title: A correlation between protein thermostability and resistance to proteolysis.

authors: Daniel, Roy M.

subjects: biology

abstract: The loss of activity due to proteolysis of purified L-asparaginase and beta-galactosidase from different sources correlates with the thermal instability of the enzymes. A similar correlation is found when populations of soluble proteins from micro-organisms grown at different temperatures are compared for proteolytic susceptibility and thermal stability. It is proposed that there is a general correlation between the thermostability of proteins and their resistance to proteolysis.

text: A correlation between protein thermostability and resistance to proteolysis. The loss of activity due to proteolysis of purified L-asparaginase and beta-galactosidase from different sources correlates with the thermal instability of the enzymes. A similar correlation is found when populations of soluble proteins from micro-organisms grown at different temperatures are compared for proteolytic susceptibility and thermal stability. It is proposed that there is a general correlation between the thermostability of proteins and their resistance to proteolysis.

year: 1982

title: Ensembles of nested dichotomies for multi-class problems

authors: Frank, Eibe

abstract: Nested dichotomies are a standard statistical technique for tackling certain polytomous classification problems with logistic regression. They can be represented as binary trees that recursively split a multi-class classification task into a system of dichotomies and provide a statistically sound way of applying two-class learning algorithms to multi-class problems (assuming these algorithms generate class probability estimates). However, there are usually many candidate trees for a given problem and in the standard approach the choice of a particular tree is based on domain knowledge that may not be available in practice. An alternative is to treat every system of nested dichotomies as equally likely and to form an ensemble classifier based on this assumption. We show that this approach produces more accurate classifications than applying C4.5 and logistic regression directly to multi-class problems. Our results also show that ensembles of nested dichotomies produce more accurate classifiers than pairwise classification if both techniques are used with C4.5, and comparable results for logistic regression. Compared to error-correcting output codes, they are preferable if logistic regression is used, and comparable in the case of C4.5. An additional benefit is that they generate class probability estimates. Consequently they appear to be a good general-purpose method for applying binary classifiers to multi-class problems.

text: Ensembles of nested dichotomies for multi-class problems Nested dichotomies are a standard statistical technique for tackling certain polytomous classification problems with logistic regression. They can be represented as binary trees that recursively split a multi-class classification task into a system of dichotomies and provide a statistically sound way of applying two-class learning algorithms to multi-class problems (assuming these algorithms generate class probability estimates). However, there are usually many candidate trees for a given problem and in the standard approach the choice of a particular tree is based on domain knowledge that may not be available in practice. An alternative is to treat every system of nested dichotomies as equally likely and to form an ensemble classifier based on this assumption. We show that this approach produces more accurate classifications than applying C4.5 and logistic regression directly to multi-class problems. Our results also show that ensembles of nested dichotomies produce more accurate classifiers than pairwise classification if both techniques are used with C4.5, and comparable results for logistic regression. Compared to error-correcting output codes, they are preferable if logistic regression is used, and comparable in the case of C4.5. An additional benefit is that they generate class probability estimates. Consequently they appear to be a good general-purpose method for applying binary classifiers to multi-class problems.

year: 2004-07-04

title: Te Ahi Kā

authors: Temara, Pou

subjects: Ahi kā

abstract: Ko te urutaumatua o tēnei tuhinga, ko te matapaki i te ahi kā. He aha tēnei tikanga? I pēhea i ara ake ai? He aha ōna mātāpono, ōna taipitopito? Koia te tūāpapa mō te tuhinga nei. Tuarua, ko te wānanga i te tikanga Māori me ōna rerenga pērā i te whakapapa. Ko te pātai ka puta i tēnei wāhanga o te wānanga ko tēnei nā, he aha te wāhi o te whakapapa ki te ahi kā? Ko te ngako o tēnei tuhinga ko te wetewete i te tikanga ahi kā kia whai tikanga ai te whakataukī nei, ‘ko te ahi kā te mana o te hapū ki te whenua’.

text: Te Ahi Kā Ko te urutaumatua o tēnei tuhinga, ko te matapaki i te ahi kā. He aha tēnei tikanga? I pēhea i ara ake ai? He aha ōna mātāpono, ōna taipitopito? Koia te tūāpapa mō te tuhinga nei. Tuarua, ko te wānanga i te tikanga Māori me ōna rerenga pērā i te whakapapa. Ko te pātai ka puta i tēnei wāhanga o te wānanga ko tēnei nā, he aha te wāhi o te whakapapa ki te ahi kā? Ko te ngako o tēnei tuhinga ko te wetewete i te tikanga ahi kā kia whai tikanga ai te whakataukī nei, ‘ko te ahi kā te mana o te hapū ki te whenua’.

year: 2012-05