Knowledge discovery and interactive data mining in bioinformatics-state-of-the-art, future challenges and research directions
Background The life sciences, biomedicine and health care are increasingly turning into a
data intensive science [2–4]. Particularly in bioinformatics and computational biology we …
data intensive science [2–4]. Particularly in bioinformatics and computational biology we …
Classification of crystallization outcomes using deep convolutional neural networks
The Machine Recognition of Crystallization Outcomes (MARCO) initiative has assembled
roughly half a million annotated images of macromolecular crystallization experiments from …
roughly half a million annotated images of macromolecular crystallization experiments from …
Computational crystallization
Crystallization is a key step in macromolecular structure determination by crystallography.
While a robust theoretical treatment of the process is available, due to the complexity of the …
While a robust theoretical treatment of the process is available, due to the complexity of the …
GPU-accelerated features extraction from magnetic resonance images
HY Tsai, H Zhang, CL Hung, G Min - IEEE Access, 2017 - ieeexplore.ieee.org
The use of a graphics processing unit (GPU) together with a CPU, referred as GPU-
accelerated computing, to accelerate tasks that requires extensive computations has been …
accelerated computing, to accelerate tasks that requires extensive computations has been …
[PDF][PDF] Crystal search–feasibility study of a real-time deep learning process for crystallization well images
Y Thielmann, T Luft, N Zint, J Koepke - … Crystallographica Section A …, 2023 - journals.iucr.org
To avoid the time-consuming and often monotonous task of manual inspection of
crystallization plates, a Python-based program to automatically detect crystals in …
crystallization plates, a Python-based program to automatically detect crystals in …
Towards generic image classification using tree-based learning: An extensive empirical study
This paper considers the general problem of image classification without using any prior
knowledge about image classes. We study variants of a method based on supervised …
knowledge about image classes. We study variants of a method based on supervised …
Towards generic image classification: an extensive empirical study
This paper considers the general problem of image classification without using any prior
knowledge about image classes. We study variants of a method based on supervised …
knowledge about image classes. We study variants of a method based on supervised …
Prediction of protein x-ray crystallisation trial image time-courses
BMT Lekamge, A Sowmya, J Newman - International Conference on …, 2017 - scitepress.org
This paper presents an algorithm to predict the outcome of a protein x-ray crystallisation trial.
Results obtained from classification of individual images in a time-course are used, along …
Results obtained from classification of individual images in a time-course are used, along …
Classification of protein crystallisation images using texture-based statistical features
BMT Lekamge, A Sowmya, K Mele, VJ Fazio… - AIP Conference …, 2013 - pubs.aip.org
We report on the classification of protein crystallisation data via decision trees using N-fold
cross validation. Protein crystallisation images were obtained over a period of time ranging …
cross validation. Protein crystallisation images were obtained over a period of time ranging …
Multi-view Learning for Classification of X-Ray Crystallography Images
BM Thamali Lekamge, A Sowmya… - … Conference on Machine …, 2016 - Springer
Multi-view learning is a very useful classification technique when multiple, conditionally
independent feature sets are available in a dataset. In this paper multi-view learning is used …
independent feature sets are available in a dataset. In this paper multi-view learning is used …