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Fingerprint Dive into the research topics where Halima Bensmail is active. These topic labels come from the works of this person. Together they form a unique fingerprint.

  • 4 Similar Profiles
Proteins Engineering & Materials Science
Proteomics Medicine & Life Sciences
Genes Engineering & Materials Science
Feature extraction Engineering & Materials Science
Learning Medicine & Life Sciences
Cluster Analysis Medicine & Life Sciences
Protein Mathematics
Qatar Medicine & Life Sciences

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Research Output 1996 2019

  • 770 Citations
  • 14 h-Index
  • 32 Article
  • 18 Conference contribution
  • 3 Chapter
  • 2 Comment/debate

Clustme: A visual quality measure for ranking monochrome scatterplots based on cluster patterns

Abbas, M., Aupetit, M., Sedlmair, M. & Bensmail, H., 1 Jan 2019, In : Computer Graphics Forum. 38, 3, p. 225-236 12 p.

Research output: Contribution to journalArticle

Merging
Physical Chemistry
Physical chemistry
machine learning
physical chemistry
Physics

Deepcrystal: A deep learning framework for sequence-based protein crystallization prediction

Elbasir, A., Moovarkumudalvan, B., Kunji, K., Kolatkar, P., Mall, R. & Bensmail, H., 1 Jul 2019, In : Bioinformatics. 35, 13, p. 2216-2225 10 p.

Research output: Contribution to journalArticle

Crystallization
Learning
Proteins
Protein
Prediction

DeepCrystal: A Deep Learning Framework for Sequence-based Protein Crystallization Prediction

Elbasir, A., Moovarkumudalvan, B., Kunji, K., Kolatkar, P., Bensmail, H. & Mall, R., 21 Jan 2019, Proceedings - 2018 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2018. Schmidt, H., Griol, D., Wang, H., Baumbach, J., Zheng, H., Callejas, Z., Hu, X., Dickerson, J. & Zhang, L. (eds.). Institute of Electrical and Electronics Engineers Inc., p. 2747-2749 3 p. 8621202. (Proceedings - 2018 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2018).

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Crystallization
Learning
Proteins
Diffraction
Crystals
2 Citations (Scopus)

Exploring new approaches towards the formability of mixed-ion perovskites by DFT and machine learning

Park, H., Mall, R., Alharbi, F., Sanvito, S., Tabet, N., Bensmail, H. & El-Mellouhi, F., 1 Jan 2019, In : Physical Chemistry Chemical Physics. 21, 3, p. 1078-1088 11 p.

Research output: Contribution to journalArticle

machine learning
perovskites
Formability
Discrete Fourier transforms
learning