Representation of features as images with neighborhood dependencies for compatibility with convolutional neural networks

[1]  Seongok Ryu,et al.  Predicting Drug-Target Interaction Using a Novel Graph Neural Network with 3D Structure-Embedded Graph Representation , 2019, J. Chem. Inf. Model..

[2]  Nitesh V. Chawla,et al.  Heterogeneous Graph Neural Network , 2019, KDD.

[3]  S. Anders,et al.  Focused multidimensional scaling: interactive visualization for exploration of high-dimensional data , 2019, BMC Bioinformatics.

[4]  Xinyi Liu,et al.  Deep-Resp-Forest: A deep forest model to predict anti-cancer drug response. , 2019, Methods.

[5]  Andre Esteva,et al.  A guide to deep learning in healthcare , 2019, Nature Medicine.

[6]  N. Razavian,et al.  Classification and mutation prediction from non–small cell lung cancer histopathology images using deep learning , 2018, Nature Medicine.

[7]  Michael Wainberg,et al.  Deep learning in biomedicine , 2018, Nature Biotechnology.

[8]  Alexander Binder,et al.  Deep One-Class Classification , 2018, ICML.

[9]  Tae Soon Kim,et al.  Cancer Drug Response Profile scan (CDRscan): A Deep Learning Model That Predicts Drug Effectiveness from Cancer Genomic Signature , 2018, Scientific Reports.

[10]  Zhen Zhang,et al.  OmicsMapNet: Transforming omics data to take advantage of Deep Convolutional Neural Network for discovery , 2018, ArXiv.

[11]  Ranadip Pal,et al.  Heterogeneity Aware Random Forest for Drug Sensitivity Prediction , 2017, Scientific Reports.

[12]  Ranadip Pal,et al.  Investigation of model stacking for drug sensitivity prediction , 2017, BMC Bioinformatics.

[13]  Ranadip Pal,et al.  IntegratedMRF: random forest‐based framework for integrating prediction from different data types , 2017, Bioinform..

[14]  Zhenchang Xing,et al.  Predicting semantically linkable knowledge in developer online forums via convolutional neural network , 2016, 2016 31st IEEE/ACM International Conference on Automated Software Engineering (ASE).

[15]  O. Stegle,et al.  Deep learning for computational biology , 2016, Molecular systems biology.

[16]  Forrest N. Iandola,et al.  SqueezeNet: AlexNet-level accuracy with 50x fewer parameters and <1MB model size , 2016, ArXiv.

[17]  B. Frey,et al.  Predicting the sequence specificities of DNA- and RNA-binding proteins by deep learning , 2015, Nature Biotechnology.

[18]  Geoffrey E. Hinton,et al.  Deep Learning , 2015, Nature.

[19]  Sergey Ioffe,et al.  Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift , 2015, ICML.

[20]  Nci Dream Community A community effort to assess and improve drug sensitivity prediction algorithms , 2014 .

[21]  R. Pal,et al.  An Ensemble Based Top Performing Approach for NCI-DREAM Drug Sensitivity Prediction Challenge , 2014, PloS one.

[22]  Laura M. Heiser,et al.  A community effort to assess and improve drug sensitivity prediction algorithms , 2014, Nature Biotechnology.

[23]  N. Cox,et al.  Clinical drug response can be predicted using baseline gene expression levels and in vitro drug sensitivity in cell lines , 2014, Genome Biology.

[24]  R. S. Huang,et al.  Abstract 5561: Clinical drug response can be predicted using baseline gene expression levels and in vitro drug sensitivity in cell lines , 2014 .

[25]  Sridhar Ramaswamy,et al.  Genomics of Drug Sensitivity in Cancer (GDSC): a resource for therapeutic biomarker discovery in cancer cells , 2012, Nucleic Acids Res..

[26]  Kai Zhu,et al.  The k‐ZIG: Flexible Modeling for Zero‐Inflated Counts , 2012, Biometrics.

[27]  Yoshua Bengio,et al.  Practical Recommendations for Gradient-Based Training of Deep Architectures , 2012, Neural Networks: Tricks of the Trade.

[28]  Adam A. Margolin,et al.  The Cancer Cell Line Encyclopedia enables predictive modeling of anticancer drug sensitivity , 2012, Nature.

[29]  Guoyi Zhang,et al.  Bias-corrected random forests in regression , 2012 .

[30]  J. Andrew Royle,et al.  Spatially explicit models for inference about density in unmarked or partially marked populations , 2011, 1112.3250.

[31]  Yoshua Bengio,et al.  Deep Sparse Rectifier Neural Networks , 2011, AISTATS.

[32]  CHUN WEI YAP,et al.  PaDEL‐descriptor: An open source software to calculate molecular descriptors and fingerprints , 2011, J. Comput. Chem..

[33]  Jane-Ling Wang,et al.  Stringing High-Dimensional Data for Functional Analysis , 2011 .

[34]  Gaël Varoquaux,et al.  Scikit-learn: Machine Learning in Python , 2011, J. Mach. Learn. Res..

[35]  Gavin C. Cawley,et al.  On Over-fitting in Model Selection and Subsequent Selection Bias in Performance Evaluation , 2010, J. Mach. Learn. Res..

[36]  D. Stoyan,et al.  Statistical Analysis and Modelling of Spatial Point Patterns , 2008 .

[37]  R. Shoemaker The NCI60 human tumour cell line anticancer drug screen , 2006, Nature Reviews Cancer.

[38]  Jye-Chyi Lu,et al.  Bayesian analysis of zero-inflated regression models , 2006 .

[39]  Nicolas Le Roux,et al.  Out-of-Sample Extensions for LLE, Isomap, MDS, Eigenmaps, and Spectral Clustering , 2003, NIPS.

[40]  Mikhail Belkin,et al.  Laplacian Eigenmaps for Dimensionality Reduction and Data Representation , 2003, Neural Computation.

[41]  M. Monga,et al.  Developmental Therapeutics Program at the NCI: molecular target and drug discovery process , 2002, Leukemia.

[42]  Y. MacNab,et al.  Autoregressive Spatial Smoothing and Temporal Spline Smoothing for Mapping Rates , 2001, Biometrics.

[43]  A. Raftery,et al.  Bayesian Multidimensional Scaling and Choice of Dimension , 2001 .

[44]  S T Roweis,et al.  Nonlinear dimensionality reduction by locally linear embedding. , 2000, Science.

[45]  J. Tenenbaum,et al.  A global geometric framework for nonlinear dimensionality reduction. , 2000, Science.

[46]  Ian H. Witten,et al.  Data mining: practical machine learning tools and techniques, 3rd Edition , 1999 .

[47]  Thomas G. Dietterich Approximate Statistical Tests for Comparing Supervised Classification Learning Algorithms , 1998, Neural Computation.

[48]  S E Vollset,et al.  Confidence intervals for a binomial proportion. , 1994, Statistics in medicine.

[49]  Larry A. Rendell,et al.  The Feature Selection Problem: Traditional Methods and a New Algorithm , 1992, AAAI.

[50]  Ben Shneiderman,et al.  Tree visualization with tree-maps: 2-d space-filling approach , 1992, TOGS.

[51]  Paul Geladi,et al.  Principal Component Analysis , 1987, Comprehensive Chemometrics.

[52]  R. Sokal,et al.  Multiple regression and correlation extensions of the mantel test of matrix correspondence , 1986 .

[53]  อนิรุธ สืบสิงห์ Data Mining Practical Machine Learning Tools and Techniques , 2014 .

[54]  David Hinkley,et al.  Bootstrap Methods: Another Look at the Jackknife , 2008 .

[55]  Nitesh V. Chawla,et al.  SMOTE: Synthetic Minority Over-sampling Technique , 2002, J. Artif. Intell. Res..

[56]  J. van Leeuwen,et al.  Neural Networks: Tricks of the Trade , 2002, Lecture Notes in Computer Science.

[57]  R. Tibshirani,et al.  Estimating the number of clusters in a data set via the gap statistic , 2000 .