Conceptual Multi-Agent Orchestration Architecture for Computational Research Workflows
Hussain, T. (2026). Conceptual Multi-Agent Orchestration Architecture for Computational Research Workflows. Zenodo.
DOI →A growing collection of research publications, preprints, software papers, books, and conference abstracts by Taufia Hussain.
Hussain, T. (2026). Conceptual Multi-Agent Orchestration Architecture for Computational Research Workflows. Zenodo.
DOI →Hussain, T., Manoj, K. M., Sukumar, N., Kavdia, M., & Anandakrishnan, A. (2026). Neuronal electricity founded in murburn-thermodynamic principles: 1. Background and basic theoretical formulation. Zenodo.
DOI →Hussain, T., Manoj, K. M., Sukumar, N., Kavdia, M., & Anandakrishnan, A. (2026). Neuronal electricity founded in murburn-thermodynamic principles: 2. Comparisons, evidenced explanations, and predictions. Zenodo.
DOI →Hussain, T. (2026). Graph-Augmented Machine Learning Framework for Residue-Level Protein–Peptide Interface Classification Using Structural and Physicochemical Features. Zenodo.
DOI →Hussain, T. (2026). Biological Tabular Data Advisor A Computational Framework for Scientifically Guided Machine Learning Workflow Recommendation in Experimental Biology. Zenodo.
DOI →Hussain, T., & Arshad, F., & Khushhal, S. (2026). Molecular Biology - The Language of Life. Zenodo.
DOI →Hussain, T. (2026). Decoding Regulatory structure from RNA-seq Data: A Reproducible Python Framework for Gene Regulatory Network Inference Using GENIE3. Zenodo.
DOI →Hussain, T. (2026). Residue-Level Feature Extraction from Protein–Peptide Complexes for Machine Learning Applications. Zenodo.
DOI →Hussain, T. (2026). Rare-Event Stability Tools: Python Utilities for Continuity-Corrected Logits and Odds Ratios (Anscombe, 1956). Zenodo.
DOI →Hussain, T. (2025). PertPy Perturb-seq Demo: A Reproducible Python Pipeline for CRISPR Single-Cell Perturbation Analysis. Zenodo.
DOI →Hussain, T. (2026). Python & AI for Modern Biological Research (Sample Chapter). In Python & AI for Modern Biological Research (pp. 1–32). Zenodo.
DOI →Hussain, T. (2026). Python for Biologists: A Practical Guide to Data Analysis. DataLens.Tools Publishing.
DataLens.Tools →Hussain, T. (2026). Python for Biologists: A Practical Guide to Data Analysis. DataLens.Tools Publishing. Amazon Kindle Direct Publishing. ASIN: B0GYLCSJC5.
Amazon →Hussain, T. (2025). The Biologist’s Guide to Data Analysis: A Practical Roadmap for Cleaning, Visualizing, and Analyzing Scientific Data with Excel & Python. Independently published via Amazon Kindle Direct Publishing. ASIN: B0F74HYDND.
Amazon →Hussain, T., & Lehmann, F.-O. (2018). Optical imaging of power muscles in Drosophila expressing genetic indicator GCaMP6 and channel channelrhodopsin. Flying Senses Symposium, Max Planck Institute, Göttingen. Zenodo.
DOI →Hussain, T., & Lehmann, F.-O. (2018). Significance of Calcium Signaling in Flight of Transgenic Drosophila. NeuroDoWo Conference. Zenodo.
DOI →Hussain, T., & Lehmann, F.-O. (2018). Light-gated channelrhodopsin-2 alters calcium signaling for power control in the indirect flight muscle of Drosophila. Meeting abstract of the German Zoological Society, Greifswald.
Hussain, T., & Lehmann, F.-O. (2017). Significance of calcium signaling in flight of transgenic Drosophila expressing the laser light-triggered calcium ion channel channelrhodopsin in asynchronous flight muscle tissue. Meeting of the German Zoological Society, Bielefeld.
Gabler, S., Soelter, J., Hussain, T., Sachse, S., & Schmuker, M. Vibrational vs. physicochemical descriptors for olfactory receptor response prediction. Molecular Informatics, 32, 855–865.
Gabler, S., Soelter, J., Hussain, T., Sachse, S., & Schmuker, M. Benchmarking physicochemical vs. vibrational descriptors in predicting odor receptor responses. Flavour, Vol. 3.