NeurDocs - Advancing Research and Technology
NeurDocs is a global organization that integrates innovative research with advanced technology to drive progress in various fields. We focus on cutting-edge research, development, and education to foster growth in technology and science worldwide.
About Us
NeurDocs promotes innovation, collaboration, and education to bridge the gap between technology and society, helping communities thrive in the digital age.
Our Focus Areas:
- Artificial Intelligence and Machine Learning
- Advanced Research and Technology Development
- Data Analysis and Processing
- Software Tools and Algorithm Design
- Technological Solutions for Various Industries
- Educational and Research Programs
Our Services
NeurDocs provides a wide range of services to empower research, development, and innovation:
- Workshops, Seminars, and Tech Conferences
- Consulting Services for Software Development
- Collaboration with Universities and Industry Experts
- Training for Researchers and Professionals
- Development of Advanced AI and ML Tools
Latest News
Evaluation of normalized T1 signal intensity obtained using an automated segmentation model in lower leg MRI as a potential imaging biomarker in Charcot– Marie–Tooth disease type 1A
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mCNN-GenEfflux: enhanced predicting Efflux protein and their super families by using generative proteins combined with multiple windows convolution neural networks
Bioinformatics Bioinformatics Methodology
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Recent News
- Clinical application research on the titanium metal metatarsal prosthesis designed through FEA and manufactured by 3D printing
- Manually weighted taxonomy classifiers improve species-specific rumen microbiome analysis compared to unweighted or average weighted taxonomy classifiers
- A deep learning based smartphone application for early detection of nasopharyngeal carcinoma using endoscopic images
- sCIN: a contrastive learning framework for single-cell multi-omics data integration
- CustOmics: A versatile deep-learning based strategy for multi-omics integration
- Tracking temporal progression of benign bone tumors through X-ray based detection and segmentation
- mCNN-GenEfflux: enhanced predicting Efflux protein and their super families by using generative proteins combined with multiple windows convolution neural networks
- Evaluation of normalized T1 signal intensity obtained using an automated segmentation model in lower leg MRI as a potential imaging biomarker in Charcot– Marie–Tooth disease type 1A
Popular Paper
- sCIN: a contrastive learning framework for single-cell multi-omics data integration
- A deep learning based smartphone application for early detection of nasopharyngeal carcinoma using endoscopic images
- CustOmics: A versatile deep-learning based strategy for multi-omics integration
- Clinical application research on the titanium metal metatarsal prosthesis designed through FEA and manufactured by 3D printing
- Manually weighted taxonomy classifiers improve species-specific rumen microbiome analysis compared to unweighted or average weighted taxonomy classifiers