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Primary liver tissue cancer types are renowned to display a consistent increase in global disease burden and mortality, thus needing more effective diagnostics and treatments. Yet, integrative research efforts to identify cell-of-origin for these cancers by utilizing human specimen data were poorly established. To this end, we analyzed previously published whole-genome sequencing data for 384 tumor and progenitor tissues along with 423 publicly available normal tissue epigenomic features and sin

Early diagnosis of lung cancer to increase the survival rate, which is currently at a low range of mid-30%, remains a critical need. Despite this, multi-omics data have rarely been applied to non-small-cell lung cancer (NSCLC) diagnosis. We developed a multi-omics data-affinitive artificial intelligence algorithm based on the graph convolutional network that integrates mRNA expression, DNA methylation, and DNA sequencing data. This NSCLC prediction model achieved a 93.7% macro F1-score, indicati

<b>Background:</b> Cerebral amyloid beta (Aβ) is a hallmark of Alzheimer's disease (AD). Aβ can be detected <i>in vivo</i> with amyloid imaging or cerebrospinal fluid assessments. However, these technologies can be both expensive and invasive, and their accessibility is limited in many clinical settings. Hence the current study aims to identify multivariate cost-efficient markers for Aβ positivity among non-demented individuals using machine learning (ML) approaches. <b>Methods:</b> The relation

This study aimed to develop a deep learning (DL) model for predicting the recurrence risk of lung adenocarcinoma (LUAD) based on its histopathological features. Clinicopathological data and whole slide images from 164 LUAD cases were collected and used to train DL models with an ImageNet pre-trained efficientnet-b2 architecture, densenet201, and resnet152. The models were trained to classify each image patch into high-risk or low-risk groups, and the case-level result was determined by multiple

Scholarly events are important scientific communication channels. Our research goal is to satisfy scientists’ basic information needs by collecting, archiving and providing access to scholarly event information. Furthermore, we aim to satisfy users’ in-depth information needs by excavating scholarly meaningful information through reasoning about knowledge. A prerequisite to accomplishing this end is to define a description base for scholarly events to enable software agents to crawl and extract

Complex prompting techniques do not significantly enhance performance compared to simpler approaches. Dataset characteristics and model architecture have greater impact, suggesting simpler CoT methods may be more effective for clinical applications.

A growing number of tagging applications have begun to provide users the ability to socialise their own keywords. Tagging, which assigns a set of keywords to resources, has become a powerful way for organising, browsing, and publicly sharing personal collections of resources on the Web. It is called folksonomies. These systems on current social websites, however, have deficiencies in defining tag's meaning, and are often blocked to users in order to reuse, share, and exchange the tags across het