Author Interviews, Genetic Research, Nature, Technology / 15.07.2020
Janggu Technology Enhances Deep Learning For Genomics
MedicalResearch.com Interview with:
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Dr. Akalin[/caption]
Dr.Altuna Akalin PhD
Head of Bioinformatics and Omics Data Science Platform
Berlin Institute for Medical Systems Biology (BIMSB)
Max Delbrück Center for Molecular Medicine (MDC)
Berlin, Germany
MedicalResearch.com: What is the background for this study? Where does the word Janggu come from?
Response: Deep learning applications on genomic datasets used to be a cumbersome process where researchers spend a lot of time on preparing and formatting data before they even can run deep learning models. In addition, the evaluation of deep learning models and the choice of deep learning framework were also not straightforward. To streamline these processes, we developed Janggu. With this framework, we are aiming to relieve some of that technical burden and make deep learning accessible to as many people as possible.
Janggu is named after a traditional Korean drum shaped like an hourglass turned on its side. The two large sections of the hourglass represent the areas Janggu is focused: pre-processing of genomics data, results visualization and model evaluation. The narrow connector in the middle represents a placeholder for any type of deep learning model researchers wish to use.
Dr. Akalin[/caption]
Dr.Altuna Akalin PhD
Head of Bioinformatics and Omics Data Science Platform
Berlin Institute for Medical Systems Biology (BIMSB)
Max Delbrück Center for Molecular Medicine (MDC)
Berlin, Germany
MedicalResearch.com: What is the background for this study? Where does the word Janggu come from?
Response: Deep learning applications on genomic datasets used to be a cumbersome process where researchers spend a lot of time on preparing and formatting data before they even can run deep learning models. In addition, the evaluation of deep learning models and the choice of deep learning framework were also not straightforward. To streamline these processes, we developed Janggu. With this framework, we are aiming to relieve some of that technical burden and make deep learning accessible to as many people as possible.
Janggu is named after a traditional Korean drum shaped like an hourglass turned on its side. The two large sections of the hourglass represent the areas Janggu is focused: pre-processing of genomics data, results visualization and model evaluation. The narrow connector in the middle represents a placeholder for any type of deep learning model researchers wish to use.
Prof. FAN Zhiyong PhD
University of California, Irvine
HKUST School of Engineering
MedicalResearch.com: What is the background for this study? What are the main findings?
Response: According to the report of The World Health Organization, there are over 252 million people suffering from visual impairment globally and 15 million of them are difficult to cure by conventional medical methods. However, today, even the best bionic eyes have only 200 clinical trials, less than 1 ppm of all the patients, mainly due to their poor performance and high cost. The huge gap in supply and demand triggers the study of bionic eyes with performance comparable to human eyes. One important reason for their poor performance is the mismatch in shape between the flat bionic eyes and concave sclera. To protect the soft tissue in eyes from being damaged by the bionic surface, the implanted bionic eyes have to be small. This has limited the sensing area and further the electrodes number, and finally yielded poor image sensing characters with low resolution and narrow field-of-view.
In this work, we are trying to achieve high performance image sensing by biomimeticing human eyes. The high-density NWs are well aligned and embedded in a hemispherical template to serve as retina. The conformal attachment of bionic eyes with sclera enables the large sensing area and wide visual angle. In addition, each individual high-density nanowires can potentially work as an individual pixel. By addressing these challenges, our device design has huge potential to improve the image sensing performance of bionic eyes.
Dr. Tandon[/caption]
Pooja S. Tandon, MD, MPH
Center for Child Health, Behavior and Development
Seattle Children's Research Institute
MedicalResearch.com: What is the background for this study?
Response: Cell phone use is common among middle and high school students, yet we do not have an understanding of school cell phone policies and practices in the U.S. We conducted a survey of public schools serving grades 6-12. The survey sent to over 1,100 school principals, representing a national sample of schools across the U.S., asked questions about the presence of a cell phone policy for students and staff and restrictions on phone use. Additional questions addressed consequences of policy violation, the use of cell phones for curricular activities and principals’ attitudes toward cell phone policies.
Dr. Helen Marsden PhD
Skin Analytics Limited
London, United Kingdom
MedicalResearch.com: What is the background for this study?
Response: In this technology age, with the explosion of interest and applications using Artificial Intelligence, it is easy to accept the output of a technology-based test - such as a smartphone app designed to identify skin cancer - without thinking too much about it. In reality, technology is only as good as the way it has been developed, tested and validated. In particular, AI algorithms are prone to a lack of “generalisation” - i.e. their performance drops when presented with data it has not seen before. In the medical field, and particularly in areas where AI is being developed to direct a patient’s diagnosis or care, this is particularly problematic. Inappropriate diagnosis or advice to patients can lead to false reassurance, heightened concern and pressure on NHS services, or worse. It is concerning, therefore, that there are a large number of smartphone apps available that provide an assessment of skin lesions, including some that provide an estimate of the probability of malignancy, that have not been assessed for diagnostic accuracy.
Skin Analytics has developed an AI-based algorithm, named: Deep Ensemble for Recognition of Malignancy (DERM), for use as a decision support tool for healthcare providers. DERM determines the likelihood of skin cancer from dermoscopic images of skin lesions. It was developed using deep learning techniques that identify and assess features of these lesions which are associated with melanoma, using over 7,000 archived dermoscopic images. Using these images, it was shown to identify melanoma with similar accuracy to specialist physicians. However, to prove the algorithm could be used in a real life clinical setting, Skin Analytics set out to conduct a clinical validation study.
Dr. Munzer[/caption]
Tiffany G. Munzer, MD
Department of Pediatrics
University of Michigan Medical School
Ann Arbor
MedicalResearch.com: What is the background for this study?
Response: There’s been such a rise in the prevalence of tablet devices and the recommendation for families of young children has been to engage in media together because children learn the most from screens when they’re shared with an adult. However, little is known about how toddlers and adults might behave and interact using a tablet.
