Bioinformatics, October 2009. Machine-learning guided directed evolution BMC Biotechnology, March 2007. [10.1093/bioinformatics/btp445], Prediction of protein stability changes for single‐site mutations using support vector machines. Joseph Gomes, Bharath Ramsundar, Evan N. Feinberg, Vijay S. Pande. Joseph Mellor, Ioana Grigoras, Pablo Carbonell, and Jean-Loup Faulon. For example, a few years ago we ran an experiment with recommending help articles in … [10.1101/2020.01.16.908509], Biological Sequence Design using Batched Bayesian Optimization. Nature Communications, April 2020. Patrick Ng [10.1101/738690][bioRxiv], DeepPSC (protein structure camera): computer vision-based reconstruction of proteins backbone structure from alpha carbon trace as a case study. Preprint, June 2020. Preprint, December 2019. [10.1101/674119] [bioRxiv], A Brief History of Protein Sorting Prediction. Preprint, May 2020. Nature, January 2020. The machine learning tutorials are supported by the Machine Learning Initiative at Imperial College London and the current organizers are Dr. Viktoriia Sharmanska and Dr. K S Sesh Kumar.. Preprint, November 2019. [arxiv], Sequence representations and their utility for predicting protein-protein interactions. (A) Classification workflow, starting with mRNA and amino acid sequences. Richard J. G. Khazen, A. Gyulkhandanian, T. Issa, R.C. PLOS Computational Biology, June 2019. [10.1021/acs.jcim.7b00488], Variational auto-encoding of protein sequences. Juannan Zhou, David M. McCandlish. Using total internal reflection (TIRF) microscopy, we have accumulated more than 10 million trajectories over dozens of experimental preparations with differences in both the imaging approaches as well as the biological context. Christof Angermueller, David Belanger, Andreea Gane, Zelda Mariet, David Dohan, Kevin Murphy, Lucy Colwell, D Sculley. [arxiv], Interpreting mutational effects predictions, one substitution at a time. [10.7554/eLife.46935.001], Generative Models for Graph-Based Protein Design. Pacific Symposium on Biocomputing, January 2002. Eli N. Weinstein, Debora S. Marks. Marshall, John McIntosh, Edward C. Sherer, Vladimir Svetnik, Jennifer Johnston. Cell Systems, June 2018. Machine-Learning-Portfolio This is a repository of the projects I worked on or currently working on. Alperen Dalkiran, Ahmet Sureyya Rifaioglu, Maria Jesus Martin, Rengul Cetin-Atalay, Volkan Atalay, Tunca Dogan. [10.1186/s12859-018-2407-8], Learned protein embeddings for machine learning. [OpenReview], How to Hallucinate Functional Proteins. Bioinformatics, May 2019. Data recorded from sensors in mobile phones, financial data like accounting figures and climate indicators are all examples of time series society and individuals are exposed to daily. [10.1101/757252], Deep generative models for T cell receptor protein sequences. 10.1093/bioinformatics/bty178. Ehsaneddin Asgari, Nina Poerner, Alice C. McHardy, Mohammad R.K. Mofrad. Journal of Chemical Information and Modeling, April 2020. [10.1073/pnas.1816640116], Simple tricks of convolutional neural network architectures improve DNA–protein binding prediction. Classification and annotation Roshan Rao, Nicholas Bhattacharya, Neil Thomas, Yan Duan, Xi Chen, John Canny, Pieter Abbeel, Yun S. Song. I also have the Jupyter Notebook version of some of my Kaggle kernels here. NEWS. Deep learning does this by successive data transformations via layers, the depth referring to the number of layers of transformations, not the extent of insight gained. Preprint, February 2020. Bioinformatics, March 2018. If nothing happens, download GitHub Desktop and try again. Joseph M. Cunningham, Grigoriy Koytiger, Peter K. Sorger & Mohammed AlQuraishi. It is updated regularly. Joe G. Greener, Shaun M. Kandathil, David T. Jones. Michael Heinzinger, Ahmed Elnaggar, Yu Wang, Christian Dallago, Dmitrii Nechaev, Florian Matthes, Burkhard Rost. Cen Wan, David T. Jones. Preprint, January 2020. [10.1101/705426], A Self-Consistent Sonification Method to Translate Amino Acid Sequences into Musical Compositions and Application in Protein Design Using Artificial Intelligence. We try to classify papers based on a combination of their applications and model type. download the GitHub extension for Visual Studio, Centro de Investigación Genética y Genómica, Facultad de Ciencias de la Salud Eugenio Espejo, Universidad UTE, Mariscal Sucre Avenue, Quito 170129, Ecuador, RNASA-IMEDIR, Computer Science Faculty, University of Coruna, Coruna 15071, Spain, Red Latinoamericana de Implementación y Validación de Guías Clínicas Farmacogenómicas (RELIVAF-CYTED), Grupo de Bio-Quimioinformática, Universidad de Las Américas, Avenue de los Granados, Quito 170125, Ecuador, Carrera de Enfermería, Facultad de Ciencias de la Salud, Universidad de Las Américas, Avenue de los Granados, Quito 170125, Ecuador, Tecnológico de Estudios Superiores de Jocotitlán, Carretera Toluca-Atlacomulco KM 44.8 Ejido de San Juan y San Agustin, 50700 Jocotitlán, México, Centro de Investigación en Tecnologías de la Información y las Comunicaciones (CITIC), Campus de Elviña s/n 15071 A Coruña, Spain, Biomedical Research Institute of A Coruña (INIBIC), University Hospital Complex of A Coruña (CHUAC), 15006, A Coruña, Spain, Department of Organic Chemistry II, University of the Basque Country UPV/EHU, Leioa 48940, Biscay, Spain, IKERBASQUE, Basque Foundation for Science, Bilbao 48011, Biscay, Spain, Escuela de Ciencias Físicas y Matemáticas, Universidad de Las Américas, Avenue de los Granados, Quito 170125, Ecuador, Facultad de Ingeniería y Ciencias Agropecuarias, Universidad de Las Américas, Avenue de los Granados, Quito 170125, Ecuador. [arxiv], Convolutional neural network architectures for predicting DNA–protein binding. Javier Caceres-Delpiano, Roberto Ibañez, Patricio Alegre, Cynthia Sanhueza, Romualdo Paz-Fiblas, Simon Correa, Pedro Retamal, Juan Cristóbal Jiménez, Leonardo Álvarez. Adam Riesselman, Jung-Eun Shin, Kollasch, Conor McMahon, Elana Simon, Chris Sander, Aashish Manglik, Andrew Kruse, Debora Marks. Other supervised learning, Machine learning and AI-based approaches for bioactive ligand discovery and GPCR-ligand recognition. Preprint, January 2017 [10.1073/pnas.1901979116], Conditioning by adaptive sampling for robust design. [10.1002/prot.25416], ProLanGO: Protein Function Prediction Using Neural Machine Translation Based on a Recurrent Neural Network. Machine learning is the study of algorithms that, given a collection of observations, infer or “learn” a model that explains or characterizes the observations. Adam J. Riesselman, John B. Ingraham, Debora S. Marks Preprint, November 2018. Vladimir Gligorijevic, P. Douglas Renfrew, Tomasz Kosciolek, Julia Koehler Leman, Kunghyun Cho, Tommi Vatanen, Daniel Berenberg, Bryn Taylor, Ian M. Fisk, Ramnik J. Xavier, Rob Knight, Richard Bonneau. Ariel S Schwartz, Gregory J Hannum, Zach R Dwiel, Michael E Smoot, Ana R Grant, Jason M Knight, Scott A Becker, Jonathan R Eads, Matthew C LaFave, Harini Eavani, Yinyin Liu, Arjun K Bansal, Toby H Richardson [10.1093/bioinformatics/btx548], Semisupervised Gaussian Process for Automated Enzyme Search. [10.1093/bioinformatics/btx350], Atomic Convolutional Networks for Predicting Protein-Ligand Binding Affinity. [10.1101/671552] [bioRxiv], SPIN2: Predicting sequence profiles from protein structures using deep neural networks. [10.1021/acsomega.9b04105], Predicting changes in protein thermostability upon point mutation with deep 3D convolutional neural networks. [10.1038/s41592-019-0687-1], Functions of olfactory receptors are decoded from their sequence. James O'Connell, Zhixiu Li, Jack Hansonm, Rhys Heffernan, James Lyons, Kuldip Paliwal, Abdollah Dehzangi, Yuedong Yang, Yaoqi Zhou. [10.1016/j.ymeth.2017.06.034], Large‐scale automated function prediction of protein sequences and an experimental case study validation on PTEN transcript variants. Description. Preprint, June 2020. [10.1186/s12859-018-2368-y], DeepGO: predicting protein functions from sequence and interactions using a deep ontology-aware classifier. Bioinformatics, March 2004. A.D.J. Journal of Computer Aided Molecular Design, December 2017. Haicang Zhang, Yufeng Shen. Unsupervised variant prediction Proteins, November 2017. [10.1371/journal.pone.0138022], NeEMO: a method using residue interaction networks to improve prediction of protein stability upon mutation. Web-servers for computing protein structure graphs exist, however the lack of public APIs for programmatic access, limited featurisation schemes and incompatibility Carlo Mazzaferro. Ellen D. Zhong, Tristan Bepler, Joseph H. Davis, Bonnie Berger. Predicting stability Within each category, papers are listed in reverse chronological order (newest first). Lei Jia , Ramya Yarlagadda, Charles C. Reed. Rice, Viviana Gradinaru, Frances H. Arnold. Machine learning in general attempts to find transformations of input data into more useful representations of that data to solve a problem. [10.1371/journal.pone.0141287], A structural alignment kernel for protein structures. This project will be focused on creating a deep learning framework for tracking individual molecules and proteins as they move within a cell under various conditions. Machine-learning guided directed evolution. Create a new repository off the ML Ops with GitHub Actions and Azure Machine Learning template. Preprint, September 2019. [10.1021/acs.jcim.7b00414], Evaluating Protein Engineering Thermostability Prediction Tools Using an Independently Generated Dataset. Badri Adhikari. [10.1038/s41586-019-1923-7], Deep learning extends de novo protein modelling coverage of genomes using iteratively predicted structural constraints. David B. Sauer, Da-Neng Wang. Namrata Anand, Po-Ssu Huang. Brandon Carter, Maxwell L. Bileschi, Jamie Smith, Theo Sanderson, Drew Bryant, David Belanger, Lucy J. Colwell. [10.1101/2020.08.26.266940], Generative probabilistic biological sequence models that account for mutational variability. Adam J Riesselman, John B Ingraham, Debora S. Marks Yuting Xu, Deeptak Verma, Robert P Sheridan, Andy Liaw, Junshui Ma, Nicholas [arXiV]], Repertoires of G protein-coupled receptors for Ciona-specific neuropeptides. Distribution explorer. Preprint, April 2020. [arxiv] [PMLR], Machine learning-assisted directed protein evolution with combinatorial libraries. [10.1101/2020.04.07.029264], Signal Peptides Generated by Attention-Based Neural Networks. Nucleic Acids Research, October 2015. Preprint, June 2020. [ML4PS], Deciphering interaction fingerprints from protein molecular surfaces using geometric deep learning. Jianlin Cheng, Arlo Randall, Pierre Baldi. Tileli Amimeur, Jeremy M. Shaver, Randal R. Ketchem, J. Alex Taylor, Rutilio H. Clark, Josh Smith, Danielle Van Citters, Christine C. Siska, Pauline Smidt, Megan Sprague, Bruce A. Kerwin, Dean Pettit. Jian Zhou, Olga G. Troyanskaya. drugs, catalysts). PLOS Computational Biology, October 2017. [10.1101/2020.06.12.148296], ProtTox: Toxin identification from Protein Sequences. Bioinformatics, February 2018. Graph representations of proteins have been successfully used in machine learning and structural analysis projects in structural biology (Pires et al., 2011; 2013; Cheng et al., 2008). This interface plays an essential role in accurately describing the properties of functional molecules (e.g. [10.1093/bioinformatics/btz328], DeepConv-DTI: Prediction of drug-target interactions via deep learning with convolution on protein sequences. International Conference on Learning Representations, February 2019. [2020.06.02.129270], Energy-based models for atomic-resolution protein conformations.