LAUSR

or oral presentation. A total of 19 papers were submitted and 6 were accepted (32% acceptance rate). All papers were anonymously peer-reviewed by at least two reviewers. A summary of the papers is as follows: Zheng Wang et al., “MASS: Protein Single-model global quality assessment using random forest and newly-designed statistical potentials”. This paper presents a singlemodel method named MASS for predicting global quality of individual protein models. The authors designed and re-implemented ten protein potentials and proved that these protein potentials are significantly different from each other. Using the values from ten potentials along with six other types of features, a random forest is trained to predict the global quality scores of individual models. MASS was evaluated along with other quality assessment methods in CASP11, CASP12, and CASP13 and the finding is that MASS outperforms most of the methods in CASP11 and is comparable with the leading methods in CASP12 and CASP13. Wren et al. BMC Bioinformatics 2020, 21(Suppl 4):254 Page 5 of 7