Figure 4

Screening for potential relationships among factors for energy landscape analysis (ELA) and refinement by other machine learning (ML) approaches. (a) The ELAassociated with antibiotic treatment is visualized. The axis formed the energy landscape with compositional energy, community state, and treatment time (days). (b) The concept of the stable state in ELA is shown. Each green circle indicates a constituent element (component) within an interaction network (community). The blue and red lines show positive and negative effects between the components, respectively. Each interaction network was different depending on the energy state. (c) Response to environmental ε. Dependencies on the developmental stage (\({g}_{i}^{d}\)) (X axis) and responsiveness to antibiotic treatment (\({g}_{i}^{a}\)) (Y axis) are plotted. Four categories are shown as Groups I-IV. The bacteria categorized within Group I had low population levels at 30–60 d that increased after with antibiotic treatment. Those within Group II had high population levels at 30–60 d that increased with antibiotic treatment. Those within Group III had low population levels at 30–60 d that decreased with antibiotic treatment. Those within Group IV had high population levels at 30–60 d and/or an independent population at this stage, but the population levels decreased with antibiotic treatment. The black letters indicate the bacterial genera and the other physiological components selected by LDA and AA.