Publications by authors named "Omkar Pathak"
Article Synopsis
- - The study addresses the issue of limited ancestral diversity in genome-wide association studies (GWAS), which makes it hard to find genetic risk variants in non-European ancestry groups, focusing on Alzheimer's Disease (AD).
- - Researchers analyzed a multi-ancestry GWAS dataset within the Alzheimer's Disease Genetics Consortium (ADGC) involving individuals from various ancestries, identifying 13 shared risk loci and 3 ancestry-specific loci, highlighting the benefits of diverse samples.
- - The findings underscore the importance of including underrepresented populations in genetic research, suggesting that even smaller sample sizes can lead to the discovery of novel genetic variants related to AD and implicating specific biological pathways like amyloid regulation and neuronal development.
Article Synopsis
- Acute lymphoblastic leukemia (ALL) is the most common cancer in children, and a study of 2,754 patients reveals that despite a low mutation burden, each case typically has about four important genetic alterations.
- Researchers identified 376 potential driver genes linked to various functions like gene regulation and cell processes, with many patients having unique gene changes associated with leukemia.
- The study highlights a difference in mutation patterns between B-ALL subtypes, with certain genetic alterations having significant implications for prognosis and potential treatment strategies.
Chemotherapy is a standard treatment for pediatric acute lymphoblastic leukemia (ALL), which sometimes relapses with chemoresistant features. However, whether acquired drug-resistance mutations in relapsed ALL pre-exist or are induced by treatment remains unknown. Here we provide direct evidence of a specific mechanism by which chemotherapy induces drug-resistance-associated mutations leading to relapse.
View Article and Find Full Text PDFChip floorplanning is the engineering task of designing the physical layout of a computer chip. Despite five decades of research, chip floorplanning has defied automation, requiring months of intense effort by physical design engineers to produce manufacturable layouts. Here we present a deep reinforcement learning approach to chip floorplanning.
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