review article | Q7318358 |
scholarly article | Q13442814 |
P2093 | author name string | Ying Yang | |
Jingjing Guo | |||
Dongxiao Gu | |||
Xiaoyu Wang | |||
Xuejie Yang | |||
Keyu Zhu | |||
P2860 | cites work | Prospective identification of tumorigenic breast cancer cells | Q24683474 |
Influential journals in health research: a bibliometric study | Q26779519 | ||
Global cancer statistics, 2002 | Q27860562 | ||
Macrophage diversity enhances tumor progression and metastasis | Q29615847 | ||
Visualizing the knowledge structure and evolution of big data research in healthcare informatics | Q30239890 | ||
Data mining with decision trees for diagnosis of breast tumor in medical ultrasonic images | Q30652536 | ||
Integrating text mining, data mining, and network analysis for identifying genetic breast cancer trends | Q31084513 | ||
Big data: How do your data grow? | Q33366386 | ||
Cancer studies based on secondary data analysis of the Taiwan's National Health Insurance Research Database: A computational text analysis and visualization study | Q33627140 | ||
Analyzing the field of bioinformatics with the multi-faceted topic modeling technique. | Q33802210 | ||
Publication trends of shared decision making in 15 high impact medical journals: a full-text review with bibliometric analysis | Q34059826 | ||
Comparison of PubMed, Scopus, Web of Science, and Google Scholar: strengths and weaknesses. | Q34691513 | ||
A "big data" view of the tumor "immunome" | Q35020606 | ||
A bibliometric analysis of cancer research in South Africa: study protocol | Q35088880 | ||
Identifying Liver Cancer and Its Relations with Diseases, Drugs, and Genes: A Literature-Based Approach | Q36022138 | ||
Computational prediction of multidisciplinary team decision-making for adjuvant breast cancer drug therapies: a machine learning approach | Q36209045 | ||
Trends of triple negative breast cancer research (2007-2015): A bibliometric study | Q37433105 | ||
Cloud computing in medical imaging. | Q38119000 | ||
Unobtrusive sensing and wearable devices for health informatics | Q38206655 | ||
Predictions of skin cancer incidence in the Netherlands up to 2015. | Q40435896 | ||
Predicting neuroendocrine tumor (carcinoid) neoplasia using gene expression profiling and supervised machine learning. | Q42050095 | ||
Citation analysis and impact factor trends of 5 core journals in occupational medicine, 1985-2006. | Q46685214 | ||
Deep Learning for Tumor Classification in Imaging Mass Spectrometry. | Q47308076 | ||
Dialogues on cancer survivorship: a new model of international cooperation | Q47630743 | ||
A case-based reasoning system based on weighted heterogeneous value distance metric for breast cancer diagnosis. | Q47829493 | ||
Union for International Cancer Control International Session: healthcare economics: the significance of the UN Summit non-communicable diseases political declaration in Asia. | Q51214654 | ||
Wearable devices for telemedicine applications. | Q51393744 | ||
A bibliometric analysis of natural language processing in medical research. | Q52626036 | ||
Radiation Therapy Research: A Global Analysis 2001-2015 | Q57175961 | ||
Cancer research in India: national priorities, global results | Q61896761 | ||
Exploiting semantic patterns over biomedical knowledge graphs for predicting treatment and causative relations. | Q64987443 | ||
Classification of isolated tumor cells: clarification of the 6th edition of the American Joint Committee on Cancer Staging Manual | Q79360287 | ||
P433 | issue | 12 | |
P921 | main subject | emerging technology | Q120208 |
P304 | page(s) | 2643-2653 | |
P577 | publication date | 2019-06-02 | |
P1433 | published in | Journal of Cancer | Q6294901 |
P1476 | title | Tracking knowledge evolution, hotspots and future directions of emerging technologies in cancers research: a bibliometrics review | |
P478 | volume | 10 |
Q90745697 | Bibliometric Analysis of Dendritic Epidermal T Cell (DETC) Research From 1983 to 2019 | cites work | P2860 |
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