21 results on '"Nair, Prashant"'
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2. Non-invasive early diagnosis of jaundice with computer vision
3. Increasing Employability of Indian Engineering Graduates through Experiential Learning Programs and Competitive Programming: Case Study
4. A genomics-informed computational biology platform prospectively predicts treatment responses in AML and MDS patients
5. Chapter 35 - Motion Encoded MRI and Elastography
6. Energy-efficient secure pattern based data aggregation for wireless sensor networks
7. Differentiation Scoring (DS) Derived from Cellworks Computational Omics Biology Model (CBM) Predicts Response to Hypomethylating Agents (HMA) and Patient Survival in Myelodysplastic Syndrome (MDS)
8. Characterization of the Endosomal Sorting Signal of the Cation-dependent Mannose 6-Phosphate Receptor
9. Biosimulation Using the Cellworks Computational Omics Biology Model (CBM) Identifies Novel Biomarkers to Inform Mitoxantrone, Etoposide, and Cytarabine (MEC)-Based Combination Therapy in Refractory & Relapsed Acute Myeloid Leukemia (AML) Patients
10. Biosimulation Using the Cellworks Computational Omics Biology Model (CBM) Identifies Immune Modulation As a Key Pathway for Predicting Azacitidine (AZA) Response in Myelodysplastic Syndromes (MDS)
11. Biosimulation Using the Cellworks Computational Omics Biology Model (CBM) Identifies Genomic and Molecular Markers for Decitabine (DAC) Plus Valproic-Acid (VPA) Treatment Response in Patients with Myelodysplastic Syndromes (MDS)
12. Therapy Biosimulation Using the Cellworks Computational Omics Biology Model (CBM) Is Predictive of Individual Acute Myeloid Leukemia (AML) Patient Probability of Clinical Response (CR) and Overall Survival (OS): Mycare-023
13. Superior Therapy Response Predictions for Patients with Myelodysplastic Syndrome (MDS) Using Cellworks Singula™: Mycare-020-02
14. Comparative Analysis for Differential Drug Response between Early T-Cell Precursor Acute Lymphoblastic Leukemia (ETP-ALL) and T-Cell Acute Lymphoblastic Leukemia (T-ALL) Patients Using the Cellworks Omics Biology Model (CBM): Mycare-021-03
15. Monosomy 7 and Co-Occurrent Genomic Aberrations Determine Chemotherapy Response in Acute Myeloid Leukemia (AML) Patients Using the Cellworks Omics Biology Model (CBM): Mycare-021-04
16. Prediction of Clinical Response for Frontline Treatment of Acute Myeloid Leukemia (AML) Patients Using the Cellworks Omics Biology Model (CBM): Mycare-021-02
17. Predicting Resistance to the Combination of ATO and ATRA in APL Patients with PML-Rara Fusions, Using a Computational Biology Modeling Approach: Mycare-021-01
18. Assessment of Cellworks Omics Biosimulation Therapy Response Predictions for Patients with Acute Myeloid Leukemia (AML) Using Cellworks Singula™: Mycare-020-01
19. Icare 1: A Prospective Clinical Trial to Predict Treatment Response Based on Mutanome-Informed Computational Biology in Patients with Acute Myeloid Leukemia (AML) and Myelodysplastic Syndromes (MDS)
20. Predicting Non-Responders to Immunotherapy Treatments through Simulation of NGS Information
21. A Novel Method of Using Molecular Profiling in Myelodysplastic Syndromes to Predict Patient-Specific Potential Therapeutics
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