Oncology/Hematology

[Application of CT Radiomics in Predicting Differentiation Level of Lung Adenocarcinoma].

OBJECTIVE: To investigate the value of prediction of the differentiation level in lung adenocarcinoma based on CT radiomics model.

Artificial Intelligence-Driven Precision Medicine: Multi-Omics and Spatial Multi-Omics Approaches in Diffuse Large B-Cell Lymphoma (DLBCL).

In this comprehensive review, we delve into the transformative role of artificial intelligence (AI) in refining the application of multi-omics and spatial multi-omics within the realm of diffuse large B-cell lymphoma (DLBCL) research. We scrutinized the current landscape of multi-omics and spatial multi-omics technologies, accentuating their combined potential with AI to provide unparalleled insig...

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. ...

Semi-analytical model for the calculation of solar radiation pressure and its effects on a LEO satellite with predicting the change in position vectors using machine learning techniques

The rapid increase in the deployment of Low Earth Orbit (LEO) satellites, catering to diverse applications such as communication, Earth observation,...

Multi-modal Representation Learning Enables Accurate Protein Function Prediction in Low-Data Setting

In this study, we propose HOPER (HOlistic ProtEin Representation), a novel multimodal learning framework designed to enhance protein function predic...

Exploring Foundation Models Fine-Tuning for Cytology Classification

Cytology slides are essential tools in diagnosing and staging cancer, but their analysis is time-consuming and costly. Foundation models have shown ...

Leveraging Cancer Therapy Peptide Data: A Case Study on Machine Learning Application in Accelerating Cancer Research.

This study leverages the DCTPep database, a comprehensive repository of cancer therapy peptides, to explore the application of machine learning in acc...

FunlncModel: integrating multi-omic features from upstream and downstream regulatory networks into a machine learning framework to identify functional lncRNAs.

Accumulating evidence indicates that long noncoding RNAs (lncRNAs) play important roles in molecular and cellular biology. Although many algorithms ha...

Augmenting small biomedical datasets using generative AI methods based on self-organizing neural networks.

Small sample sizes in biomedical research often led to poor reproducibility and challenges in translating findings into clinical applications. This pr...

Comprehensive bioinformatics and machine learning analyses for breast cancer staging using TCGA dataset.

Breast cancer is an alarming global health concern, including a vast and varied set of illnesses with different molecular characteristics. The fusion ...

Bayesian unsupervised clustering identifies clinically relevant osteosarcoma subtypes.

Identification of cancer subtypes is a critical step for developing precision medicine. Most cancer subtyping is based on the analysis of RNA sequenci...

microT-CNN: an avant-garde deep convolutional neural network unravels functional miRNA targets beyond canonical sites.

microRNAs (miRNAs) are central post-transcriptional gene expression regulators in healthy and diseased states. Despite decades of effort, deciphering ...

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...

Towards simplified graph neural networks for identifying cancer driver genes in heterophilic networks.

The identification of cancer driver genes is crucial for understanding the complex processes involved in cancer development, progression, and therapeu...

Multimodal deep learning approaches for precision oncology: a comprehensive review.

The burgeoning accumulation of large-scale biomedical data in oncology, alongside significant strides in deep learning (DL) technologies, has establis...

DD-PRiSM: a deep learning framework for decomposition and prediction of synergistic drug combinations.

Combination therapies have emerged as a promising approach for treating complex diseases, particularly cancer. However, predicting the efficacy and sa...

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...

Multi-view multi-level contrastive graph convolutional network for cancer subtyping on multi-omics data.

Cancer is a highly diverse group of diseases, and each type of cancer can be further divided into various subtypes according to specific characteristi...

Inferring tumor purity using multi-omics data based on a uniform machine learning framework MoTP.

Existing algorithms for assessing tumor purity are limited to a single omics data, such as gene expression, somatic copy number variations, somatic mu...