Latest AI and machine learning research in infectious disease for healthcare professionals.
As cancer cases continue to rise, with a 2023 study from Zhejiang and Harvard predicting a 31 percent increase in cases and a 21 percent increase in deaths by 2030, the need to find more effective treatments for cancer is greater than ever before. Traditional approaches to treating cancer, such as chemotherapy, often kill healthy cells because of their lack of targetability. In contrast, persona...
The success of machine learning models relies heavily on effectively representing high-dimensional data. However, ensuring data representations capture human-understandable concepts remains difficult, often requiring the incorporation of prior knowledge and decomposition of data into multiple subspaces. Traditional linear methods fall short in modeling more than one space, while more expressive ...
This study evaluates the concordance between RNA sequencing (RNA-Seq) and NanoString technologies for gene expression analysis in non-human primates...
Identifying mutations of SARS-CoV-2 strains associated with their phenotypic changes is critical for pandemic prediction and prevention. We compared...
Navigation presents a significant challenge for persons with visual impairments (PVI). While traditional aids such as white canes and guide dogs are...
Fungal keratitis is a severe vision-threatening corneal infection with a prognosis influenced by fungal virulence and the host's immune defense mech...
During the early stages of respiratory virus outbreaks, such as severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), the efficient utilize ...
In human-centric tasks such as healthcare and education, the heterogeneity among patients and students necessitates personalized treatments and inst...
This work presents an omics-driven modeling pipeline that integrates machine-learning tools to facilitate the dynamic modeling of multiscale biologi...
Artificial intelligence (AI) has become indispensable for managing and processing the vast amounts of data generated during the COVID-19 pandemic. O...
Cohort discovery is a crucial step in clinical research on Electronic Health Record (EHR) data. Temporal queries, which are common in cohort discove...
This work aims to assess the molecular architectures of anti-tuberculosis drugs using both degree-based topological indices and novel distance based...
A central question in human immunology is how a patient's repertoire of T cells impacts disease. Here, we introduce a method to infer the causal eff...
We introduce DynaCLR, a self-supervised framework for modeling cell dynamics via contrastive learning of representations of time-lapse datasets. Liv...
This letter comments on the article that developed and tested a machine learning model that predicts lymphovascular invasion/perineural invasion statu...
Acetylation of lysine residues (K-Ace) is a post-translation modification occurring in both prokaryotes and eukaryotes. It plays a crucial role in d...
Identifying unusual brain activity is a crucial task in neuroscience research, as it aids in the early detection of brain disorders. It is common to...
Toxicity is a widespread phenomenon in competitive online video games. In addition to its direct undesirable effects, there is a concern that toxici...
Public actors are often seen as slow, especially in renewing information systems, due to complex tendering and competition regulations, which delay ...
The corona virus disease-19 (COVID-19) epidemic, the whole globe is suffering from a medical condition catastrophe that is unprecedented in scale. As ...