A Case-Control based Genomic Analysis of Chronic Obstructive Pulmonary Disease

Chronic Obstructive Pulmonary Disease (COPD) is a respiratory illness that affects millions of people all over the world. It is a major cause of chronic morbidity and mortality and a serious global public health problem. COPD is the fourth leading cause of death worldwide. Although the environmental causes of COPD which predominantly include cigarette smoking are well-documented, to this date the genetic underpinnings of COPD remain largely unknown. Furthermore in the current landscape of a respiratory pandemic, COPD patients are at a much higher risk for developing other respiratory illnesses and co-morbidities. In this study we use genomic data from…

Proof tree contrast mining for automatic hypothesis generation

The probability of developing almost any given disease is affected by multiple risk factors. These risk factors often do not behave independently, instead interacting in specific ways which affect the probability of developing the disease. To better understand the root causes of many diseases, it is necessary to study these interactions as they may provide clues about the underlying mechanisms responsible for the development of the disease. We have developed a method for studying these interactions by applying contrast mining to extract patterns of nested logical interactions associated with specific medical outcomes. We demonstrate the effectiveness of this method in…

Bioinformatic Prediction of the Potential SARS-CoV-2 Receptors in Human

Unraveling receptors used by SARS-CoV-2 for entry and the exact positively selected sites on the Spike (S) protein associated with this process can provide insights into the viral transmission and reveal therapeutic targets. Except for ACE2, accumulating evidence indicates that the S protein potentially recognizes other receptors like CD147. Therefore, methods for new receptors identification are urgently needed. To account for this, the following three aspects will be explored in this proposal. First, with increasing genome sequence data to investigate evolution and selection patterns and to assess their influence on the structure and function of the S protein. Second, integrating…

Neural information extraction in biomedical domain: issues and challenges

Much medical data today remain inaccessible thus limiting their impact on patient care. Images and illustrations, scientific articles, and free-text reports do not allow for easy extraction and re-use of the knowledge they contain. They lack the structure and metadata necessary for automated processing and annotation. The resources required to collect and annotate manually are not sufficient to produce enough comprehensive benchmark datasets to bootstrap specialty research. We discuss neural network-based approaches to the problem of extraction of medical information from clinical images and unstructured text sources.