Causal modeling allows predicting a system’s behavior not only under observation but also under intervention. Computational causal discovery reverse-engineers causal models (networks) from observational data with limited or no interventions. In this work, I will present logic-based causal discovery, a new, versatile approach for learning causal networks from observations and interventions: based on standard causal assumptions, associative patterns in the data that constrain the search space of possible causal models are expressed as a logic formula.
Machine learning is commonly described as a “field of study that gives computers the ability to learn without being explicitly programmed” (Simon, 2013). Despite this common claim, practitioners know that designing effective machine learning pipelines is often a tedious endeavor, and typically requires considerable experience with machine learning algorithms, expert knowledge of the problem domain, and brute force search to accomplish.
Saja Al-Alawneh abstract: Providing radiologists with feedback has been shown to improve their performance in mammography diagnosis. In 1992, the Mammography Quality Standards Act (MQSA) was enacted to improve the quality of mammography using audit and feedback procedures. However, no standard audit and feedback system for radiologists has been installed in the United States. Instead, auditing typically requires human effort to manually correlate radiology and pathology results.
Rathnam Abstract: Ubiquitin is arguable one of the most important molecules involved in post-translational modifications as it is present in all eukaryotic cells and plays a key role in mediating a wide assortment of biological processes, such as cell cycle regulation, endocytosis of cellular proteins, and transcriptional regulation.
Lee Abstract: The dysregulation of microRNAs (miRNAs) alters expression level of pro-oncogenic or tumor suppressive mRNAs in breast cancer, and in the long run, causes multiple biological abnormalities.