Author: sjung

Deployable Code for Early Prediabetes Detection: The PRISQ Model

Leveraging data from the Qatar Biobank, we have created and validated a deployable algorithm for prediabetes screening. The PRISQ model’s code takes basic health metrics as input and outputs a clear risk category (Low, Moderate, High). This allows for seamless integration into digital health platforms, electronic health records, and public screening tools, providing a cost-effective, […]

OutSingle

A Python tool for finding outliers in RNA-Seq gene expression count data using SVD/OHT OutSingle has been tested on Windows (11). Note that OutSingle is still in alpha stage, so encountering bugs while running it is expected. If you use OutSingle in your research you can cite our paper:Edin Salkovic, Mohammad Amin Sadeghi, Abdelkader Baggag, […]

BigQUIC: Big Quadratic Inverse Covariance Estimation

Use Newton’s method, coordinate descent, and METIS clustering to solve the L1 regularized Gaussian MLE inverse covariance matrix estimation problem. https://cran.r-project.org/web/packages/BigQuic/index.html

COUSCOus

Motivation: Current methods for predicting protein residue contacts are valuable but incomplete and do not fully agree. We developed a new method, COUSCOus, that combines advanced statistical techniques to improve accuracy. Our method consistently outperforms the established PSICOV tool across multiple benchmarks and independent tests. This demonstrates that superior statistical approaches can significantly advance protein […]

Deepsol

MotivationProtein solubility plays a vital role in pharmaceutical research and production yield. For a given protein, the extent of its solubility can represent the quality of its function, and is ultimately defined by its sequence. Thus, it is imperative to develop novel, highly accurate in silico sequence-based protein solubility predictors. ModelIn this work we propose, […]

DeepCrystal

MotivationQCRI deep learning models for crystallization propensity prediction, DeepCrystal and B, BCrystal is ready to compute. How to get started?Perform the following steps to signup:1. Navigate to the Sign Up tab in the top navigation bar.2. Fill in your details and press register, a registration confirmation mail will be sent, you will need to click […]

New Paper Published

We are pleased to announce the publication of Deep learning, transformers, and graph neural networks: a linear algebra perspective in Numerical Algorithms. Authors: Abdelkader Baggag and Yousef Saad (SIAM von Neumann Award winner). Abstract (brief): As AI permeates nearly every field of science and engineering, this article invites the numerical linear algebra (NLA) community to engage directly with the foundations of deep learning. […]