What is he best known for?
The fields of study he is best known for:
- Internal medicine
- Cancer
- Gene
Michael W. Deininger mostly deals with Imatinib, Imatinib mesylate, Myeloid leukemia, Internal medicine and Cancer research.
Michael W. Deininger has included themes like Cytarabine, Chronic myelogenous leukemia, Pharmacology and Drug resistance in his Imatinib study.
His studies in Imatinib mesylate integrate themes in fields like Molecular biology, ABL and Bone marrow.
His Myeloid leukemia research is classified as research in Immunology.
His Internal medicine research is multidisciplinary, relying on both Gastroenterology, Surgery and Oncology.
His Cancer research research incorporates elements of Megakaryoblastic leukemia, Tyrosine kinase, Philadelphia chromosome and Tyrosine-kinase inhibitor.
His most cited work include:
- Five-year follow-up of patients receiving imatinib for chronic myeloid leukemia (2837 citations)
- The molecular biology of chronic myeloid leukemia (1350 citations)
- European LeukemiaNet recommendations for the management of chronic myeloid leukemia: 2013 (1271 citations)
What are the main themes of his work throughout his whole career to date?
Michael W. Deininger spends much of his time researching Cancer research, Imatinib, Internal medicine, Myeloid leukemia and Leukemia.
His studies deal with areas such as Tyrosine kinase, ABL, Kinase activity, Stem cell and Philadelphia chromosome as well as Cancer research.
His study brings together the fields of Chronic myelogenous leukemia and Imatinib.
He combines subjects such as Gastroenterology and Oncology with his study of Internal medicine.
His Myeloid leukemia research is within the category of Immunology.
His Leukemia study combines topics from a wide range of disciplines, such as Hematology, Cell culture, Chronic myelomonocytic leukemia and Drug resistance.
He most often published in these fields:
- Cancer research (47.60%)
- Imatinib (40.50%)
- Internal medicine (40.33%)
What were the highlights of his more recent work (between 2016-2021)?
- Internal medicine (40.33%)
- Cancer research (47.60%)
- Myeloid leukemia (40.00%)
In recent papers he was focusing on the following fields of study:
His primary scientific interests are in Internal medicine, Cancer research, Myeloid leukemia, Oncology and Imatinib.
In the field of Internal medicine, his study on Bosutinib, Adverse effect and Nilotinib overlaps with subjects such as In patient.
His research integrates issues of Cell culture, Apoptosis, Bcr abl1, Stem cell and Leukemia in his study of Cancer research.
His Myeloid leukemia research also works with subjects such as
- Tyrosine-kinase inhibitor, which have a strong connection to Ponatinib,
- Tyrosine kinase which connect with Drug resistance.
His research on Oncology often connects related areas such as Chronic myelogenous leukemia.
Michael W. Deininger works on Imatinib which deals in particular with Imatinib mesylate.
Between 2016 and 2021, his most popular works were:
- Long-Term Outcomes of Imatinib Treatment for Chronic Myeloid Leukemia (434 citations)
- Functional genomic landscape of acute myeloid leukaemia. (288 citations)
- Bosutinib Versus Imatinib for Newly Diagnosed Chronic Myeloid Leukemia: Results From the Randomized BFORE Trial (164 citations)
In his most recent research, the most cited papers focused on:
- Internal medicine
- Cancer
- Gene
Michael W. Deininger mainly focuses on Internal medicine, Myeloid leukemia, Cancer research, Leukemia and Myeloid.
He has researched Internal medicine in several fields, including Gastroenterology and Oncology.
His Myeloid leukemia research is multidisciplinary, incorporating elements of Tyrosine kinase and Philadelphia chromosome.
His Philadelphia chromosome research focuses on subjects like Cumulative incidence, which are linked to Dasatinib.
His Cancer research research includes themes of Progenitor cell, Apoptosis, ABL, Tyrosine-kinase inhibitor and Allosteric regulation.
Michael W. Deininger combines subjects such as Cancer and Pharmacology with his study of Imatinib.
This overview was generated by a machine learning system which analysed the scientist’s
body of work. If you have any feedback, you can
contact us
here.
