Improved classification of crystallization images using data fusion and multiple classifiers

TY - JOUR

T1 - Improved classification of crystallization images using data fusion and multiple classifiers

AU - Buchala, Samarasena

AU - Wilson, Julie C.

PY - 2008/8/17

Y1 - 2008/8/17

N2 - Identifying the conditions that will produce diffraction-quality crystals can require very many crystallization experiments. The use of robots has increased the number of experiments performed in most laboratories, while in structural genomics centres tens of thousands of experiments can be produced every day. Reliable automated evaluation of these experiments is becoming increasingly important. A more robust classification is achieved by combining different methods of feature extraction with the use of multiple classifiers.

AB - Identifying the conditions that will produce diffraction-quality crystals can require very many crystallization experiments. The use of robots has increased the number of experiments performed in most laboratories, while in structural genomics centres tens of thousands of experiments can be produced every day. Reliable automated evaluation of these experiments is becoming increasingly important. A more robust classification is achieved by combining different methods of feature extraction with the use of multiple classifiers.

KW - PROTEIN-CRYSTALLIZATION

KW - MACROMOLECULAR CRYSTALLIZATION

KW - AUTOMATED CLASSIFICATION

KW - TRIALS

KW - TEXTURE

UR - http://www.scopus.com/inward/record.url?scp=47749133788&partnerID=8YFLogxK

U2 - 10.1107/S0907444908014273

DO - 10.1107/S0907444908014273

M3 - Article

SN - 0907-4449

VL - 64

SP - 823

EP - 833

JO - Acta Crystallographica. Section D, Biological Crystallography

JF - Acta Crystallographica. Section D, Biological Crystallography

IS - 8

ER -