Allergy

Predicting adaptive immune receptor specificities by machine learning is a data generation problem.

Determining the specificity of adaptive immune receptors-B cell receptors (BCRs), their secreted form antibodies, and T cell receptors (TCRs)-is critical for understanding immune responses and advancing immunotherapy and drug discovery. Immune receptors exhibit extensive diversity in their variable domains, enabling them to interact with a plethora of antigens. Despite the significant progress mad...

From Bench to Bedside: A Review of Clinical Trials in Drug Discovery and Development

Clinical trials are an indispensable part of the drug development process, bridging the gap between basic research and clinical application. During the development of new drugs, clinical trials are used not only to evaluate the safety and efficacy of the drug but also to explore its dosage, treatment regimens, and potential side effects. This review discusses the various stages of clinical trial...

Exploring the Impact of Synthetic Data on Human Gesture Recognition Tasks Using GANs

In the evolving domain of Human Activity Recognition (HAR) using Internet of Things (IoT) devices, there is an emerging interest in employing Deep G...

"Oh, sh*t! I actually opened the document!": An Empirical Study of the Experiences with Suspicious Emails in Virtual Reality Headsets

This paper reports on a study exploring user experiences with suspicious emails and associated warnings when accessed through virtual reality (VR) h...

Deciphering the Role of SLFN12: A Novel Biomarker for Predicting Immunotherapy Outcomes in Glioma Patients Through Artificial Intelligence.

Gliomas are the most prevalent form of primary brain tumours. Recently, targeting the PD-1 pathway with immunotherapies has shown promise as a novel g...

Multimodal Integration of Longitudinal Noninvasive Diagnostics for Survival Prediction in Immunotherapy Using Deep Learning

Purpose: Analyzing noninvasive longitudinal and multimodal data using artificial intelligence could potentially transform immunotherapy for cancer p...

DapPep: Domain Adaptive Peptide-agnostic Learning for Universal T-cell Receptor-antigen Binding Affinity Prediction

Identifying T-cell receptors (TCRs) that interact with antigenic peptides provides the technical basis for developing vaccines and immunotherapies. ...

A transformer-based deep learning survival prediction model and an explainable XGBoost anti-PD-1/PD-L1 outcome prediction model based on the cGAS-STING-centered pathways in hepatocellular carcinoma.

Recent studies suggest cGAS-STING pathway may play a crucial role in the genesis and development of hepatocellular carcinoma (HCC), closely associated...

Attention-aware differential learning for predicting peptide-MHC class I binding and T cell receptor recognition.

The identification of neoantigens is crucial for advancing vaccines, diagnostics, and immunotherapies. Despite this importance, a fundamental question...

Cloned Identity Detection in Social-Sensor Clouds based on Incomplete Profiles

We propose a novel approach to effectively detect cloned identities of social-sensor cloud service providers (i.e. social media users) in the face o...

Automated detection of maxillary sinus opacifications compatible with sinusitis from CT images.

BACKGROUND: Sinusitis is a commonly encountered clinical condition that imposes a considerable burden on the healthcare systems. A significant number ...

Integrated machine learning developed a prognosis-related gene signature to predict prognosis in oesophageal squamous cell carcinoma.

The mortality rate of oesophageal squamous cell carcinoma (ESCC) remains high, and conventional TNM systems cannot accurately predict its prognosis, t...

Unveiling Varied Cell Death Patterns in Lung Adenocarcinoma Prognosis and Immunotherapy Based on Single-Cell Analysis and Machine Learning.

Programmed cell death (PCD) pathways hold significant influence in the etiology and progression of a variety of cancer forms, particularly offering pr...

E(3)-invariant diffusion model for pocket-aware peptide generation

Biologists frequently desire protein inhibitors for a variety of reasons, including use as research tools for understanding biological processes and...

Disentangling Genotype and Environment Specific Latent Features for Improved Trait Prediction using a Compositional Autoencoder

This study introduces a compositional autoencoder (CAE) framework designed to disentangle the complex interplay between genotypic and environmental ...

A House United Within Itself: SLO-Awareness for On-Premises Containerized ML Inference Clusters via Faro

This paper tackles the challenge of running multiple ML inference jobs (models) under time-varying workloads, on a constrained on-premises productio...

Grounded GUI Understanding for Vision Based Spatial Intelligent Agent: Exemplified by Virtual Reality Apps

In recent years, spatial computing Virtual Reality (VR) has emerged as a transformative technology, offering users immersive and interactive experie...

DANCE: Deep Learning-Assisted Analysis of Protein Sequences Using Chaos Enhanced Kaleidoscopic Images

Cancer is a complex disease characterized by uncontrolled cell growth. T cell receptors (TCRs), crucial proteins in the immune system, play a key ro...

Epidermal renewal during the treatment of atopic dermatitis lesions: A study coupling line-field confocal optical coherence tomography with artificial intelligence quantifications: LC-OCT reveals new biological markers of AD.

OBJECTIVE: This study explores the application of Line-field Confocal Optical Coherence Tomography (LC-OCT) imaging coupled with artificial intelligen...

Predicting T-Cell Receptor Specificity

Researching the specificity of TCR contributes to the development of immunotherapy and provides new opportunities and strategies for personalized ca...