:: Library Catalog

Ata uhi

I tiakina i:

Ngā taipitopito rārangi puna kōrero
Ngā kaituhi matua:	Das, Bikram, Sutradhar, Rupchand, Dalal, D C
Hōputu:	Preprint
I whakaputaina:	2025
Ngā marau:	Quantitative Methods Machine Learning
Urunga tuihono:	https://arxiv.org/abs/2503.10708
Ngā Tūtohu:	Tāpirihia he Tūtohu Kāore He Tūtohu, Me noho koe te mea tuatahi ki te tūtohu i tēnei pūkete!

Ngā tūemi rite

Multi-point Infection Dynamics of Hepatitis B in the Presence of Sub-Viral Particles
mā: Sutradhar, Rupchand, me ētahi atu.
I whakaputaina: (2024)

Exploration of Hepatitis B Virus Infection Dynamics through an Intracellular Model
mā: Sutradhar, Rupchand, me ētahi atu.
I whakaputaina: (2023)

Understanding Hepatitis B Virus Infection through Hepatocyte Proliferation and Capsid Recycling
mā: Sutradhar, Rupchand, me ētahi atu.
I whakaputaina: (2024)

Sparsity is All You Need: Rethinking Biological Pathway-Informed Approaches in Deep Learning
mā: Caranzano, Isabella, me ētahi atu.
I whakaputaina: (2025)

Deep Learning Approaches for Blood Disease Diagnosis Across Hematopoietic Lineages
mā: Bo, Gabriel, me ētahi atu.
I whakaputaina: (2025)

Physics-Informed Machine Learning in Biomedical Science and Engineering
mā: Ahmadi, Nazanin, me ētahi atu.
I whakaputaina: (2025)

MC-NN: An End-to-End Multi-Channel Neural Network Approach for Predicting Influenza A Virus Hosts and Antigenic Types
mā: Xu, Yanhua, me ētahi atu.
I whakaputaina: (2023)

CMINNs: Compartment Model Informed Neural Networks -- Unlocking Drug Dynamics
mā: Daryakenari, Nazanin Ahmadi, me ētahi atu.
I whakaputaina: (2024)

Classifying the Stoichiometry of Virus-like Particles with Interpretable Machine Learning
mā: Zhang, Jiayang, me ētahi atu.
I whakaputaina: (2025)

Med-Real2Sim: Non-Invasive Medical Digital Twins using Physics-Informed Self-Supervised Learning
mā: Kuang, Keying, me ētahi atu.
I whakaputaina: (2024)

Integrating Biological-Informed Recurrent Neural Networks for Glucose-Insulin Dynamics Modeling
mā: De Carli, Stefano, me ētahi atu.
I whakaputaina: (2025)

FlowCyt: A Comparative Study of Deep Learning Approaches for Multi-Class Classification in Flow Cytometry Benchmarking
mā: Bini, Lorenzo, me ētahi atu.
I whakaputaina: (2024)

CompassDock: Comprehensive Accurate Assessment Approach for Deep Learning-Based Molecular Docking in Inference and Fine-Tuning
mā: Sarigun, Ahmet, me ētahi atu.
I whakaputaina: (2024)

Mamba-based Deep Learning Approach for Sleep Staging on a Wireless Multimodal Wearable System without Electroencephalography
mā: Zhang, Andrew H., me ētahi atu.
I whakaputaina: (2024)

Development of Machine Learning Classifiers for Blood-based Diagnosis and Prognosis of Suspected Acute Infections and Sepsis
mā: Buturovic, Ljubomir, me ētahi atu.
I whakaputaina: (2024)

Explainable Deep Learning Framework for SERS Bio-quantification
mā: Zaki, Jihan K., me ētahi atu.
I whakaputaina: (2024)

Distribution Learning for Molecular Regression
mā: Shoghi, Nima, me ētahi atu.
I whakaputaina: (2024)

Physics-Informed Neural Networks for Biological $2\mathrm{D}{+}t$ Reaction-Diffusion Systems
mā: Lavery, William, me ētahi atu.
I whakaputaina: (2026)

Optical Inversion and Spectral Unmixing of Spectroscopic Photoacoustic Images with Physics-Informed Neural Networks
mā: Ter Martirosyan, Sarkis, me ētahi atu.
I whakaputaina: (2026)

Sleep Apnea Detection on a Wireless Multimodal Wearable Device Without Oxygen Flow Using a Mamba-based Deep Learning Approach
mā: Luszczynski, Dominik, me ētahi atu.
I whakaputaina: (2025)

Deep Generative Modeling for Identification of Noisy, Non-Stationary Dynamical Systems
mā: Voina, Doris, me ētahi atu.
I whakaputaina: (2024)

Physiologically Informed Deep Learning: A Multi-Scale Framework for Next-Generation PBPK Modeling
mā: Liu, Shunqi, me ētahi atu.
I whakaputaina: (2026)

Prenatal Stress Detection from Electrocardiography Using Self-Supervised Deep Learning: Development and External Validation
mā: Frasch, Martin G., me ētahi atu.
I whakaputaina: (2026)

Learning Chemotherapy Drug Action via Universal Physics-Informed Neural Networks
mā: Podina, Lena, me ētahi atu.
I whakaputaina: (2024)

Use of Continuous Glucose Monitoring with Machine Learning to Identify Metabolic Subphenotypes and Inform Precision Lifestyle Changes
mā: Metwally, Ahmed A., me ētahi atu.
I whakaputaina: (2025)

Simulation-free Structure Learning for Stochastic Dynamics
mā: Rimawi-Fine, Noah El, me ētahi atu.
I whakaputaina: (2025)

PoseX: AI Defeats Physics Approaches on Protein-Ligand Cross Docking
mā: Jiang, Yize, me ētahi atu.
I whakaputaina: (2025)

Deep Learning Framework for RNA Inverse Folding with Geometric Structure Potentials
mā: Yao, Annabelle
I whakaputaina: (2025)

Drug-Target Interaction/Affinity Prediction: Deep Learning Models and Advances Review
mā: Vefghi, Ali, me ētahi atu.
I whakaputaina: (2025)

Learning Patient-Specific Spatial Biomarker Dynamics via Operator Learning for Alzheimer's Disease Progression
mā: Wang, Jindong, me ētahi atu.
I whakaputaina: (2025)

Q-MRS: A Deep Learning Framework for Quantitative Magnetic Resonance Spectra Analysis
mā: Wu, Christopher J., me ētahi atu.
I whakaputaina: (2024)

Identification of Capture Phases in Nanopore Protein Sequencing Data Using a Deep Learning Model
mā: Martin, Annabelle, me ētahi atu.
I whakaputaina: (2025)

DANCE: Deep Learning-Assisted Analysis of Protein Sequences Using Chaos Enhanced Kaleidoscopic Images
mā: Murad, Taslim, me ētahi atu.
I whakaputaina: (2024)

Combination Therapy for Chronic Hepatitis B Using Capsid Recycling Inhibitor
mā: Sutradhar, Rupchand, me ētahi atu.
I whakaputaina: (2024)

Molecular Odor Prediction with Harmonic Modulated Feature Mapping and Chemically-Informed Loss
mā: Xie, HongXin, me ētahi atu.
I whakaputaina: (2025)

Pathway-Guided Optimization of Deep Generative Molecular Design Models for Cancer Therapy
mā: Qayyum, Alif Bin Abdul, me ētahi atu.
I whakaputaina: (2024)

BeeTLe: An Imbalance-Aware Deep Sequence Model for Linear B-Cell Epitope Prediction and Classification with Logit-Adjusted Losses
mā: Yuan, Xiao
I whakaputaina: (2023)

DeepProtein: Deep Learning Library and Benchmark for Protein Sequence Learning
mā: Xie, Jiaqing, me ētahi atu.
I whakaputaina: (2024)

Automated Model-Free Sorting of Single-Molecule Fluorescence Events Using a Deep Learning Based Hidden-State Model
mā: Zeng, Wenqi, me ētahi atu.
I whakaputaina: (2025)

Enhancing Cell Tracking with a Time-Symmetric Deep Learning Approach
mā: Szabó, Gergely, me ētahi atu.
I whakaputaina: (2023)