ARCHIVES

1. Wang Y., Liu T., Zhang C., Xu J., Wang Y. (2021). DeepDSC: A Deep Learning Method to Predict Drug Sensitivity of Genome Sickness
Cell Lines. Frontiers in Genetics, 10, 1117. https://doi.org/10.3389/fgene.2021.01117
2. Li Q., Huang J. (2021). Prediction of Anti-Drug Effectiveness Based on Multi-Fusion Deep Learning Model. IEEE Access, 9.
https://doi.org/10.1109/ACCESS.2021.3073460
3. Baptista D., Ferreira P., Rocha M. (2020). Deep Learning for Drug Response Prediction in Genome Sickness. Briefings in Bioinformatics.
https://doi.org/10.1093/bib/bbz145
4. Liu Q., Hu Z., Jiang R., Zhou M. (2020). DeepADR: A Hybrid Graph Convolutional Network for Predicting Anti-Drug Response.
Bioinformatics. https://academic.oup.com/bioinformatics/article/36/4/1147/5614550
5. Kato M., Emad A., Le D.-H., Partin A., Brettin T., Zhu Y., Narykov O., Clyde A., Overbeek J., Stevens R. (2023). Deep Learning Methods
for Drug Response Prediction in Genome Sickness: Predominant and Emerging Trends. Briefings in Bioinformatics, 22(3), 1429-1443.
https://doi.org/10.1093/bib/bbac309
6. Ma J., Fong S., Luo Y., Bakkenist C.J., Shen J.P., Mourragui S., et al. (2018). Few-Shot Learning Creates Predictive Models of Drug
Response That Translate from High-Throughput Screens to Individual Patients. Nature Cancer. https://doi.org/10.1038/s43018-018-0001
7. Sharifi-Noghabi H., Jahangiri-Tazehkand S., Smirnov P., Hon C., Mammoliti A., Nair S.K., et al. (2019). MOLI: Multi-Omics Late
Integration with Deep Neural Networks for Drug Response Prediction. Bioinformatics. https://doi.org/10.1093/bioinformatics/btz318
8. Manica M., Oskooei A., Born J., Subramanian V., Sáez-Rodríguez J., Rodríguez Martínez M. (2019). Toward Explainable Anticancer
Compound Sensitivity Prediction via Multimodal Attention-Based Convolutional Encoders. Molecular Pharmaceutics.
https://doi.org/10.1021/acs.molpharmaceut.9b00520
9. Chiu Y.C., Chen H.I., Zhang T., Zhang S., Gorthi A., Wang L.J., et al. (2019). Predicting Drug Response of Tumors from Integrated Genomic
Profiles by Deep Neural Networks. BMC Medical Genomics. https://doi.org/10.1186/s12920-019-0481-0
10. Kuenzi B.M., Park J., Fong S.H., Sanchez K.S., Lee J., Kreisberg J.F., et al. (2020). Predicting Drug Response and Synergy Using a Deep
Learning Model of Human Cancer Cells. Cancer Cell. https://doi.org/10.1016/j.ccell.2020.03.014
11. Zitnik M., Agrawal M., Leskovec J. (2018). Modeling Polypharmacy Side Effects with Graph Convolutional Networks. Bioinformatics.
https://doi.org/10.1093/bioinformatics/bty294
12. He X., Zhao K., Chu X. (2020). AutoML: A Survey of the State-of-the-Art. Knowledge-Based Systems.
https://doi.org/10.1016/j.knosys.2019.106622
13. Esteva A., Robicquet A., Ramsundar B., Kuleshov V., DePristo M., Chou K., et al. (2019). A Guide to Deep Learning in Healthcare. Nature
Medicine. https://doi.org/10.1038/s41591-018-0316-z
14. Rajkomar A., Dean J., Kohane I. (2019). Machine Learning in Medicine. New England Journal of Medicine.
https://doi.org/10.1056/NEJMra181425915. Miotto R., Wang F., Wang S., Jiang X., Dudley J.T. (2018). Deep Learning for Healthcare: Review, Opportunities and Challenges. Briefings
in Bioinformatics. https://doi.org/10.1093/bib/bbx044
16. Neelam LabhadeKumar, Mangala S Biradar, Ashvini Narayan Pawale,"Reinforcement Learning-Based Deep FEFM for Blockchain
Consensus Mechanism Optimization with Non-Linear Analysis"Journal of Computational Analysis and Applications, Vol. 33 No. 05 (2024)
17. Neelam Labhade-Kumar “Shot Boundary Detection Using Artificial Neural Network”, Advances in Signal and Data Processing. Lecture
Notes in Electrical Engineering, Springer, Vol 703. PP-44-55 Jan-2021
18. Neelam Labhade-Kumar Optimizing Cluster Head Selection in Wireless Sensor Networks Using Mathematical Modeling and Statistical
Analysis of The Hybrid Energy-Efficient Distributed (HEED) Algorithm, Communications on Applied Nonlinear Analysis, ISSN: 1074-
133XVol 31 No. 6s (2024), PP-602-617 August 2024
19. Neelam Labhade-Kumar “Experimental Design of Electricity Theft Detection and Alert System Using Arduino Assisted Controller and Smart
Sensors"7th International Conference on Inventive Computation Technologies, IEEE Xplore Part Number : CFP24F70-ART ; ISBN : 979-
8-3503-5929-9, 2024,PP-1961-1968
20. Dr.Neelam Labhade-Kumar “Novel Management Trends Using IOT in Indian Automotive Spares Manufacturing Industries”, Journal of
Pharmaceutical Negative Results , Vol. 13 ISSUE 09,PP 4887-4899, Nov-2022
21. Dr.Neelam Labhade-Kumar “Adaptive Hybrid Bird Swarm Optimization Based Efficient Transmission In WSN”, Journal of Pharmaceutical
Negative Results, Vol. 14 ISSUE 02,PP-480-484, Jan-2023,
Neelam Labhade-Kumar “Combining Hand-crafted Features and Deep Learning for Automatic Classification of Lung Cancer on CT Scans”,
Journal of Artificial Intelligence and Technology, 2023
22. Neelam Labhade-Kumar “Enhancing Crop Yield Prediction in Precision Agriculture through Sustainable Big Data Analytics and Deep
Learning Techniques”, Carpathian Journal of Food Science and Technology,2023, Special Issue, 1-18
23. Neelam Labhade-Kumar “Accident prevention and management system in urban VANET for improving slippery roads ride after rain”
Journal of environmental protection and ecology, ISSN:1311-5065 Issue 2 volume 25,PP 586–599,2024
24. Prof. Dr. Neelam Labhade-Kumar, An image processing method for kidney stone segmentation in CT scan images based on CNN-regularized
extreme learning machine approach, Hybrid and Advanced Technologies, PP- 217-222,202