20). wg-blimp: an end-to-end analysis pipeline for whole genome bisulfite

uencing data, BMC Bioinformatics, 21, pp. 169.

W. and Simon, R. M. (2003). A random variance model for detection of

ferential gene expression in small microarray experiments, Bioinformatics, 19,

2448–2455.

07). Cancer outlier differential gene expression detection, Biostatistics, 8, pp.

–575.

rry, M. Shivakumar, S. and McLarty, J. (1995). Neural networks for full-scale

tein sequence classification: sequence encoding with singular value

omposion, Machine Learning, 21, pp. 177–193.

ang, X., He, Z. and Hou, L. (2019). Identifying candidate diagnostic markers

early stage of non-small cell lung cancer, PLoS One, 14, pp. e0225080.

n, Q., Su, S., Wang, X., Xu, W., Liu, Z., Zhu, Y., Wang, Q., Lu, L. and Jiang,

2020). The role of furin cleavage site in SARS-CoV-2 spike protein-mediated

mbrane fusion in the presence or absence of trypsin, Signal Transduction and

geted Therapy, 5, pp. 92.

Wong, L. (2021). PDR: a new genome assembly evaluation metric based on

etics concerns, Bioinformatics, (in press).

Kossenkov, A. V., Knecht, V. R., Showe, L. C., Ratcliffe, S. J., Montaner, L.

Tebas, P. and Collman, R. G. (2019). Evidence for persistent monocyte and

mune dysregulation after prolonged viral suppression despite normalization of

nocyte subsets, sCD14 and sCD163 in HIV-infected individuals, Pathogens and

munity, 4, pp. 324 – 362.

Huang, M., Li, S., Chen, J., Yang, Y., Qin, N., Huang, D. and Shu, J. (2020).

diomics model of magnetic resonance imaging for predicting pathological

ding and lymph node metastases of extrahepatic cholangiocarcinoma, Cancer

ters, 470, pp. 1–7.

H and Yang, Z. R. (2013). Prediction of heterogenous differential genes by

ecting outliers to a Gaussian tight cluster, BMC Bioinformatics, 14, pp. 81.

R. (2005). Mining SARS-CoV protease cleavage data using non-orthogonal

ision trees: a novel method for decisive template selection, Bioinformatics, 21,

2644–2650.

(2005b). Prediction of caspase cleavage sites using Bayesian bio-basis function

ral networks, Bioinformatics, 21, pp. 1831–1837.

. (2005c). Orthogonal kernel machine for the prediction of functional sites in

teins, IEEE Trans on Systems and Cybernetics B, 35, pp. 100–106.

R., Bullifent, H. L., Moore, K., Paszkiewicz, K., Saint, R. J., Southern, S. J.,

ampion, O. L., Senior, N. J., Sarkar-Tyson, M., Oyston, P. C., Atkins, T. P. and

ball, R. W. (2017). A noise trimming and positional significance of transposon

ertion system to identify essential genes in Yersinia pestis. Scientific Reports, 7,

41923.

R. and Berry, E. A. (2004). Reduced bio-basis function neural networks for

tease cleavage site prediction, Journal of Bioinformatics and Computational

logy, 2, pp. 511–531.