DeepHBSP: A Deep Learning Framework for Predicting Human Blood-Secretory Proteins Using Transfer Learning

[1]  Ying. Li,et al.  CapsNet-SSP: multilane capsule network for predicting human saliva-secretory proteins , 2020, BMC Bioinformatics.

[2]  Huansheng Cao,et al.  DEEPSMP: A deep learning model for predicting the ectodomain shedding events of membrane proteins , 2020, J. Bioinform. Comput. Biol..

[3]  Yan Wang,et al.  Human body-fluid proteome: quantitative profiling and computational prediction , 2020, Briefings Bioinform..

[4]  E. Nice,et al.  Potential early clinical stage colorectal cancer diagnosis using a proteomics blood test panel , 2019, Clinical Proteomics.

[5]  Zhiqiang Ma,et al.  In silico Prediction of Human Secretory Proteins in Plasma Based on Discrete Firefly Optimization and Application to Cancer Biomarkers Identification , 2019, Front. Genet..

[6]  Alireza Hadj Khodabakhshi,et al.  Metascape provides a biologist-oriented resource for the analysis of systems-level datasets , 2019, Nature Communications.

[7]  Jonathan L. Robinson,et al.  A Systematic Investigation of the Malignant Functions and Diagnostic Potential of the Cancer Secretome , 2019, Cell reports.

[8]  Yanchun Liang,et al.  Capsule network for protein post-translational modification site prediction , 2018, Bioinform..

[9]  Xia Yang,et al.  Co-regulatory networks of human serum proteins link genetics to disease , 2018, Science.

[10]  Yanling Li,et al.  High-Throughput Identification of Mammalian Secreted Proteins Using Species-Specific Scheme and Application to Human Proteome , 2018, Molecules.

[11]  Ann Siew Gek Lee,et al.  The future of blood-based biomarkers for the early detection of breast cancer. , 2018, European journal of cancer.

[12]  Vishal M. Patel,et al.  Learning Deep Features for One-Class Classification , 2018, IEEE Transactions on Image Processing.

[13]  Piero Fariselli,et al.  DeepSig: deep learning improves signal peptide detection in proteins , 2017, Bioinform..

[14]  Yanchun Liang,et al.  MusiteDeep: a deep‐learning framework for general and kinase‐specific phosphorylation site prediction , 2017, Bioinform..

[15]  Hong Liang,et al.  Text feature extraction based on deep learning: a review , 2017, EURASIP J. Wirel. Commun. Netw..

[16]  Ole Winther,et al.  An introduction to deep learning on biological sequence data: examples and solutions , 2017, Bioinform..

[17]  Ole Winther,et al.  DeepLoc: prediction of protein subcellular localization using deep learning , 2017, Bioinform..

[18]  Geoffrey E. Hinton,et al.  Dynamic Routing Between Capsules , 2017, NIPS.

[19]  Matthias Mann,et al.  Revisiting biomarker discovery by plasma proteomics , 2017, Molecular systems biology.

[20]  Yuanzhi Li,et al.  Convergence Analysis of Two-layer Neural Networks with ReLU Activation , 2017, NIPS.

[21]  Matthias Mann,et al.  Plasma Proteome Profiling to Assess Human Health and Disease. , 2016, Cell systems.

[22]  Shreya Singh,et al.  Tumor markers: A diagnostic tool , 2016, National journal of maxillofacial surgery.

[23]  Xiaohui S. Xie,et al.  DanQ: a hybrid convolutional and recurrent deep neural network for quantifying the function of DNA sequences , 2015, bioRxiv.

[24]  Stefan Winkler,et al.  Deep Learning for Emotion Recognition on Small Datasets using Transfer Learning , 2015, ICMI.

[25]  Gary Walker,et al.  MetazSecKB: the human and animal secretome and subcellular proteome knowledgebase , 2015, Database J. Biol. Databases Curation.

[26]  Jimmy Ba,et al.  Adam: A Method for Stochastic Optimization , 2014, ICLR.

[27]  Peter B. McGarvey,et al.  UniRef clusters: a comprehensive and scalable alternative for improving sequence similarity searches , 2014, Bioinform..

[28]  Pierre Baldi,et al.  SSpro/ACCpro 5: almost perfect prediction of protein secondary structure and relative solvent accessibility using profiles, machine learning and structural similarity , 2014, Bioinform..

[29]  Cheolju Lee,et al.  Integrated Glycoproteomics Demonstrates Fucosylated Serum Paraoxonase 1 Alterations in Small Cell Lung Cancer , 2013, Molecular & Cellular Proteomics.

[30]  Kyle L. Morris,et al.  Exploring the sequence determinants of amyloid structure using position-specific scoring matrices , 2010, Nature Methods.

[31]  Ying Xu,et al.  In-silico prediction of blood-secretory human proteins using a ranking algorithm , 2010, BMC Bioinformatics.

[32]  Ying Gao,et al.  Bioinformatics Applications Note Sequence Analysis Cd-hit Suite: a Web Server for Clustering and Comparing Biological Sequences , 2022 .

[33]  Rong Zeng,et al.  Sys-BodyFluid: a systematical database for human body fluid proteome research , 2008, Nucleic Acids Res..

[34]  Ying Xu,et al.  Computational prediction of human proteins that can be secreted into the bloodstream , 2008, Bioinform..

[35]  Gilbert S Omenn,et al.  THE HUPO Human Plasma Proteome Project , 2007, Proteomics. Clinical applications.

[36]  Y. Yao,et al.  On Early Stopping in Gradient Descent Learning , 2007 .

[37]  Ariel Linden.  Measuring diagnostic and predictive accuracy in disease management: an introduction to receiver operating characteristic (ROC) analysis. , 2006, Journal of evaluation in clinical practice.

[38]  S. Dhanasekaran,et al.  Delineation of prognostic biomarkers in prostate cancer , 2001, Nature.

[39]  Thomas L. Madden,et al.  Gapped BLAST and PSI-BLAST: a new generation of protein database search programs. , 1997, Nucleic acids research.

[40]  B. Matthews.  Comparison of the predicted and observed secondary structure of T4 phage lysozyme. , 1975, Biochimica et biophysica acta.

[41]  Ying Li,et al.  DeepUEP: Prediction of Urine Excretory Proteins Using Deep Learning , 2020, IEEE Access.

[42]  Nitish Srivastava,et al.  Dropout: a simple way to prevent neural networks from overfitting , 2014, J. Mach. Learn. Res..

[43]  Cathy H. Wu,et al.  UniProt: the Universal Protein knowledgebase , 2004, Nucleic Acids Res..

[44]  Rich Caruana,et al.  Learning Many Related Tasks at the Same Time with Backpropagation , 1994, NIPS.