23 citations · 46 across the 32 of their papers we have counts for
6 papers · 1 filter
Leveraging Wastewater Monitoring for COVID-19 Forecasting in the US: a Deep Learning study
Mehrdad Fazli, Heman Shakeri
The outburst of COVID-19 in late 2019 was the start of a health crisis that shook the world and took millions of lives in the ensuing years. Many governments and health officials f…
Contra-Analysis: Prioritizing Meaningful Effect Size in Scientific Research
Bruce A. Corliss, Yaotian Wang, Heman Shakeri +1
At every phase of scientific research, scientists must decide how to allocate limited resources to pursue the research inquiries with the greatest potential. This prioritization di…
The Least Difference in Means: A Statistic for Effect Size Strength and Practical Significance
Bruce A. Corliss, Yaotian Wang, Heman Shakeri +1
With limited resources, scientific inquiries must be prioritized for further study, funding, and translation based on their practical significance: whether the effect size is large…
GeoTyper: Automated Pipeline from Raw scRNA-Seq Data to Cell Type Identification
Cecily Wolfe, Yayi Feng, David Chen +4
The cellular composition of the tumor microenvironment can directly impact cancer progression and the efficacy of therapeutics. Understanding immune cell activity, the body's natur…
Using Machine Learning to Evaluate Real Estate Prices Using Location Big Data
Walter Coleman, Ben Johann, Nicholas Pasternak +3
With everyone trying to enter the real estate market nowadays, knowing the proper valuations for residential and commercial properties has become crucial. Past researchers have bee…
The Most Difference in Means: A Statistic for the Strength of Null and Near-Zero Results
Bruce A. Corliss, Taylor R. Brown, Tingting Zhang +3
Statistical insignificance does not suggest the absence of effect, yet scientists must often use null results as evidence of negligible (near-zero) effect size to falsify scientifi…