10.1097/cco.0000000000000428.

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Liu, Zhenqiu, Dechang Chen, Xuewen Chen, and Haomiao Jia. "Computational Data Mining in Cancer Bioinformatics and Cancer Epidemiology." Journal of Biomedicine and Biotechnology 2009 (2009): 1–2. http://dx.doi.org/10.1155/2009/582697.

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Li, Ju-Yueh, Chia-Jung Li, Li-Te Lin, and Kuan-Hao Tsui. "Multi-Omics Analysis Identifying Key Biomarkers in Ovarian Cancer." Cancer Control 27, no. 1 (January 1, 2020): 107327482097667. http://dx.doi.org/10.1177/1073274820976671.

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Ovarian cancer is one of the most common malignant tumors. Here, we aimed to study the expression and function of the CREB1 gene in ovarian cancer via the bioinformatic analyses of multiple databases. Previously, the prognosis of ovarian cancer was based on single-factor or single-gene studies. In this study, different bioinformatics tools (such as TCGA, GEPIA, UALCAN, MEXPRESS, and Metascape) have been used to assess the expression and prognostic value of the CREB1 gene. We used the Reactome and cBioPortal databases to identify and analyze CREB1 mutations, copy number changes, expression chan

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19

Gensterblum-Miller, Elizabeth, and J. Chad Brenner. "Protecting Tumors by Preventing Human Papilloma Virus Antigen Presentation: Insights from Emerging Bioinformatics Algorithms." Cancers 11, no. 10 (October 12, 2019): 1543. http://dx.doi.org/10.3390/cancers11101543.

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Recent developments in bioinformatics technologies have led to advances in our understanding of how oncogenic viruses such as the human papilloma virus drive cancer progression and evade the host immune system. Here, we focus our review on understanding how these emerging bioinformatics technologies influence our understanding of how human papilloma virus (HPV) drives immune escape in cancers of the head and neck, and how these new informatics approaches may be generally applicable to other virally driven cancers. Indeed, these tools enable researchers to put existing data from genome wide ass

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20

Lin, Chih-Hsu, and Olivier Lichtarge. "Using interpretable deep learning to model cancer dependencies." Bioinformatics 37, no. 17 (May 27, 2021): 2675–81. http://dx.doi.org/10.1093/bioinformatics/btab137.

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Abstract Motivation Cancer dependencies provide potential drug targets. Unfortunately, dependencies differ among cancers and even individuals. To this end, visible neural networks (VNNs) are promising due to robust performance and the interpretability required for the biomedical field. Results We design Biological visible neural network (BioVNN) using pathway knowledge to predict cancer dependencies. Despite having fewer parameters, BioVNN marginally outperforms traditional neural networks (NNs) and converges faster. BioVNN also outperforms an NN based on randomized pathways. More importantly,

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Kim, Jiwoong, Yun-Gyeong Lee, and Namshin Kim. "Bioinformatics Interpretation of Exome Sequencing: Blood Cancer." Genomics & Informatics 11, no. 1 (2013): 24. http://dx.doi.org/10.5808/gi.2013.11.1.24.

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Hanauer, David, Daniel Rhodes, Chandan Sinha-Kumar, and Arul Chinnaiyan. "Bioinformatics Approaches in the Study of Cancer." Current Molecular Medicine 7, no. 1 (February 1, 2007): 133–41. http://dx.doi.org/10.2174/156652407779940431.

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Senior, Kathryn. "Bioinformatics: a tangled web for cancer researchers." Lancet Oncology 7, no. 3 (March 2006): 208. http://dx.doi.org/10.1016/s1470-2045(06)70611-8.

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Bensmail, Halima, and Abdelali Haoudi. "Postgenomics: Proteomics and Bioinformatics in Cancer Research." Journal of Biomedicine and Biotechnology 2003, no. 4 (2003): 217–30. http://dx.doi.org/10.1155/s1110724303209207.

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Now that the human genome is completed, the characterization of the proteins encoded by the sequence remains a challenging task. The study of the complete protein complement of the genome, the “proteome,” referred to as proteomics, will be essential if new therapeutic drugs and new disease biomarkers for early diagnosis are to be developed. Research efforts are already underway to develop the technology necessary to compare the specific protein profiles of diseased versus nondiseased states. These technologies provide a wealth of information and rapidly generate large quantities of data. Proce

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Simon, Richard. "Bioinformatics in cancer therapeutics—hype or hope?" Nature Clinical Practice Oncology 2, no. 5 (May 2005): 223. http://dx.doi.org/10.1038/ncponc0176.

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Dimond, Patricia Fitzpatrick. "Future Cancer Care: Anticipating “Panomics” with Bioinformatics." Clinical OMICs 1, no. 3 (May 15, 2014): 8–11. http://dx.doi.org/10.1089/clinomi.01.03.04.

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Ruan, Peifeng, Shuang Wang, and Hua Liang. "mirPLS: a partial linear structure identifier method for cancer subtyping using microRNAs." Bioinformatics 36, no. 19 (July 1, 2020): 4902–9. http://dx.doi.org/10.1093/bioinformatics/btaa606.

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Abstract Motivation MicroRNAs (miRNAs) are small non-coding RNAs that have been successfully identified to be differentially expressed in various cancers. However, some miRNAs were reported to be up-regulated in one subtype of a cancer but down-regulated in another, making overall associations between these miRNAs and the heterogeneous cancer non-linear. These non-linearly associated miRNAs, if identified, are thus informative for cancer subtyping. Results Here, we propose mirPLS, a Partial Linear Structure identifier for miRNA data that simultaneously identifies miRNAs of linear or non-linear

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Lu, Mingbei, Suping Wu, Guoxiong Cheng, Chaobo Xu, and Zhengwei Chen. "Integrative Bioinformatics Analysis of iNOS/NOS2 in gastric and colorectal cancer." Pteridines 31, no. 1 (December 17, 2020): 174–84. http://dx.doi.org/10.1515/pteridines-2020-0011.

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AbstractObjective The aim of the present work was to investigate the expression of nitric oxide synthase 2 (iNOS/ NOS2) in colorectal and gastric cancers and evaluate its association with patient’s prognosis by integrated bioinformatics analysis.Methods The data for present study was obtained from the TCGA, GTEx, and STRING database. iNOS/NOS2 mRNA expression in normal tissue and colorectal, and gastric cancer tissuea were investigated through the GTEx and TCGA database. iNOS/NOS2 gene mutations and frequency were analyzed in the TCGA database using the cBioPortal online data analysis tool. Th

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Hicks, C. "Bioinformatics Project Streamlines Data Exchange." JNCI Journal of the National Cancer Institute 96, no. 8 (April 20, 2004): 580. http://dx.doi.org/10.1093/jnci/96.8.580.

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Coleman, William B. "Cancer Bioinformatics: Addressing the Challenges of Integrated Postgenomic Cancer Research." Cancer Investigation 22, no. 1 (January 2004): 171–73. http://dx.doi.org/10.1081/cnv-120027591.

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Gadaleta, Emanuela, Stefano Pirrò, Abu Zafer Dayem Ullah, Jacek Marzec, and Claude Chelala. "BCNTB bioinformatics: the next evolutionary step in the bioinformatics of breast cancer tissue banking." Nucleic Acids Research 46, no. D1 (October 9, 2017): D1055—D1061. http://dx.doi.org/10.1093/nar/gkx913.

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Han, Jie, Yihui Rong, and Xudong Gao. "Multiomic analysis of the function of SPOCK1 across cancers: an integrated bioinformatics approach." Journal of International Medical Research 49, no. 6 (June 2021): 030006052096265. http://dx.doi.org/10.1177/0300060520962659.

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Objective To investigate SPARC (osteonectin), cwcv and kazal like domains proteoglycan 1 ( SPOCK1) gene expression across The Cancer Genome Atlas (TCGA) cancers, both in cancer versus normal tissues and in different stages across the cancer types. Methods This integrated bioinformatics study used data from several bioinformatics databases (Cancer Cell Line Encyclopedia, Genotype-Tissue Expression, TCGA, Tumor Immune Estimation Resource [TIMER]) to define the expression pattern of the SPOCK1 gene. A survival analysis was undertaken across the cancers. The search tool for retrieval of interactin

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Pettini, Francesco, Anna Visibelli, Vittoria Cicaloni, Daniele Iovinelli, and Ottavia Spiga. "Multi-Omics Model Applied to Cancer Genetics." International Journal of Molecular Sciences 22, no. 11 (May 27, 2021): 5751. http://dx.doi.org/10.3390/ijms22115751.

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In this review, we focus on bioinformatic oncology as an integrative discipline that incorporates knowledge from the mathematical, physical, and computational fields to further the biomedical understanding of cancer. Before providing a deeper insight into the bioinformatics approach and utilities involved in oncology, we must understand what is a system biology framework and the genetic connection, because of the high heterogenicity of the backgrounds of people approaching precision medicine. In fact, it is essential to providing general theoretical information on genomics, epigenomics, and tr

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Kihara, Daisuke, Yifeng David Yang, and Troy Hawkins. "Bioinformatics Resources for Cancer Research with an Emphasis on Gene Function and Structure Prediction Tools." Cancer Informatics 2 (January 2006): 117693510600200. http://dx.doi.org/10.1177/117693510600200020.

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The immensely popular fields of cancer research and bioinformatics overlap in many different areas, e.g. large data repositories that allow for users to analyze data from many experiments (data handling, databases), pattern mining, microarray data analysis, and interpretation of proteomics data. There are many newly available resources in these areas that may be unfamiliar to most cancer researchers wanting to incorporate bioinformatics tools and analyses into their work, and also to bioinformaticians looking for real data to develop and test algorithms. This review reveals the interdependence

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He, Yongxiong, Yongfei Cao, Xiaolei Wang, Wu Jisiguleng, Mingkai Tao, Jianfeng Liu, Fei Wang, et al. "Identification of Hub Genes to Regulate Breast Cancer Spinal Metastases by Bioinformatics Analyses." Computational and Mathematical Methods in Medicine 2021 (May 12, 2021): 1–12. http://dx.doi.org/10.1155/2021/5548918.

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Breast cancer (BC) had been one of the deadliest types of cancers in women worldwide. More than 65% of advanced-stage BC patients were identified to have bone metastasis. However, the molecular mechanisms involved in the BC spinal metastases remained largely unclear. This study screened dysregulated genes in the progression of BC spinal metastases by analyzing GSE22358. Moreover, we constructed PPI networks to identify key regulators in this progression. Bioinformatics analysis showed that these key regulators were involved in regulating the metabolic process, cell proliferation, Toll-like rec

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Robinson, Welles, Roded Sharan, and Mark D. M. Leiserson. "Modeling clinical and molecular covariates of mutational process activity in cancer." Bioinformatics 35, no. 14 (July 2019): i492—i500. http://dx.doi.org/10.1093/bioinformatics/btz340.

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Abstract Motivation Somatic mutations result from processes related to DNA replication or environmental/lifestyle exposures. Knowing the activity of mutational processes in a tumor can inform personalized therapies, early detection, and understanding of tumorigenesis. Computational methods have revealed 30 validated signatures of mutational processes active in human cancers, where each signature is a pattern of single base substitutions. However, half of these signatures have no known etiology, and some similar signatures have distinct etiologies, making patterns of mutation signature activity

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Katoh, Masuko, and Masaru Katoh. "Bioinformatics for Cancer Management in the Post-Genome Era." Technology in Cancer Research & Treatment 5, no. 2 (April 2006): 169–75. http://dx.doi.org/10.1177/153303460600500208.

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Human cancer is caused by multiple factors, such as genetic predisposition, chronic persistent inflammation, environmental factors, life style, and aging. Dysregulated proliferation, dysregulated adhesion, resistance to apoptosis, resistance to senescence, and resistance to anti-cancer drugs are features of cancer cells. Accumulation of multiple epigenetic changes and genetic alterations of cancer-associated genes during multi-stage carcinogenesis results in more malignant phenotypes. Post-genome science is characterized by omics data related to genome, transcriptome, proteome, metabolome, int

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Abdalwahid, Shadan Mohammed Jihad, Sami Ismael, and Shahab Wahhab Kareem. "Pre-Cancer Diagnosis via TP53 Gene Mutations Applied Ensemble Algorithms." Technium BioChemMed 2, no. 4 (September 9, 2021): 9–16. http://dx.doi.org/10.47577/biochemmed.v2i4.4654.

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According to current study, individuals with cancer who have a gene mutation have a bad prognosis. Young women with breast cancer had a poorer prognosis than older women, although it is unknown if the p53 gene mutation contributed to this. Due in part to the devastation of cancer, the appropriate technology may help cancer sufferers in regaining their lives. Researchers seek for mutations in cancer-causing gene sequences in order to identify the precancerous stage. While genetic testing may be used to forecast some kinds of cancer, there is presently no effective technique for identifying all

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McConkey, David J., and Woonyoung Choi. "Subtyping Bladder Cancers: Biology vs Bioinformatics." JNCI: Journal of the National Cancer Institute 110, no. 5 (January 12, 2018): 439–40. http://dx.doi.org/10.1093/jnci/djx254.

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Zhang, Chuan, Mandy Berndt-Paetz, and Jochen Neuhaus. "Bioinformatics Analysis Identifying Key Biomarkers in Bladder Cancer." Data 5, no. 2 (April 16, 2020): 38. http://dx.doi.org/10.3390/data5020038.

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Our goal was to find new diagnostic and prognostic biomarkers in bladder cancer (BCa), and to predict molecular mechanisms and processes involved in BCa development and progression. Notably, the data collection is an inevitable step and time-consuming work. Furthermore, identification of the complementary results and considerable literature retrieval were requested. Here, we provide detailed information of the used datasets, the study design, and on data mining. We analyzed differentially expressed genes (DEGs) in the different datasets and the most important hub genes were retrieved. We repor

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Sankar, Shanju, Sangeetha K. Nayanar, and Satheesan Balasubramanian. "Current Trends in Cancer Vaccines - a Bioinformatics Perspective." Asian Pacific Journal of Cancer Prevention 14, no. 7 (July 30, 2013): 4041–47. http://dx.doi.org/10.7314/apjcp.2013.14.7.4041.

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Grafström, Roland C., Rebecca Ceder, Bengt Fadeel, Karin Roberg, and Egon Willighagen. "Bioinformatics-based cancer research have wide toxicological applicability." Toxicology Letters 211 (June 2012): S160. http://dx.doi.org/10.1016/j.toxlet.2012.03.580.

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Pienta, K. J., and A. M. Chinnaiyan. "186 Bioinformatics and gene discovery in prostate cancer." European Journal of Cancer Supplements 7, no. 2 (September 2009): 47. http://dx.doi.org/10.1016/s1359-6349(09)70165-x.

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Hoheisel, Jörg. "Bioinformatics tools for molecular cancer diagnostics on microarrays." European Journal of Cancer Supplements 4, no. 6 (June 2006): 9. http://dx.doi.org/10.1016/j.ejcsup.2006.04.018.

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Manning, A. T., J. T. Garvin, R. I. Shahbazi, N. Miller, R. E. McNeill, and M. J. Kerin. "Molecular profiling techniques and bioinformatics in cancer research." European Journal of Surgical Oncology (EJSO) 33, no. 3 (April 2007): 255–65. http://dx.doi.org/10.1016/j.ejso.2006.09.002.

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Malik, Adeel, Hemajit Singh, Munazah Andrabi, Syed Akhtar Husain, and Shandar Ahmad. "Databases and QSAR for Cancer Research." Cancer Informatics 2 (January 2006): 117693510600200. http://dx.doi.org/10.1177/117693510600200002.

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In this review, we take a survey of bioinformatics databases and quantitative structure-activity relationship studies reported in published literature. Databases from the most general to special cancer-related ones have been included. Most commonly used methods of structure-based analysis of molecules have been reviewed, along with some case studies where they have been used in cancer research. This article is expected to be of use for general bioinformatics researchers interested in cancer and will also provide an update to those who have been actively pursuing this field of research.

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Huang, Chiang-Ching, Meijun Du, and Liang Wang. "Bioinformatics Analysis for Circulating Cell-Free DNA in Cancer." Cancers 11, no. 6 (June 11, 2019): 805. http://dx.doi.org/10.3390/cancers11060805.

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Molecular analysis of cell-free DNA (cfDNA) that circulates in plasma and other body fluids represents a “liquid biopsy” approach for non-invasive cancer screening or monitoring. The rapid development of sequencing technologies has made cfDNA a promising source to study cancer development and progression. Specific genetic and epigenetic alterations have been found in plasma, serum, and urine cfDNA and could potentially be used as diagnostic or prognostic biomarkers in various cancer types. In this review, we will discuss the molecular characteristics of cancer cfDNA and major bioinformatics ap

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Kim, So Yeon, Eun Kyung Choe, Manu Shivakumar, Dokyoon Kim, and Kyung-Ah Sohn. "Multi-layered network-based pathway activity inference using directed random walks: application to predicting clinical outcomes in urologic cancer." Bioinformatics 37, no. 16 (February 5, 2021): 2405–13. http://dx.doi.org/10.1093/bioinformatics/btab086.

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Abstract Motivation To better understand the molecular features of cancers, a comprehensive analysis using multi-omics data has been conducted. In addition, a pathway activity inference method has been developed to facilitate the integrative effects of multiple genes. In this respect, we have recently proposed a novel integrative pathway activity inference approach, iDRW and demonstrated the effectiveness of the method with respect to dichotomizing two survival groups. However, there were several limitations, such as a lack of generality. In this study, we designed a directed gene–gene graph u

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Zhang, Yuwei, Yang Tao, Huihui Ji, Wei Li, Xingli Guo, Derry Minyao Ng, Maria Haleem, et al. "Genome-wide identification of the essential protein-coding genes and long non-coding RNAs for human pan-cancer." Bioinformatics 35, no. 21 (March 27, 2019): 4344–49. http://dx.doi.org/10.1093/bioinformatics/btz230.

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Abstract Motivation Genome-scale CRISPR/Cas9 system has been a democratized gene editing technique and widely used to investigate gene functions in some biological processes and diseases especially cancers. Aiming to characterize gene aberrations and assess their effects on cancer, we designed a pipeline to identify the essential genes for pan-cancer. Methods CRISPR screening data were used to identify the essential genes that were collected from published data and integrated by Robust Rank Aggregation algorithm. Then, hypergeometrics test and random walks with restart (RWR) were used to predi

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Finney, Richard, and Daoud Meerzaman. "Chromatic: WebAssembly-Based Cancer Genome Viewer." Cancer Informatics 17 (January 1, 2018): 117693511877197. http://dx.doi.org/10.1177/1176935118771972.

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Chromatic is a novel web-browser tool that enables researchers to visually inspect genomic variations identified through next-generation sequencing of cancer data sets to determine whether such calls are, in fact, valid. It is the first cancer bioinformatics tool developed using WebAssembly technology, which comprises a portable, low-level byte code format that provides for the rapid execution of programs within supported web browsers. It has been designed expressly for ease of use by scientists without extensive expertise in bioinformatics.

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