Florence d’Alché–Buc

4.7k total citations
34 papers, 965 citations indexed

About

Florence d’Alché–Buc is a scholar working on Molecular Biology, Artificial Intelligence and Signal Processing. According to data from OpenAlex, Florence d’Alché–Buc has authored 34 papers receiving a total of 965 indexed citations (citations by other indexed papers that have themselves been cited), including 16 papers in Molecular Biology, 11 papers in Artificial Intelligence and 4 papers in Signal Processing. Recurrent topics in Florence d’Alché–Buc's work include Bioinformatics and Genomic Networks (9 papers), Neural Networks and Applications (8 papers) and Gene Regulatory Network Analysis (7 papers). Florence d’Alché–Buc is often cited by papers focused on Bioinformatics and Genomic Networks (9 papers), Neural Networks and Applications (8 papers) and Gene Regulatory Network Analysis (7 papers). Florence d’Alché–Buc collaborates with scholars based in France, Belgium and Finland. Florence d’Alché–Buc's co-authors include Liva Ralaivola, Aurélien Mazurie, Samuele Bottani, Jacques Mallet, George Michailidis, Nicolas Brunel, Céline Brouard, Juho Rousu, Jean‐Pierre Nadal and Huibin Shen and has published in prestigious journals such as Nucleic Acids Research, Bioinformatics and PLoS ONE.

In The Last Decade

Florence d’Alché–Buc

34 papers receiving 933 citations

Peers — A (Enhanced Table)

Peers by citation overlap · career bar shows stage (early→late) cites · hero ref

Name h Career Trend Papers Cites
Florence d’Alché–Buc France 16 629 233 77 71 59 34 965
Stijn Meganck Belgium 10 596 0.9× 317 1.4× 53 0.7× 48 0.7× 27 0.5× 25 1.1k
Jonatan Taminau Belgium 8 673 1.1× 268 1.2× 139 1.8× 45 0.6× 25 0.4× 14 1.1k
Alioune Ngom Canada 18 540 0.9× 255 1.1× 122 1.6× 36 0.5× 74 1.3× 90 1.2k
Luis Rueda Canada 19 509 0.8× 289 1.2× 108 1.4× 36 0.5× 90 1.5× 122 1.1k
Genevera I. Allen United States 23 500 0.8× 310 1.3× 55 0.7× 92 1.3× 62 1.1× 62 1.3k
Yung‐Keun Kwon South Korea 19 680 1.1× 119 0.5× 101 1.3× 89 1.3× 34 0.6× 50 1.1k
David Steenhoff Belgium 5 557 0.9× 263 1.1× 41 0.5× 42 0.6× 16 0.3× 8 928
Robin Duqué Belgium 5 557 0.9× 263 1.1× 41 0.5× 42 0.6× 16 0.3× 8 928
Hagit Shatkay United States 19 994 1.6× 677 2.9× 94 1.2× 37 0.5× 33 0.6× 52 1.5k
Mélanie Hilario Switzerland 15 519 0.8× 666 2.9× 71 0.9× 25 0.4× 45 0.8× 34 1.4k

Countries citing papers authored by Florence d’Alché–Buc

Since Specialization
Citations

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

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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-authorship network of co-authors of Florence d’Alché–Buc

This figure shows the co-authorship network connecting the top 25 collaborators of Florence d’Alché–Buc. A scholar is included among the top collaborators of Florence d’Alché–Buc based on the total number of citations received by their joint publications. Widths of edges represent the number of papers authors have co-authored together. Node borders signify the number of papers an author published with Florence d’Alché–Buc. Florence d’Alché–Buc is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

20 of 20 papers shown
1.
Flamary, Rémi, et al.. (2023). Wind power predictions from nowcasts to 4-hour forecasts: A learning approach with variable selection. Renewable Energy. 211. 938–947. 10 indexed citations
2.
Hennequin, Romain, et al.. (2020). Audio-Based Detection of Explicit Content in Music. SPIRE - Sciences Po Institutional REpository. 526–530. 8 indexed citations
3.
Brouard, Céline, et al.. (2019). Improved Small Molecule Identification through Learning Combinations of Kernel Regression Models. Metabolites. 9(8). 160–160. 15 indexed citations
4.
Heinonen, Markus, Fabien Milliat, Mohamed Amine Benadjaoud, et al.. (2018). Temporal clustering analysis of endothelial cell gene expression following exposure to a conventional radiotherapy dose fraction using Gaussian process clustering. PLoS ONE. 13(10). e0204960–e0204960. 5 indexed citations
5.
Rouet, Laurence, et al.. (2016). Parameter estimation of perfusion models in dynamic contrast-enhanced imaging: a unified framework for model comparison. Medical Image Analysis. 35. 360–374. 12 indexed citations
6.
Chatagnon, Amandine, Philippe Veber, Valérie Morin, et al.. (2015). RAR/RXR binding dynamics distinguish pluripotency from differentiation associated cis-regulatory elements. Nucleic Acids Research. 43(10). 4833–4854. 67 indexed citations
7.
Heinonen, Markus, Olivier Guipaud, Fabien Milliat, et al.. (2014). Detecting time periods of differential gene expression using Gaussian processes: an application to endothelial cells exposed to radiotherapy dose fraction. Bioinformatics. 31(5). 728–735. 28 indexed citations
8.
d’Alché–Buc, Florence, et al.. (2014). Operator-valued kernel-based vector autoregressive models for network inference. Machine Learning. 99(3). 489–513. 16 indexed citations
9.
Brouard, Céline, Christel Vrain, Julie Dubois, et al.. (2013). Learning a Markov Logic network for supervised gene regulatory network inference. BMC Bioinformatics. 14(1). 273–273. 11 indexed citations
10.
Michailidis, George & Florence d’Alché–Buc. (2013). Autoregressive models for gene regulatory network inference: Sparsity, stability and causality issues. Mathematical Biosciences. 246(2). 326–334. 60 indexed citations
11.
Letort, Véronique, et al.. (2013). A Multi-task Learning Approach for Compartmental Model Parameter Estimation in DCE-CT Sequences. Lecture notes in computer science. 16(Pt 2). 271–278. 2 indexed citations
12.
Frouin, Vincent, et al.. (2008). Evolutionary approaches for the reverse-engineering of gene regulatory networks: A study on a biologically realistic dataset. BMC Bioinformatics. 9(1). 91–91. 20 indexed citations
13.
d’Alché–Buc, Florence & Louis Wehenkel. (2008). Machine Learning in Systems Biology. BMC Proceedings. 2(S4). S1–S1. 8 indexed citations
14.
Geurts, Pierre, Nizar Touleimat, Marie Dutreix, & Florence d’Alché–Buc. (2007). Inferring biological networks with output kernel trees. BMC Bioinformatics. 8(S2). S4–S4. 22 indexed citations
15.
Quiñonero-Candela, Joaquin, Ido Dagan, Bernardo Magnini, & Florence d’Alché–Buc. (2006). Machine Learning Challenges: Evaluating Predictive Uncertainty, Visual Object Classification, and Recognizing Textual EntailmentFirst Pascal Machine Learning ... / Lecture Notes in Artificial Intelligence). Springer eBooks. 1 indexed citations
16.
Ralaivola, Liva & Florence d’Alché–Buc. (2006). Time series filtering, smoothing and learning using the kernel kalman filter. Proceedings. 2005 IEEE International Joint Conference on Neural Networks, 2005.. 3. 1449–1454. 34 indexed citations
17.
Ralaivola, Liva & Florence d’Alché–Buc. (2003). Dynamical Modeling with Kernels for Nonlinear Time Series Prediction. Neural Information Processing Systems. 16. 129–136. 39 indexed citations
18.
Ralaivola, Liva, et al.. (2003). Gene networks inference using dynamic Bayesian networks. Bioinformatics. 19(suppl_2). ii138–ii148. 294 indexed citations
19.
d’Alché–Buc, Florence & Jean‐Pierre Nadal. (1995). Asymptotic performances of a constructive algorithm. Neural Processing Letters. 2(2). 1–4. 5 indexed citations
20.
d’Alché–Buc, Florence, et al.. (1994). TRIO LEARNING: A NEW STRATEGY FOR BUILDING HYBRID NEURAL TREES. International Journal of Neural Systems. 5(4). 259–274. 20 indexed citations

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.

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