Lymphoma

Harnessing Computational Biology for Exact Linear B-Cell Epitope Prediction: A Novel Amino Acid Composition-Based Feature Descriptor.

Proteins embody epitopes that serve as their antigenic determinants. Epitopes occupy a central place in integrative biology, not to mention as targets for novel vaccine, pharmaceutical, and systems diagnostics development. The presence of T-cell and B-cell epitopes has been extensively studied due to their potential in synthetic vaccine design. However, reliable prediction of linear B-cell epitope...

Artificial Neural Network applied as a methodology of mosquito species identification.

There are about 200 species of mosquitoes (Culicidae) known to be vectors of pathogens that cause diseases in humans. Correct identification of mosquito species is an essential step in the development of effective control strategies for these diseases; recognizing the vectors of pathogens is integral to understanding transmission. Unfortunately, taxonomic identification of mosquitoes is a laboriou...

Predicting Response to Neoadjuvant Chemotherapy with PET Imaging Using Convolutional Neural Networks.

Imaging of cancer with 18F-fluorodeoxyglucose positron emission tomography (18F-FDG PET) has become a standard component of diagnosis and staging in o...

Finding Risk Groups by Optimizing Artificial Neural Networks on the Area under the Survival Curve Using Genetic Algorithms.

We investigate a new method to place patients into risk groups in censored survival data. Properties such as median survival time, and end survival ra...

Machine Learning-based Classification of Diffuse Large B-cell Lymphoma Patients by Their Protein Expression Profiles.

Characterization of tumors at the molecular level has improved our knowledge of cancer causation and progression. Proteomic analysis of their signalin...

ANN modelling of sediment concentration in the dynamic glacial environment of Gangotri in Himalaya.

The present study explores for the first time the possibility of modelling sediment concentration with artificial neural networks (ANNs) at Gangotri, ...

Probabilistic hazard assessment for skin sensitization potency by dose-response modeling using feature elimination instead of quantitative structure-activity relationships.

Supervised learning methods promise to improve integrated testing strategies (ITS), but must be adjusted to handle high dimensionality and dose-respon...

Application of neural networks with back-propagation to genome-enabled prediction of complex traits in Holstein-Friesian and German Fleckvieh cattle.

BACKGROUND: Recently, artificial neural networks (ANN) have been proposed as promising machines for marker-based genomic predictions of complex traits...

Evaluation of combinations of in vitro sensitization test descriptors for the artificial neural network-based risk assessment model of skin sensitization.

The skin sensitization potential of chemicals has been determined with the use of the murine local lymph node assay (LLNA). However, in recent years p...

Genetic Bee Colony (GBC) algorithm: A new gene selection method for microarray cancer classification.

Naturally inspired evolutionary algorithms prove effectiveness when used for solving feature selection and classification problems. Artificial Bee Col...

Correlation of plasma crizotinib trough concentration with adverse events in patients with anaplastic lymphoma kinase positive non-small-cell lung cancer.

BACKGROUND: Crizotinib, an ATP-competitive receptor tyrosine kinase inhibitor of both anaplastic lymphoma kinase (ALK) and the hepatocyte growth facto...

Noninvasive blood glucose sensing using near infra-red spectroscopy and artificial neural networks based on inverse delayed function model of neuron.

In this paper, a non-invasive blood glucose sensing system is presented using near infra-red(NIR) spectroscopy. The signal from the NIR optodes is pro...

Estimation of the chemical-induced eye injury using a weight-of-evidence (WoE) battery of 21 artificial neural network (ANN) c-QSAR models (QSAR-21): part I: irritation potential.

Evaluation of potential chemical-induced eye injury through irritation and corrosion is required to ensure occupational and consumer safety for indust...

Predictors of schizophrenia spectrum disorders in early-onset first episodes of psychosis: a support vector machine model.

Identifying early-onset schizophrenia spectrum disorders (SSD) at a very early stage remains challenging. To assess the diagnostic predictive value of...

Latent feature representation with stacked auto-encoder for AD/MCI diagnosis.

Recently, there have been great interests for computer-aided diagnosis of Alzheimer's disease (AD) and its prodromal stage, mild cognitive impairment ...

Binary node clustering via contrastive learning for haplotype phasing in de novo genome assembly

Accurate haplotype phasing is essential for high-quality genome assembly, yet de novo phasing of complex genomes without parental data remains challen...

Validating Artificial Intelligence Guidance for Ultrasound Acquisition and Remote Interpretation

Background: Venous thromboembolism (VTE), including deep vein thrombosis (DVT), remains a major global health burden. Diagnostic pathways rely on ultr...

SLAPBench: Benchmarking Multimodal Large Language Models for Four-Finger SLAP Fingerprint Verification

Four-finger SLAP fingerprints are flat live-scan impressions of the index, middle, ring, and little fingers of one hand, used for identity verificatio...

Large Language Model - Enhanced Decision Tree Framework for Identifying Multiple Sclerosis Diagnoses from Clinical Documentation

Background. Early diagnosis and intervention are crucial in multiple sclerosis (MS), yet diagnostic delays are common. Large language models (LLMs) su...

CAR T cell foundation model predicts immunotherapy response

Single-cell transcriptomics resolves CAR T-cell states, yet translating heterogeneous cellular signals into patient-level therapeutic response remains...