Microbial communities exist throughout the biosphere including the associations they form with humans, plants and animals. Understanding the diversity and genetic complexity of these communities, along with the interactions they undertake both within communities and with their environment has given rise to the concept of the study of the microbiome. With improvements in molecular biology, computational power, and high throughput technologies such as the advent of next generation sequencing, new opportunities exist to study the microbiome from multiple environments including their role in human health and disease. However, in order to maximize the information we can realize from these data types, careful considerations need to be made in terms of study design, and new tools and approaches are needed for data generation, analysis and interpretation. This talk will provide an overview of these topics using examples from microbiome studies.
In this talk, we will discuss some of the recent research on improving the performance of personalized recommender systems by using knowledge graphs (KG) to uncover the long range preferences of users.
Cancer is mainly caused by heterogeneous somatic genome alterations (SGAs). Genome-scale data from individual patients are now readily available, and it is anticipated that precisely targeting specific genomic alterations of individual tumors will bring in more effective therapies. However, there are 3 major gaps that hinder the translation of the genome data of a tumor to personalized therapy.
Adverse drug reactions (ADRs) are dangerous and expensive. Idiosyncratic ADRs, especially rare and severe hypersensitivity-driven ADRs, are the leading cause of medicine withdrawal and termination of clinical development.
The Cancer Genome Atlas project has generated a daunting amount of genomic and deep sequencing data for tens of thousands of human tumors. This provided unprecedented opportunities to systematically analyze the cancer genomes to discover driving genetic alternations and develop novel therapeutics. In the past a few years, we have developed the computational approaches that interrogates multiple levels of genomic data to reveal cancer-causal genes and therapeutic targets.
Posada: According to the substance abuse and mental health services administration (SAMHSA) 43.1 million of adults (18.1%) in the US experienced some form of mental illness. Hospitalization for these of diseases is increasing at a faster rate than any other type of hospitalization. From the psychiatric hospitalized patients, 15% of all discharges were readmitted within 30 days and the costs among those readmissions are higher than for any other readmission cause. Moreover, CMS releases individual hospital readmission rates to the public as an indicator of quality of care.
Predicting Psychiatric Hospital Admission Among Adults with Major Depressive Disorder
Recent advances in next-generation sequencing (NGS) technologies have provided us with an unprecedented opportunity to better characterize the molecular signatures of human cancers. One hallmark of cancer genomes is aneuploidy, which engenders abnormal copy numbers amongst broadly connected sets of alleles. Structural variations (SVs) further modify the aneuploid cancer genomes into a mixture of rearranged genomic segments with extensive somatic copy number alterations (CNAs).