Processing literature (i.e., text corpora) to capture gene regulation events is not easy and can be driven by the final data representation. We propose to build, manually, an example of temporal representation (whole gene networks for coat formation in Bacillus Subtilis). Our temporal representation is based on a generalised formal language theory (S-languages). We propose an algorithm to link bags of relations with representation, by ordering interactions. In this paper, starting from the network made manually from text data, we show that S-languages are quite relevant to encapsulate gene properties, and infer knowledge across timestamped gene relations found in texts.
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http://dx.doi.org/10.1504/ijdmb.2008.016755 | DOI Listing |
Int J Clin Health Psychol
January 2025
Department of Psychology, Guangzhou University, 230 Wai Huan Xi Road, Guangzhou Higher Education Mega Center, Guangzhou 510006, China.
Background/objective: Recent years have witnessed a surge of interest in dissecting the anticipatory and the consummatory aspects of anhedonia in terms of temporal dynamics. However, few research has directly examined reward valuation as a function of time in anhedonia.
Method: Using a delay discounting task, this event-related potential study examined the neural representation of rewards available immediately or in six months in a high-anhedonia group ( = 40) and a low-anhedonia group ( = 40) recruited from a nonclinical sample.
BMC Med Inform Decis Mak
January 2025
Great Ormond Street Institute of Child Health, University College London, London, UK.
Introduction: Unsupervised feature learning methods inspired by natural language processing (NLP) models are capable of constructing patient-specific features from longitudinal Electronic Health Records (EHR).
Design: We applied document embedding algorithms to real-world paediatric intensive care (PICU) EHR data to extract patient-specific features from 1853 patients' PICU journeys using 647 unique lab tests and medication events. We evaluated the clinical utility of the patient features via a K-means clustering analysis.
Front Robot AI
January 2025
IDLab, Ghent University-imec, Ghent, Belgium.
Smart cities deploy various sensors such as microphones and RGB cameras to collect data to improve the safety and comfort of the citizens. As data annotation is expensive, self-supervised methods such as contrastive learning are used to learn audio-visual representations for downstream tasks. Focusing on surveillance data, we investigate two common limitations of audio-visual contrastive learning: false negatives and the minimal sufficient information bottleneck.
View Article and Find Full Text PDFPerception
January 2025
State Key Laboratory of Brain and Cognitive Science, Institute of Biophysics, Chinese Academy of Sciences, China; University of Chinese Academy of Sciences, China; Hefei Comprehensive National Science Center, Institute of Artificial Intelligence, China.
Previous research has indicated that exposure to sensory stimuli of short or long durations influences the perceived duration of subsequent stimuli within the same modality. However, it remains unclear whether this adaptation is driven by the stimulus physical duration or by the perceived duration. We hypothesized that the absence of cross-modal duration adaptation observed in earlier studies was due to the mismatched perceived durations of adapting stimuli.
View Article and Find Full Text PDFPsychol Rev
January 2025
Department of Cognitive Science, University of California, San Diego.
It has long been hypothesized that episodic memory supports adaptive decision making by enabling mental simulation of future events. Yet, attempts to characterize this process are surprisingly rare. On one hand, memory research is often carried out in settings that are far removed from ecological contexts of decision making.
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