Florence d’Alché–Buc
Impact in
- Health Informatics top 10%
- Artificial Intelligence top 5%
- Neural Networks and Applications
- Machine Learning and Data Classification
Papers in
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- Neural Networks and Applications 10
- Machine Learning and Data Classification 6
- Machine Learning and Algorithms 5
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- Gene Regulatory Network Analysis 8
- Bioinformatics and Genomic Networks 6
- Co-authors
- Liva Ralaivola (4 shared papers)Jacques Mallet (1 shared paper)Samuele Bottani (1 shared paper)Aurélien Mazurie (1 shared paper)George Michailidis (3 shared papers)Nicolas Brunel (2 shared papers)Christophe Ambroise (2 shared papers)Yves Grandvalet (2 shared papers)
In The Last Decade
Florence d’Alché–Buc
47 papers receiving 1.2k citations
Peers
Comparison fields: 5 of 123
- Health Informatics 16
- Artificial Intelligence 410
- Molecular Biology 655
- Signal Processing 85
- Computer Vision and Pattern Recognition 141
Countries citing papers authored by Florence d’Alché–Buc
This map shows the geographic impact of Florence d’Alché–Buc's research. It shows the number of citations coming from papers published by authors working in each country. You can also color the map by specialization and compare the number of citations received by Florence d’Alché–Buc with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Florence d’Alché–Buc more than expected).
Fields of papers citing papers by Florence d’Alché–Buc
This network shows the impact of papers produced by Florence d’Alché–Buc. Nodes represent research fields, and links connect fields that are likely to share authors. Colored nodes show fields that tend to cite the papers produced by Florence d’Alché–Buc. The network helps show where Florence d’Alché–Buc may publish in the future.
Co-authors
The 25 scholars most cited alongside Florence d’Alché–Buc, linked wherever they have co-authored with each other. Click a name or a connecting line to browse the papers they share.
All Works
Showing the 20 most-cited of 48 papers — load more, or switch the sort, to bring in the rest.
| # | Work | ||
|---|---|---|---|
| 1 | 2003 | 333 | |
| 2 | 2007 | 110 | |
| 3 | 2001 | 86 | |
| 4 | 2002 | 75 | |
| 5 | 2015 | 72 | |
| 6 | 2016 | 64 | |
| 7 | 2013 | 62 | |
| 8 | 2020 | 43 | |
| 9 | Dynamical Modeling with Kernels for Nonlinear Time Series Prediction | 2003 | 42 |
| 10 | 2006 | 37 | |
| 11 | 2014 | 29 | |
| 12 | 2013 | 25 | |
| 13 | 2007 | 24 | |
| 14 | 1994 | 24 | |
| 15 | 2008 | 23 | |
| 16 | 2009 | 19 | |
| 17 | 1997 | 19 | |
| 18 | 2014 | 17 | |
| 19 | 2019 | 15 | |
| 20 | 2001 | 14 |
About Florence d’Alché–Buc
Florence d’Alché–Buc is a scholar working on Artificial Intelligence, Molecular Biology, Computer Vision and Pattern Recognition, Signal Processing and Radiology, Nuclear Medicine and Imaging, having authored 48 papers that have together received 1.3k indexed citations. Recurring topics across this work include Neural Networks and Applications (10 papers), Gene Regulatory Network Analysis (8 papers), Bioinformatics and Genomic Networks (6 papers), Machine Learning and Data Classification (6 papers), Machine Learning and Algorithms (5 papers), MRI in cancer diagnosis (4 papers), Radiomics and Machine Learning in Medical Imaging (4 papers) and Face and Expression Recognition (3 papers). The work is most often cited by research in Health Informatics (16 citations), Artificial Intelligence (410 citations), Molecular Biology (655 citations), Signal Processing (85 citations) and Computer Vision and Pattern Recognition (141 citations). Florence d’Alché–Buc has collaborated with scholars based in France, Belgium and Finland. Frequent co-authors include Liva Ralaivola, Jacques Mallet, Samuele Bottani, Aurélien Mazurie, George Michailidis, Nicolas Brunel, Christophe Ambroise, Yves Grandvalet, Céline Brouard and Jean‐Pierre Nadal. Their work appears in journals such as Bioinformatics, BMC Bioinformatics, International Journal of Neural Systems, Lecture notes in computer science and IEEE/ACM Transactions on Audio Speech and Language Processing.
Rankless uses publication and citation data sourced from OpenAlex, an open and comprehensive bibliographic database. While OpenAlex provides broad and valuable coverage of the global research landscape, it—like all bibliographic datasets—has inherent limitations. These include incomplete records, variations in author disambiguation, differences in journal indexing, and delays in data updates. As a result, some metrics and network relationships displayed in Rankless may not fully capture the entirety of a scholar's output or impact.