Leukemia

Identification of relevant features using SEQENS to improve supervised machine learning models predicting AML treatment outcome.

BACKGROUND AND OBJECTIVE: This study has two main objectives. First, to evaluate a feature selection methodology based on SEQENS, an algorithm for identifying relevant variables. Second, to validate machine learning models that predict the risk of complications in patients with acute myeloid leukemia (AML) using data available at diagnosis. Predictions are made at three time points: 90 days, six m...

Organoid-Guided Precision Medicine: From Bench to Bedside.

Organoid technology, as an emerging field within biotechnology, has demonstrated transformative potential in advancing precision medicine. This review systematically outlines the translational trajectory of organoids from bench to bedside, emphasizing their construction methodologies, key regulatory factors, and multifaceted applications in personalized healthcare. By recapitulating physiological ...

Proteomic profiling in cerebrospinal fluid reveal biomarkers for shunt outcome in idiopathic normal-pressure hydrocephalus.

BACKGROUND: The pathophysiology of idiopathic normal pressure hydrocephalus (iNPH) remains unclear, and the treatment strategy remains suboptimal. Thi...

Deep learning radiopathomics predicts targeted therapy sensitivity in EGFR-mutant lung adenocarcinoma.

BACKGROUND: Ttyrosine kinase inhibitors (TKIs) represent the standard first-line treatment for patients with epidermal growth factor receptor (EGFR)-m...

Detection of pathologic complete response using deep neural network-based endoscopic evaluation in patients with esophageal cancer receiving neoadjuvant chemotherapy: a nationwide multicenter retrospective study from 46 Japanese esophageal centers.

BACKGROUND: Detecting pathological complete response (pCR) preoperatively facilitated a non-surgical approach after neoadjuvant chemotherapy (NAC). We...

Implementing large language model and retrieval augmented generation to extract geographic locations of illicit transnational kidney trade.

BACKGROUND: Illicit kidney trade networks, operating globally, involve intricate interactions among various players, most notably buyers, sellers, bro...

Multimodal representations of transfer learning with snake optimization algorithm on bone marrow cell classification using biomedical histopathological images.

Bone marrow (BM) plays a crucial role in the hematopoietic process, producing all of the body's blood cells and maintaining the overall immune and hea...

Bioinformatics Approach to Identifying Molecular Targets of Isoliquiritigenin Affecting Chronic Obstructive Pulmonary Disease: A Machine Learning Pharmacology Study.

To identify the molecular targets and possible mechanisms of isoliquiritigenin (ISO) in affecting chronic obstructive pulmonary disease (COPD) by regu...

Learning Chemotherapy Drug Action via Universal Physics-Informed Neural Networks.

OBJECTIVE: Quantitative systems pharmacology (QSP) is widely used to assess drug effects and toxicity before the drug goes to clinical trial. However,...

Machine Learning-Based Prediction of Unplanned Readmission Due to Major Adverse Cardiac Events Among Hospitalized Patients with Blood Cancers.

BackgroundHospitalized patients with blood cancer face an elevated risk for cardiovascular diseases caused by cardiotoxic cancer therapies, which can ...

Histological tumor necrosis predicts decreased survival after neoadjuvant chemotherapy in head and neck squamous cell carcinoma.

OBJECTIVE: Despite growing interest in neoadjuvant therapies, there are no methods to predict radio- (RT) or chemoradiotherapy (CRT) response in head ...

Applying AI to Canada's Financial Intelligence System: Promises and Perils in Combatting Money Laundering and Terrorism Financing.

Money laundering (ML) and terrorist financing (TF) are pernicious global challenges. Estimates suggest that ML represents 2 to 4 percent of global GDP...

ALL diagnosis: can efficiency and transparency coexist? An explainble deep learning approach.

Acute Lymphoblastic Leukemia (ALL) is a life-threatening malignancy characterized by its aggressive progression and detrimental effects on the hematop...

Integrating Machine Learning and Bulk and Single-Cell RNA Sequencing to Decipher Diverse Cell Death Patterns for Predicting the Prognosis of Neoadjuvant Chemotherapy in Breast Cancer.

Breast cancer (BRCA) continues to pose a serious risk to women's health worldwide. Neoadjuvant chemotherapy (NAC) is a critical treatment strategy. Ne...

Analysis of the Relationship Between and Cytokine Gene Expression in Hematological Malignancy: Leveraging Explained Artificial Intelligence and Machine Learning for Small Dataset Insights.

This study measures expression of () and related cytokine genes in bone marrow mononuclear cells in patients with hematological malignancies, analyzi...

Targeting the MAPK signaling pathway: implications and prospects of flavonoids in 3P medicine as modulators of cancer cell plasticity and therapeutic resistance in breast cancer patients.

Cancer drug resistance poses a significant challenge in oncology, primarily driven by cancer cell plasticity, which promotes tumor initiation, progres...

Integrated bioinformatics analysis to develop diagnostic models for malignant transformation of chronic proliferative diseases.

The combined analysis of dual diseases can provide new insights into pathogenic mechanisms, identify novel biomarkers, and develop targeted therapeuti...

Complex-valued neural networks to speed-up MR thermometry during hyperthermia using Fourier PD and PDUNet.

Hyperthermia (HT) in combination with radio- and/or chemotherapy has become an accepted cancer treatment for distinct solid tumour entities. In HT, tu...

RPI-GGCN: Prediction of RNA-Protein Interaction Based on Interpretability Gated Graph Convolution Neural Network and Co-Regularized Variational Autoencoders.

RNA-protein interactions (RPIs) play an important role in several fundamental cellular physiological processes, including cell motility, chromosome re...

Predicting cell cycle stage from 3D single-cell nuclear-stained images.

The cell cycle governs the proliferation of all eukaryotic cells. Profiling cell cycle dynamics is therefore central to basic and biomedical research....