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Muhammed Khan on Muck Rack

Muhammed Khan

As seen in: Medium, Nature, PLOS ONE, The Daily Star (Bangladesh), The Financial Express (Bangladesh), The Muslim Vibe, United News of Bangladesh (UNB), HT Syndication, Dhaka Courier

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Articles

Comparative machine-learning modelling of effective thermal conductivity in nano-enhanced phase change materials

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By Faris Alqurashi, Muhammed Khan
| Nature Verified
Abstract Organic phase change materials store latent heat efficiently but their low thermal conductivity limits charging and discharging rates. This study evaluates whether a data-driven surrogate can predict conductivity enhancement across a heterogeneous, multi-source experimental database. A curated set of 150 measurements from 21 primary studies was modelled using linear regression, support-vector regression, random forest, gradient boosting and a feed-forward neural network.
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A generalizable cross-continent prediction of esophageal squamous cell carcinoma using the oral microbiome - Communications Medicine

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By Shahd ElNaggar, Wenlong Chen, Muhammed Khan, Christopher G. Mathew, Paul Ruff, Zhezhen Jin, Alfred I. Neugut, Anil K. Rustgi, Anne-Catrin Uhlemann, Tal Korem, Julian Abrams, Leanne M. Prodehl, Thomas K. Marumo
| Nature Verified
Abstract Esophageal squamous cell carcinoma (ESCC) has a poor prognosis and limited tools for early detection. Saliva is easily accessible and its microbiome composition can serve as a marker for upper gastrointestinal tract disease. This study aims to evaluate the potential of an oral microbiome signature for classifying ESCC.
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Investigating traditional and novel predictors of a single versus multiple fragility fractures in a large observational cohort

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By Hamzah Amin, Muhammed Khan, Marwan Bukhari
| PLOS ONE
Loading metrics Open Access Peer-reviewed Research Article Citation: Amin H, Khan MA, Bukhari M (2026) Investigating traditional and novel predictors of a single versus multiple fragility fractures in a large observational cohort. PLoS One 21(2): e0343413.
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