Advances in computational and experimental approaches for deciphering transcriptional regulatory networks: Understanding the roles of cis-regulatory elements is essential, and recent research utilizing MPRAs, STARR-seq, CRISPR-Cas9, and machine learning has yielded valuable insights. | LitMetric
Understanding the influence of cis-regulatory elements on gene regulation poses numerous challenges given complexities stemming from variations in transcription factor (TF) binding, chromatin accessibility, structural constraints, and cell-type differences. This review discusses the role of gene regulatory networks in enhancing understanding of transcriptional regulation and covers construction methods ranging from expression-based approaches to supervised machine learning. Additionally, key experimental methods, including MPRAs and CRISPR-Cas9-based screening, which have significantly contributed to understanding TF binding preferences and cis-regulatory element functions, are explored. Lastly, the potential of machine learning and artificial intelligence to unravel cis-regulatory logic is analyzed. These computational advances have far-reaching implications for precision medicine, therapeutic target discovery, and the study of genetic variations in health and disease.
Background: Wearable technologies have become increasingly prominent in health care. However, intricate machine learning and deep learning algorithms often lead to the development of "black box" models, which lack transparency and comprehensibility for medical professionals and end users. In this context, the integration of explainable artificial intelligence (XAI) has emerged as a crucial solution.
Artificial Neural Networks (ANNs) have proven to be fantastic at a wide range of machine learning tasks, and they have certainly come into their own in all sorts of technologies that are widely consumed today in society as a whole. A basic task of machine learning that neural networks are well suited to is supervised learning, including when learning orbits from time samples of dynamical systems. The usual construct in ANN is to fully train all of the perhaps many millions of parameters that define the network architecture.
Objectives: Rituximab is emerging as a promising therapeutic option for systemic sclerosis-associated interstitial lung disease (SSc-ILD). However, little is known about factors that predict the efficacy of rituximab in SSc-ILD.
Methods: A post-hoc analysis was performed on prospective data from 48 patients with SSc-ILD in the double-blind, randomized, placebo-controlled DESIRES trial.
This study aimed to construct machine learning models and predict prolonged intensive care units (ICU) stay in patients receiving perioperative intra-aortic balloon pump (IABP) therapy during cardiac surgery. 236 patients were divided into the normal (≤ 14 days) and prolonged (> 14 days) ICU groups based on the 75th percentile of ICU duration across the entire cohort. Seven machine learning models were trained and validated.
In sensory perception, stochastic resonance (SR) refers to the application of noise to enhance information transfer, allowing for the sensing of lower-level stimuli. Previously, subjective-assessments identified SR in vestibular perceptual thresholds, assessed using a standard two alternative (i.e.
