International Workshop on Deep Learning in Bioinformatics, Biomedicine, and Healthcare Informatics (DLB2H 2017)

in conjunction with

IEEE International Conference on Bioinformatics and Biomedicine (BIBM 2017)

Description

In recent years, deep learning has been spotlighted as the most active research field with its great success in various machine learning communities, such as image analysis, speech recognition, and natural language processing, and now its promising potential is being actively discussed in the field of biomedicine. In particular, a dramatically increasing number of deep learning-based approaches have been proposed in biomedical images and signal processing. However, relatively little application of deep learning has been made in other biomedical areas such as genomics and computational biology due to the difficulty of definition and interpretation of deep learning architecture. Moreover, there are still many challenging tasks with many open problems in deep learning that need to be solved for active use in bioinformatics, biomedicine, and healthcare informatics.

The first International Workshop on Deep Learning in Bioinformatics, Biomedicine, and Healthcare Informatics (DLB2H) will be held in conjunction with IEEE International Conference on Bioinformatics and Biomedicine (BIBM) at Kansas City, MO, USA, on November, 2017. The goal of this workshop is to bring together researchers with expertise of deep learning in bioinformatics, biomedicine, and healthcare informatics and share current cutting-edge deep learning methodologies and its applications. Papers are welcome from the following topics (but not limited to):

• Protein structures
• Gene expression regulations
• Genome-wide association studies
• Protein function prediction
• DNA-protein binding site identification
• Clustering cancer subtypes
• Single-cell clustering
• Cancer diagnosis
• 3D brain reconstruction (MRI/fMRI)
• Tissue image classification/Organ segmentation
• Anomaly detection
• Human activity recognition
• Human behavior monitoring

Important Dates (Tentative)

• Full workshop paper submission due: Sunday, October 1, 2017 October 7, 2017 (D2631*)
• Notification of paper acceptance: Friday, October 13, 2017 (D2625)
• Camera-ready due of accepted papers: Friday, October 25, 2017 (D2613)
• Workshop: November 13~16, 2017 (D2594)

Paper Submission

Please submit a paper (4 page IEEE 2-column format, but can be up to 6 pages without an additionaly fee), via online BIBM paper submission system: https://wi-lab.com/cyberchair/2017/bibm17/index.php. Papers should be formatted to IEEE Proceedings Manuscript Formatting Guidelines. You can download the format instruction here: http://www.ieee.org/conferences_events/conferences/publishing/templates.html. Electronic submissions (in PDF or Postscript format) are required.

Submit a paper: Click here

Publication

All accepted papers will be published in the BIBM proceedings and IEEE Digital Library (Xplore).

Journal Inivitation

Accepted papers

• Jasper Zuallaert, Mijung Kim, Yvan Saeys, and Wesley De Neve, "Interpretable Convolutional Neural Networks for Effective Translation Initiation Site Prediction"
• Waseem Abbas and Qaiser Ijaz, "DeepMI: Deep Learning for Multiclass Motor Imagery Classification"
• Marcia Hon and Naimul Mefraz Khan, "Towards Alzheimer's Disease Classification through Transfer Learning"
• Yao-zhong Zhang, Seiya Imoto, Satoru Miyano, and Rui Yamaguchi, "Reconstruction of high read-depth signals from low-depth whole genome sequencing data using deep learning"
• Tejaswini Mallavarapu, Youngsoon Kim, Jung Hun Oh, and Mingon Kang, "R-PathCluster: Identifying Cancer Subtype of Glioblastoma Multiforme Using Pathway-Based Restricted Boltzmann Machine"
• Ricardo Calix, Ravish Gupta, Matrika Gupta, and Keyuan Jiang, "Deep Gramulator: Improving Precision in the Classification of Personal Health-Experience Tweets with Deep Learning"
• haipeng wan, hong song, lei chen, and jian yang, "Dorsal Hand Vein Recognition Based On Convolutional Neural Networks"
• Vítor Teixeira, Rui Camacho, and Pedro Gabriel Ferreira, "Learning influential genes on cancer gene expression data with stacked denoising autoencoders"
• yasmin kassim and Kannappan Palaniappan, "Extracting Retinal Vascular Networks Using Deep Learning Architecture"
• Asami Yonekura, Hiroharu Kawanaka, V. B. Surya Prasath, Bruce J. Aronow, and Haruhiko Takase, "Improving the Generalization of Disease Stage Classification with Deep CNN for Glioma Histopathological Images"
• Majdi Maabreh, Basheer Qolomany, James Springstead, Izzat Alsmadi, and Ajay Gupta, "Deep vs. Shallow Learning-based Filters of MSMS Spectra in Support of Protein Search Engines"
• Haipeng Chen, Fuhai Xiong, Dihong Wu, Lingxiang Zheng, Ao Peng, Xuemin Hong, Biyu Tang, Hai Lu, Haibin Shi, and Huiru Zheng, "Assessing impacts of data volume and data set balance in using deep learning approach to human activity recognition"
• Ismail Oztel, Gozde Yolcu, Ilker Ersoy, Tommi White, and Filiz Bunyak, "Mitochondria Segmentation in Electron Microscopy Volumes using Deep Convolutional Neural Network"
• Rashika Mishra and Ovidiu Daescu, "Deep learning for skin lesion segmentation"
• Rajgopal Srinivasan and Sanket Gupte, "Deep Learning for Automated Feature Extraction from Sequence Information : A Case Study using Branchpoint Detection"
• Nawanol Theera-Ampornpunt and Somali Chaterji, "Prediction of Enhancer RNA Activity Levels from ChIP-seq-derived Histone Modification Combinatorial Codes"

Program

DLB2H will be held on November 13 (Monday), 9:00am - 5:00pm. The program is here

Program Chairs

• Jung Hun Oh, Department of Medical Physics, Memorial Sloan Kettering Cancer Center, USA
  E-mail: ohj@mskcc.org
• Mingon Kang, Department of Computer Science, Kennesaw State University, USA
  E-mail: mkang9@kennesaw.edu

Program Committee Members

• Romeil Sandhu, Stony Brook University
• Wookjin Choi, Memorial Sloan Kettering Cancer Center
• Michael Young, Harvard University
• Morteza Mardani, Stanford University
• Yixin Chen, Washington University in St. Louis
• Ashis Kumer Biswas, University of Colorado Denver