Data Science in Medicine & Biology III

A4LB: Data Science in Medicine & Biology III

Session Type: Lecture
Session Code: A4L-B
Location: Room 2
Date & Time: Wednesday March 22, 2023 (14:00 - 15:00)
Chair: Pablo Iglesias
Track: 5
Paper IDPaper NameAuthorsAbstract
3177Quantifying Phase-Amplitude Modulation in Neural DataVictoria Subritzky-Katz{1}, Aaron Sampson{1}, Erik Emeric{1}, Witold Lipski{2}, Sophia Moreira-González{2}, Jorge González-Martínez{2}, Sridevi Sarma{1}, Veit Stuphorn{1}, Ernst Niebur{1}Phase-amplitude modulation (the modulation of the amplitude of higher frequency oscillations by the phase of lower frequency oscillations) is a specific type of cross-frequency coupling that has been observed in neural recordings from multiple species in a range of behavioral contexts. Given its potential importance, care must be taken with how it is measured and quantified. Previous studies have quantified phase-amplitude modulation by measuring the distance of the amplitude distribution from a uniform distribution. While this method is of general applicability, it is not targeted to the specific modulation pattern observed with low-frequency oscillations. Here we develop a new method that has increased specificity to detect modulation in the sinusoidal shape commonly observed in neural data.
3022A Decision Making Model Where the Cell Exhibits Maximum Detection Probability: Statistical Signal Detection Theory and Molecular Experimental DataAli Emadi{1}, Tomasz Lipniacki{2}, Andre Levchenko{3}, Ali Abdi{1}Molecular noise and signaling abnormalities in biochemical signaling systems in cells affect signaling events and consequently may alter cellular decision making results. Since unexpected and altered cellular decisions may contribute to the development of many pathological conditions and diseases, it is of interest to develop proper models to characterize and measure molecular signal detection parameters and cellular decisions. In this paper and using the Neyman-Pearson signal detection theorem, we propose a signal detection model in which the cell maximizes its signal detection probability in the presence of noise. To evaluate the usefulness of the proposed model, we use measured molecular experimental data of the important TNF—NF-κB cell signaling system. Our results demonstrate that the proposed model provides biologically relevant findings. The introduced Neyman-Pearson-based molecular signal detection framework allows to systematically model and quantify the signal detection behavior and failure of molecular signaling systems, and compute their key decision making parameters such as detection and false alarm probabilities. With regard to the specific TNF—NF-κB system case study in this paper and given the high involvement of the transcription factor NF-κB in cell survival, programmed cell death, immune signaling and stress response, the developed signal detection framework can serve as a useful tool to model the associated cell decision making processes.
3116Improving Particle Thompson Sampling Through Regenerative ParticlesZeyu Zhou{1}, Bruce Hajek{2}This paper proposes regenerative particle Thompson sampling (RPTS) as an improvement of particle Thompson sampling (PTS) for solving general stochastic bandit problems. PTS approximates Thompson sampling by replacing the continuous posterior distribution with a discrete distribution supported at a set of weighted static particles. PTS is flexible but may suffer from poor performance due to the tendency of the probability mass to concentrate on a small number of particles. RPTS exploits the particle weight dynamics of PTS and uses non-static particles: it deletes a particle if its probability mass gets sufficiently small and regenerates new particles in the vicinity of the surviving particles. Empirical evidence shows uniform improvement across a set of representative bandit problems without increasing the number of particles.