J Biomed Inform - Screening drug target proteins based on sequence information.

Tópicos

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{ ehr(2073) health(1662) electron(1139) }
{ patient(2837) hospit(1953) medic(668) }
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{ medic(1828) order(1363) alert(1069) }
{ signal(2180) analysi(812) frequenc(800) }
{ cost(1906) reduc(1198) effect(832) }
{ group(2977) signific(1463) compar(1072) }
{ sampl(1606) size(1419) use(1276) }
{ first(2504) two(1366) second(1323) }
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{ activ(1138) subject(705) human(624) }
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{ process(1125) use(805) approach(778) }

Resumo

Identifying new drug target (DT) proteins is important in pharmaceutical and biomedical research. General machine learning method (GMLM) classifiers perform fairly well at prediction if the training dataset is well prepared. However, a common problem in preparing the training dataset is the lack of a negative dataset. To address this problem, we proposed two methods that can help GMLM better select the negative training dataset from the test dataset. The prediction accuracy was improved with the training dataset from the proposed strategies. The classifier identified 1797 and 227 potential DT proteins, some of which were mentioned in previous research, which added correlative weight to the new method. Practically, these two sets of potential DT proteins or their homologues are worth considering.

Resumo Limpo

identifi new drug target dt protein import pharmaceut biomed research general machin learn method gmlm classifi perform fair well predict train dataset well prepar howev common problem prepar train dataset lack negat dataset address problem propos two method can help gmlm better select negat train dataset test dataset predict accuraci improv train dataset propos strategi classifi identifi potenti dt protein mention previous research ad correl weight new method practic two set potenti dt protein homologu worth consid

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