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Pacific Symposium on Biocomputing. Pacific Symposium on Biocomputing|July 21, 2000
Stochastic heuristic algorithms for target motif identification (extended abstract)H T Wareham, T Jiang, X Zhang, et al.Pacific Symposium on Biocomputing. Pacific Symposium on Biocomputing|July 21, 2000
In silico analysis of gene expression patterns during early development of Xenopus laevisN Pollet, H A Schmidt, V Gawantka, et al.Pacific Symposium on Biocomputing. Pacific Symposium on Biocomputing|July 21, 2000
The haplotype linkage disequilibrium test for genome-wide screens: its power and study designM Xiong, J Akey, L JinPacific Symposium on Biocomputing. Pacific Symposium on Biocomputing|December 5, 2019
Disordered Function Conjunction: On the in-silico function annotation of intrinsically disordered regionsSina Ghadermarzi, Akila Katuwawala, Christopher J Oldfield, et al.Pacific Symposium on Biocomputing. Pacific Symposium on Biocomputing|December 5, 2019
Genome Gerrymandering: optimal division of the genome into regions with cancer type specific differences in mutation ratesAdamo Young, Jacob Chmura, Yoonsik Park, et al.Pacific Symposium on Biocomputing. Pacific Symposium on Biocomputing|December 5, 2019
Learning a Latent Space of Highly Multidimensional Cancer DataBenjamin Kompa, Beau CokerPacific Symposium on Biocomputing. Pacific Symposium on Biocomputing|December 5, 2019
Tree-Weighting for Multi-Study Ensemble LearnersMaya Ramchandran, Prasad Patil, Giovanni ParmigianiPacific Symposium on Biocomputing. Pacific Symposium on Biocomputing|December 5, 2019
Network Representation of Large-Scale Heterogeneous RNA Sequences with Integration of Diverse Multi-omics, Interactions, and Annotations DataNhat Tran, Jean GaoPacific Symposium on Biocomputing. Pacific Symposium on Biocomputing|December 5, 2019
Enhancing Model Interpretability and Accuracy for Disease Progression Prediction via Phenotype-Based Patient Similarity LearningYue Wang, Tong Wu, Yunlong Wang, et al.Pacific Symposium on Biocomputing. Pacific Symposium on Biocomputing|December 5, 2019
AnomiGAN: Generative Adversarial Networks for Anonymizing Private Medical DataHo Bae, Dahuin Jung, Hyun-Soo Choi, et al.Pageof 150