Biomedical and Health Sciences
A collection of Python pickles objects containing a Pandas DataFrame. Each Dataframe corresponds to the postprocessed firing rate (fr) in Hz and mean amplitude of the spikes (AMP) in microV/s of the vagus nerve recordings obtained from 12 adult female Sprague-Dawley rats. Additionally, the blood-glucose level in mg/dL is included. The fr and AMP signals have 0.1 miliseconds of resolution, whereas the glucose level was measured approximately every 5 minutes. Temporal variations are due to experimental factors. The number of available glucose samples changes across recordings.
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The dataset consists of two primary files: dataset.json and analysis_script.ipynb. The dataset.json file contains structured records of AI-assisted psychological therapy sessions, including emotion recognition, NLP techniques, cognitive behavioral therapy (CBT) patterns, hypnotherapy data, user feedback, and therapy outcomes. The analysis_script.ipynb Jupyter Notebook provides data preprocessing, visualization, and statistical analysis of therapy session outcomes.
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1203 Views
The medical biometric dataset comprises 10,000 records collected across 23 patients spanning different demographics, biometric profiles, and temporal variations between 2022 and 2023. It is accumulated from various hospitals, digital health records, and biometric-enabled healthcare security systems.
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299 Views
This dataset is designed for research on 2D Multi-frequency Electrical Impedance Tomography (mfEIT). It includes:
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This dataset is from our paper "Bridging Lab-to-Clinic: Microbiological Screening via Swin-Ultra Transformer with Transfer Learning", which aims to validate the extension of the lab-verified bacterial classification model to the gene-type screening of unseen pathogens in clinical settings.
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103 Views
The data of ROSMAP dataset have been preprocessed and dimensionally reduced in the original research, thus we did not perform further preprocessing on it. For SCZ dataset, we firstly removed features with more than 50% missing or 0 expression values for all omics sets. Log transformation was then utilized to normalize omics expression values, and the Z-score method was used to standardize all features of each sample in every omics sets. Only samples presented in both omics sets and label set were retained in the dataset of analysis.
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169 Views
The accurate identification of miRNA-disease associations plays a crucial role in biomedical research and clinical applications. However, most research focuses on the existence of the association, without conducting further exploration. In this study, we propose a novel statistical meta-path contrastive learning-based approach (SMCLMDA), which aims to accurately identify the multidimensional relationships(up/down-regulation and causal/non-causal) between miRNAs and diseases.
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46 Views
Most promoters are derived from an arbitrary truncation of sequences upstream from the transcription start site of a gene, which is typically around 1,000 base pairs. Since the truncation is arbitrary, regulatory elements might be missing for transcription. Unfortunately, there exists no reasonable rationale for selecting a truncation threshold. Therefore, providing a reasonable rationale for truncation is crucial for obtaining the expected expression profiles of genes.
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This study explores the potential of electromyography (EMG) decoding to enhance motor function outcomes, focusing on developing an innovative EMG-based hand movement classification system. Leveraging advanced signal processing and machine learning techniques, our objectives are twofold: (1) optimize EMG decoding performance through time-domain windowing, feature selection, and classifier optimization, and (2) assess the system's effectiveness in classifying 15 distinct finger movements.
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221 Views
We curated and release a real-world medical clinical dataset, namely MedCD, in the context of building generative artificial intelligence (AI) applications in the clinical setting. The MedCD dataset is one of the accomplishments from our longitudinal applied AI research and deployment in a tertiary care hospital in China. First, the dataset is real and comprehensive, in that it was sourced from real-world electronic health records (EHRs), clinical notes, lab examination reports and more.
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304 Views