posted by user: program1011 || 4678 views || tracked by 4 users: [display]

CMMM 2020 : Special Issue on Machine Learning Applications in Single-Cell RNA Sequencing Data

FacebookTwitterLinkedInGoogle

Link: https://www.hindawi.com/journals/cmmm/si/810784/
 
When N/A
Where N/A
Submission Deadline Apr 23, 2021
Categories    machine learning   deep learning   bioinformatics
 

Call For Papers

The invention of single-cell RNA sequencing (scRNA-seq) has led to the generation of tremendous amounts of data pertaining to populations of cells of specific interest. However, one of the major challenges associated with analysing such data includes designing efficient machine learning approaches that can cope with the noise and sparsity existing in data.

Examples of machine learning applications for scRNA-seq data include: identifying biomarkers of dementia and Alzheimer’s disease; identifying candidate drugs for numerous other neurological disorders; identifying putative cell types from scRNA-seq data of various diseases; noise filtering of low quality cells; pseudo-time reconstruction; and proposals of new clustering methods for scRNA-seq. The success behind machine learning applications depends on the development of new machine learning techniques.

This Special Issue invites not only machine learning researchers, but also researchers interested in potential applications to scRNA-seq data. Both research and review articles pertaining to new machine learning methods and applications to the interpretation of scRNA-seq data are welcomed.

Potential topics include but are not limited to the following:

Supervised learning
Unsupervised learning
Semi-supervised learning
Active learning
Transfer and multitask learning
Ranking
Deep learning
Representation learning
Parallel and distributed learning approaches
Distance learning
Ensemble methods
Dimensionality reduction methods

Lead Editor
* Turki Turki, Department of Computer Science, King Abdulaziz University, Jeddah, Saudi Arabia. Contact Email: tturki@kau.edu.sa

Guest Editors
* Y-h. Taguchi, Department of Physics, Chuo University, Tokyo, Japan. Contact Email: tag@granular.com
* Sanjiban Sekhar Roy, School of Computer Science and Engineering, Vellore Institute of Technology, India, Contact Email: sanjibanroy09@gmail.com

Related Resources

CFDSP 2022   2022 International Conference on Frontiers of Digital Signal Processing (CFDSP 2022)
JCRAI 2021-Ei Compendex & Scopus 2021   2021 International Joint Conference on Robotics and Artificial Intelligence (JCRAI 2021)
MLDM 2022   18th International Conference on Machine Learning and Data Mining
CVPR 2022   Computer Vision and Pattern Recognition
FAIML 2022   2022 International Conference on Frontiers of Artificial Intelligence and Machine Learning (FAIML 2022)
AISTATS 2022   25th International Conference on Artificial Intelligence and Statistics
ICADCML 2022   3rd International Conference on Advances in Distributed Computing and Machine Learning - 2022
ISC HPC 2021   ISC HIGH PERFORMANCE 2021 DIGITAL
blockchain_ml_iot 2021   Network and Electronics (MDPI) Joint Special Issue - Blockchain and Machine Learning for IoT: Security and Privacy Challenges
CAIML 2022   3rd International Conference on Artificial Intelligence and Machine Learning