Paying attention to the SARS-CoV-2 dialect : a deep neural network approach to predicting novel protein mutations.

Predicting novel mutations has long-lasting impacts on life science research. Traditionally, this problem is addressed through wet-lab experiments, which are often expensive and time consuming. The recent advancement in neural language models has provided stunning results in modeling and deciphering sequences.

A machine learning study of COVID-19 serology and molecular tests and predictions.

Serology and molecular tests are the two most commonly used methods for rapid COVID-19 infection testing. The two types of tests have different mechanisms to detect infection, by measuring the presence of viral SARS-CoV-2 RNA (molecular test) or detecting the presence of antibodies triggered by the SARS-CoV-2 virus (serology test). A handful of studies have shown that symptoms, combined with demographic and/or diagnosis features, can be helpful for the prediction of COVID-19 test outcomes.

Community and topic modeling for infectious disease clinical trial recommendation.

Clinical trials are crucial for the advancement of treatment and knowledge within the medical community. Although the ClinicalTrials.gov initiative has resulted in a rich source of information for clinical trial research, only a handful of analytic studies have been carried out to understand this valuable data source.

Understanding and predicting COVID-19 clinical trial completion vs. cessation.

As of March 30 2021, over 5,193 COVID-19 clinical trials have been registered through Clinicaltrial.gov. Among them, 191 trials were terminated, suspended, or withdrawn (indicating the cessation of the study).

Predictive modeling of clinical trial terminations using feature engineering and embedding learning.

In this study, we propose to use machine learning to understand terminated clinical trials. Our goal is to answer two fundamental questions: (1) what are common factors/markers associated to terminated clinical trials? and (2) how to accurately predict whether a clinical trial may be terminated or not? The answer to the first question provides effective ways to understand characteristics of terminated trials for stakeholders to better plan their trials; and the answer to the second question can direct estimate the chance of success of a clinical trial in order to minimize costs. By using 311,260 trials to build a testbed with 68,999 samples, we use feature engineering to create 640 features, reflecting clinical trial administration, eligibility, study information, criteria etc.

Sensory system development influences the ontogeny of eyeblink conditioning.

A rate-limiting factor in the ontogeny of auditory eyeblink conditioning (EBC) is the development of sensory inputs to the pontine nucleus. One possible way to facilitate the emergence of EBC would be to use a conditioned stimulus (CS) that activates an earlier-developing sensory system. The goal of the current study was to investigate whether using a vibration CS would facilitate the ontogeny of delay EBC relative to an auditory CS.