Deep Learning for Protein(omics)


This blog post has recent publications related to Deep Learning for proteinomics (the study of proteins). Proteins are a set of molecules in the human (and animal) bodies (probably best known for their role related to muscle mass and in DNA replication).

Wikipedia describes proteins as:

    Proteins (/ˈproʊˌtiːnz/ or /ˈproʊti.ᵻnz/) are large biomolecules, or macromolecules, consisting of one or more long chains of amino acid residues. Proteins perform a vast array of functions within organisms, including catalysing metabolic reactions, DNA replication, responding to stimuli, and transporting molecules from one location to another. Proteins differ from one another primarily in their sequence of amino acids, which is dictated by the nucleotide sequence of their genes, and which usually results in protein folding into a specific three-dimensional structure that determines its activity.

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Amund Tveit (WeChat ID: AmundTveit)

Year  Title Author
2017   Deep Recurrent Neural Network for Protein Function Prediction from Sequence  XL Liu
2017   Sequence-based prediction of protein protein interaction using a deep-learning algorithm  T Sun, B Zhou, L Lai, J Pei
2017   Protein Model Quality Assessment: A Machine Learning Approach  K Uziela
2017   Deep convolutional neural networks for detecting secondary structures in protein density maps from cryo-electron microscopy  R Li, D Si, T Zeng, S Ji, J He
2017   Towards recognition of protein function based on its structure using deep convolutional networks  A Tavanaei, AS Maida, A Kaniymattam
2017   Improved protein model quality prediction by changing the target function  K Uziela, D Menendez Hurtado, N Shu, B Wallner
2017   A Novel Model Based On Fcm-Lm Algorithm For Prediction Of Protein Folding Rate  L Liu, M Ma, J Cui
2017   EPSILON-CP: using deep learning to combine information from multiple sources for protein contact prediction  K Stahl, M Schneider, O Brock
2017   Prediction of protein function using a deep convolutional neural network ensemble  EI Zacharaki
2017   Protein Function Prediction using Deep Restricted Boltzmann Machines  X Zou, G Wang, G Yu
2017   Next-Step Conditioned Deep Convolutional Neural Networks Improve Protein Secondary Structure Prediction  A Busia, N Jaitly
2017   Predicting membrane protein contacts from non-membrane proteins by deep transfer learning  Z Li, S Wang, Y Yu, J Xu
2017   A Template-Based Protein Structure Reconstruction Method Using Deep Autoencoder Learning  H Li, Q Lyu, J Cheng
2017   DNpro: A Deep Learning Network Approach to Predicting Protein Stability Changes Induced by Single-Site Mutations  X Zhou, J Cheng
2017   Computational Methods for the Prediction of Drug-Target Interactions from Drug Fingerprints and Protein Sequences by Stacked Auto-Encoder Deep Neural Network  L Wang, ZH You, X Chen, SX Xia, F Liu, X Yan, Y Zhou
2017   Multi-task Deep Neural Networks in Automated Protein Function Prediction  AS Rifaioglu, T Doğan, MJ Martin, R Cetin
2016   AUC-Maximized Deep Convolutional Neural Fields for Protein Sequence Labeling  S Wang, S Sun, J Xu
2016   Evaluation of Protein Structural Models Using Random Forests  R Cao, T Jo, J Cheng
2016   A Protein Domain and Family Based Approach to Rare Variant Association Analysis  TG Richardson, HA Shihab, MA Rivas, MI McCarthy
2016   Protein Sequencing And Neural Network Classification Methods  V Indarni, SK Terala, PV Bhushan, MR Ireddy
2016   Accurate prediction of docked protein structure similarity using neural networks and restricted Boltzmann machines  R Farhoodi, B Akbal
2016   Identification of thermostabilizing mutations for a membrane protein whose three‐dimensional structure is unknown  Y Kajiwara, S Yasuda, Y Takamuku, T Murata
2016   Identification of Genetic Sequences Recognized by Human SC35 Protein Using Artificial Neural Networks: A Deep Learning Approach  AJ Luke, S Fergione
2016   MUST-CNN: A MUltilayer Shift-and-sTitch Deep Convolutional Architecture for Sequence-based Protein Structure Prediction  Z Lin, Y Qi
2016   Protein Secondary Structure Prediction Using Deep Multi-scale Convolutional Neural Networks and Next-Step Conditioning  A Busia, J Collins, N Jaitly
2016   A computational framework for disease grading using protein signatures  E Zerhouni, B Prisacari, Q Zhong, P Wild, M Gabrani
2016   ProtPOS: a python package for the prediction of protein preferred orientation on a surface  JCF Ngai, PI Mak, SWI Siu
2016   DeepQA: Improving the estimation of single protein model quality with deep belief networks  R Cao, D Bhattacharya, J Hou, J Cheng
2016   Protein contact prediction from amino acid co-evolution using convolutional networks for graph-valued images  V Golkov, MJ Skwark, A Golkov, A Dosovitskiy, T Brox
2016   Protein Secondary Structure Prediction by using Deep Learning Method  Y Wang, H Mao, Z Yi
2016   On the importance of composite protein multiple ligand interactions in protein pockets  S Tonddast‐Navaei, B Srinivasan, J Skolnick
2016   Protein function in precision medicine: deep understanding with machine learning  B Rost, P Radivojac, Y Bromberg
2016   Protein Residue-Residue Contact Prediction Using Stacked Denoising Autoencoders  IV Luttrell, J Bailey
2016   Protein Residue Contacts and Prediction Methods  B Adhikari, J Cheng
2016   RaptorX-Property: a web server for protein structure property prediction.  S Wang, W Li, S Liu, J Xu
2016   AUCpreD: proteome-level protein disorder prediction by AUC-maximized deep convolutional neural fields  S Wang, J Ma, J Xu
2016   Benchmarking Deep Networks for Predicting Residue-Specific Quality of Individual Protein Models in CASP11  T Liu, Y Wang, J Eickholt, Z Wang
2015   Theory, Methods, and Applications of Coevolution in Protein Contact Prediction  J Ma, S Wang
2015   A topological approach for protein classification  Z Cang, L Mu, K Wu, K Opron, K Xia, GW Wei
2015   Application of Learning to Rank to protein remote homology detection  B Liu, J Chen, X Wang
2015   Improving Protein Fold Recognition by Deep Learning Networks  T Jo, J Hou, J Eickholt, J Cheng
2015   Proteins, physics and probability kinematics: a Bayesian formulation of the protein folding problem  T Hamelryck, W Boomsma, J Ferkinghoff
2015   DeepCNF-D: Predicting Protein Order/Disorder Regions by Weighted Deep Convolutional Neural Fields  S Wang, S Weng, J Ma, Q Tang
2015   A deep learning framework for modeling structural features of RNA-binding protein targets  S Zhang, J Zhou, H Hu, H Gong, L Chen, C Cheng
2015   A serum protein test for improved prognostic stratification of patients with myelodysplastic syndrome (MDS)  J Roder, J Löffler
2015   An Overview of Practical Applications of Protein Disorder Prediction and Drive for Faster, More Accurate Predictions  X Deng, J Gumm, S Karki, J Eickholt, J Cheng
2015   A panel of mass spectrometry based serum protein tests for predicting graft-versus-host disease (GvHD) and its severity  H Roder, AC Hoffmann, J Roder, M Koldehoff
2015   Learning Deep Architectures for Protein Structure Prediction  K Baek
2015   Protein secondary structure prediction using deep convolutional neural fields  S Wang, J Peng, J Ma, J Xu
2015   Protein sequence labelling by AUC-maximized Deep Convolutional Neural Fields  S Wang, J Ma, S Sun, J Xu
2015   Fast loop modeling for protein structures  J Zhang, S Nguyen, Y Shang, D Xu, I Kosztin
2015   Introducing Students to Protein Analysis Techniques: Separation and Comparative Analysis of Gluten Proteins in Various Wheat Strains  AL Pirinelli, JC Trinidad, NLB Pohl
2014   Predicting backbone Cα angles and dihedrals from protein sequences by stacked sparse auto‐encoder deep neural network  J Lyons, A Dehzangi, R Heffernan, A Sharma
2014   Improved contact predictions using the recognition of protein like contact patterns.  MJ Skwark, D Raimondi, M Michel, A Elofsson
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