Detection of the Challenging mutations at 1% VAF and 50 ng DNA input. by Lizhi Yu (12476136)

IC₅₀s data of antimalarials against controls. by Winnie Okore (14139712)

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Drugs IC₅₀s data during the study period. by Winnie Okore (14139712)

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SNPs IC₅₀s data at codon 876 during the study period. by Winnie Okore (14139712)

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Association of polymorphic IC_50s versus wild<i>-</i>type at codon 876 in selected drugs tested. by Winnie Okore (14139712)

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Effect of melatonin (50 μM) on the TGF-β1 (10 ng/mL, 72 h)-mediated TGF-β1/Smad pathway and EMT in HBdSMCs. by Yang Zhang (30734)

ROC curve and P-R curve (attack traffic proportion = 50%). by Longyan Ran (18452261)

Detailed meta–analyses in untreated hypothyroid patients compared with controls for each HRV parameters: RR intervals, SDNN, RMSSD, pNN50, TP, LF, HF, LF/HF. by Valentin Brusseau (12769704)

Number of samples per site during the study period. by Winnie Okore (14139712)

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Performance of selected antimalarials against field isolates during the study period. by Winnie Okore (14139712)

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<i>Pfmrp1</i> gene SNPs data during the study period. by Winnie Okore (14139712)

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Performance of selected drugs against field isolates during the study period. by Winnie Okore (14139712)

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Frequency of SNPs in <i>Pfmrp1</i> gene in field isolates during the study period. by Winnie Okore (14139712)

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<i>In vitro</i> 95% CI lower and upper mean susceptibility responses of field isolates to antimalarials. by Winnie Okore (14139712)

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<i>In vitro</i> susceptibility responses of field isolates to antimalarials during the study period. by Winnie Okore (14139712)

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Primers used for analysis of four SNPs in one gene. by Winnie Okore (14139712)

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Stability scores with refinement and no USM decreases. by Jedediah M. Singer (10331212)

“…<p>Effects of guided and random substitutions within expert-designed proteins, when guided substitutions are not permitted to decrease USM EC₅₀ predictions.</p> <p>(EPS)</p>…”

The decrease in blood flow disrupts CV pruning. by Lin Wen (557398)

Predictions of random forests (RF), gradient boosting machines (GBM), and neural networks (NN) trained on 12 covariates chosen using subjective feature selection. by Dennis Ochola (11626912)

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Predictions of random forests (RF), gradient boosting machines (GBM), and neural networks (NN) trained on 17 covariates selected after recursive feature selection. by Dennis Ochola (11626912)

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