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Машинное обучение с применением множественной логистической регрессии для предсказания антимикробных и гемолитических пептидов и их обнаружения в крупных белках

Figure 1 - Prediction of HNP1 localization in its precursor protein: 

l is the rightmost a.a. in the sliding window, pAntimicrobial is probability of antimicrobial activity, L' is the sliding window length (A – 20 a.a.; B – 30 a.a. and C – 50 a.a.)

Prediction of HNP1 localization in its precursor protein: l is the rightmost a.a. in the sliding window, pAntimicrobial is probability of antimicrobial activity, L' is the sliding window length (A – 20 a.a.; B – 30 a.a. and C – 50 a.a.)

the actual mature HNP1 length is 30 a.a.; the dashed horizontal line represents the estimated optimal cut-off for AMPs prediction (0.6)