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MICMED 2021 : PAKDD 2021 Workshop on Machine Intelligence Coinciding with Data Mining Applications in Biology and Medicine

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Link: https://sites.google.com/kau.edu.sa/micmed-pakdd2021/
 
When May 11, 2021 - May 14, 2021
Where Delhi, India
Submission Deadline TBD
Notification Due Feb 22, 2021
Categories    machine learning   deep learning   data mining   biology and medicine
 

Call For Papers

Workshop Overview
Data mining applications in biology and medicine rely on various machine intelligence methodologies for the analysis and interpretation of results. Examples of applications include detecting COVID-19 from chest X-ray images using deep learning, ranking safety of COVID-19 drugs of patients with cancer and chronic diseases using machine learning, identifying important genes in the underlying mechanism of COVID-19 and other neurological diseases such as Alzheimer’s disease using tensor decomposition-based unsupervised feature extraction, finding drugs for infectious disease such as COVID-19 (and Ebola) using unsupervised learning, and identifying cell types of single-cell data using unsupervised learning. The success of such applications depends on the performance, which is attributed to the used machine intelligence methodology

List of Topics
This workshop aims to gather not only data mining researchers, but also researchers interested in machine intelligence methodologies towards emerging applications in biology and medicine. Original papers related (but not limited) to the following are welcome:

Methods and Algorithms
· Unsupervised Tensor Techniques
· Supervised Tensor Techniques
· Semi-supervised Tensor Techniques
· Supervised Representation Learning
· Unsupervised Representation Learning
· Semi-supervised Representation Learning
· Deep Learning Architectures
· Generative Adversarial Networks
· Supervised Learning
· Unsupervised Learning
· Semi-Supervised Learning
· Active Learning
· Multitask and Transfer Learning
· Reinforcement Learning
· Metric Learning
· Kernel Learning
· Online Learning
· Large-scale Machine Learning
· Large-scale Deep Learning
· Large-scale Data Mining
· Boosting
· Ensemble Learning
· Federated Learning
· Adversarial Machine Learning
· Zero-shot Learning
· Dimensionality Reduction
· Optimization
· Computer Vision
· Statistical and Probabilistic Methods
· Novel Learning and Mining Methods

Applications
· Medical Imaging
· Bioinformatics
· Biomedicine
· Biotechnology
· Nanobiotechnology
· Cell Biology
· Chemical Biology
· Molecular and Structural Biology
· Developmental Biology
· Genomics
· Oncology
· Immunology
· Drug Discovery
· Electronic Health Records
· Healthcare
· Autoimmune Diseases
· Infectious Diseases
· Neurological Diseases
· Rare Diseases
· Other Novel Applications

Important Dates
* Workshop call for papers Nov 2, 2020
* Workshop author notification Feb 22, 2021
* Workshop camera-ready due Mar 8, 2021
* All deadlines are 23:59 Pacific Standard Time (PST)

Submission Guidelines and Instructions
* The submitted papers must not be previously published anywhere and must not be under consideration by any other conference or journal during the PAKDD review process
* All papers will be double-blind reviewed by the Program Committee based on technical quality, relevance to data mining, originality, significance, and clarity
* All papers must be submitted electronically in PDF format only, using EasyChair submission system
* Authors should use LNCS Springer manuscript submission guidelines http://www.springer.de/comp/lncs/authors.html
* Before submitting your paper, please carefully read and agree with the PAKDD Paper Submission Policy and No-Show Policy: https://pakdd.org/policies/.

Submission Site
https://easychair.org/conferences/?conf=micmed2021

Paper Publication
Outstanding accepted papers will be published in a LNCS/LNAI post Proceedings of PAKDD Workshops published by Springer

Special Issues:
Authors of selected papers from MICMED 2021 will be invited to submit an extended and improved version to:
Computational and Mathematical Methods in Medicine (CMMM) (JCR 2019: 1.770)
Special issue on Machine Learning Applications in Single-Cell RNA Sequencing Data
Article processing charges (APC) is paid by authors or their institutions

Workshop Organizers
* Turki Turki, Department of Computer Science, King Abdulaziz University, Jeddah, Saudi Arabia. Contact Email: tturki@kau.edu.sa
* Y-h. Taguchi, Department of Physics, Chuo University, Tokyo, Japan. Contact Email: tag@granular.com

Program Committee
* Yingying Wei, Department of Statistics, The Chinese University of Hong Kong, http://www.sta.cuhk.edu.hk/ywei/
* Tao Huang, (Shanghai Institute of Nutrition and Health, Shanghai Institutes for Biological Sciences), Chinese Academy of Sciences, https://scholar.google.com.hk/citations?hl=en&user=N6meTgoAAAAJ
* Shifei Ding, School of Computer Science and Technology, China University of Mining and Technology, https://www.researchgate.net/profile/Shifei_Ding
* Guoqiang Zhong, Department of Computer Science and Technology, Ocean University of China, https://orcid.org/0000-0002-2952-6642
* Zenghui Wang, Department of Electrical and Mining Engineering, University of South Africa, South Africa, https://orcid.org/0000-0003-3025-336X
* Jie Zhang, Senior Machine Learning Research Scientist, Adobe Inc., USA
* Xin Gao, Applied Scientist, Amazon, USA, http://stella-gao.github.io/
* Yasser El-Manzalawy, Assistant Professor, Department of Translational Data Science and Informatics, Geisinger Clinic, USA, https://i2rlab.com/
* Sanjiban Sekhar Roy, School of Computer Science and Engineering, Vellore Institute of Technology, India, https://www.researchgate.net/profile/Sanjiban_Roy
* Arif Ahmed Sekh, Department of Physics and Technology, University of Tromsø, Norway, https://orcid.org/0000-0003-0706-2565
* Y-h. Taguchi, Department of Physics, Chuo University, Tokyo, Japan, https://orcid.org/0000-0003-0867-8986
* Turki Turki, Department of Computer Science, King Abdulaziz University, Jeddah, Saudi Arabia, https://orcid.org/0000-0002-9491-2435











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