Recent Papers


  1. Harnessing Qatar Biobank to understand type 2 diabetes and obesity in adult Qataris from the First Qatar Biobank Project

    Ullah, E., Mall, R., Rawi, R., Moustaid, N.M., Butt, A.A. and Bensmail, H., 2018.

    Journal of translational medicine, 16(1), p.99.

  2. ISaaC: Identifying Structural Relations in Biological Data with Copula-Based Kernel Dependency Measures

    Al Meer, H., Mall, R., Ullah, E., Megrez, N. and Bensmail, H., 2018, April.

    In International Conference on Bioinformatics and Biomedical Engineering (pp. 71-82). Springer, Cham.

  3. An unsupervised disease module identification technique in biological networks using novel quality metric based on connectivity, conductance and modularity

    Mall, R., Ullah, E., Kunji, K., Ceccarelli, M. and Bensmail, H., 2018.

    F1000Research, 7.

  4. RGBM: regularized gradient boosting machines for identification of the transcriptional regulators of discrete glioma subtypes

    Mall, R., Cerulo, L., Garofano, L., Frattini, V., Kunji, K., Bensmail, H., Sabedot, T.S., Noushmehr, H., Lasorella, A., Iavarone, A. and Ceccarelli, M., 2018.

    Nucleic Acids Research.

  5. Whole genome sequencing of Caribbean Hispanic families with late‐onset Alzheimer's disease

    Vardarajan, B.N., Barral, S., Jaworski, J., .., Saad, M., .., Koboldt, D.C. and Waligorski, J., 2018.

    Annals of clinical and translational neurology, 5(4), pp.406-417.

  6. Quality Control and Integration of Genotypes from Two Calling Pipelines for Whole Genome Sequence Data in the Alzheimer's Disease Sequencing Project

    Naj, A.C., Lin, H., .., Saad, M., .., Martin, E. and DeStefano, A.L., 2018.

    bioRxiv, p.318857.

  7. A metabolic function of FGFR3-TACC3 gene fusions in cancer

    Frattini, V., Pagnotta, S.M., .., Mall, R., .., Ceccarelli, M., .., Lasorella, A. and Iavarone, A., 2018.

    Nature.

  8. DeepSol: A Deep Learning Framework for Sequence-Based Protein Solubility Prediction

    Khurana, S., Rawi, R., Kunji, K., Chuang, G.Y., Bensmail, H. and Mall, R., 2018.

    Bioinformatics.

  9. Open community challenge reveals molecular network modules with key roles in diseases

    Choobdar, S., Ahsen, M.E., DREAM Module Identification Challenge Consortium: Kunji, K., .., Mall, R., .. and Ullah, E., Bergmann, S. and Marbach, D., 2018.

    bioRxiv, p.265553.

  10. DeepCrystal: A Deep Learning Framework for Sequence-based Protein Crystallization Prediction

    Elbasir, A., Moovarkumudalvan, B., Kunji, K., Kolatkar, P. R., Mall, R., Bensmail, H., 2018.

    Bioinformatics.


  1. PaRSnIP: sequence-based protein solubility prediction using gradient boosting machine

    Rawi, R., Mall, R., Kunji, K., Shen, C.H., Kwong, P.D. and Chuang, G.Y., 2017.

    Bioinformatics

  2. Differential Community Detection in Paired Biological Networks

    Mall, R., Ullah, E., Kunji, K., D'Angelo, F., Bensmail, H. and Ceccarelli, M., 2017, August.

    Proceedings of the 8th ACM International Conference on Bioinformatics, Computational Biology, and Health Informatics (pp. 330-339). ACM.

  3. Detection of statistically significant network changes in complex biological networks

    Mall, R., Cerulo, L., Bensmail, H., Iavarone, A. and Ceccarelli, M., 2017.

    BMC systems biology, 11(1), p.32.

  4. Advanced Computation of Sparse Precision Matrices for Big Data

    Baggag, A., Bensmail, H. and Srivastava, J., 2017, May.

    Pacific-Asia Conference on Knowledge Discovery and Data Mining (pp. 27-38). Springer, Cham.

  5. Integrative Statistical Inferences for Drug Sensitivity Biomarkers in Cancer

    Ullah, E., Shama, S., Al Muftah, N., Thompson, I.R., Rawi, R., Mall, R. and Bensmail, H., 2017.

    bioRxiv, p.194670.

  6. GIGI-Quick: A Fast Approach to Impute Missing Genotypes in Genome-Wide Association Family Data.

    Kunji, K., Ullah, E., Nato, A.Q., Wijsman, E.M. and Saad, M., 2017.

    Bioinformatics, p.btx782.

  7. An adaptive refinement for community detection methods for disease module identification in biological networks using novel metric based on connectivity, conductance and modularity

    Mall, R.,Ullah, E.,Kunji, K., Bensmail, H. and M Ceccarelli

    Bioinformatics and Biomedicine (BIBM)

  8. Application of High-Dimensional Statistics and Network based Visualization techniques on Arab Diabetes and Obesity data

    Mall, R., Ullah, E., Kunji, K., Bensmail, H. and Ceccarelli, M., 2017, November.

    Bioinformatics and Biomedicine (BIBM), 2017 IEEE International Conference on (pp. 2282-2284). IEEE.

  9. Identification of cancer drug sensitivity biomarkers

    Ullah, E., Mall, R., Bensmail, H., Rawi, R., Shama, S., Al Muftah, N. and Thmpson, I.R., 2017, November.

    Bioinformatics and Biomedicine (BIBM), 2017 IEEE International Conference on (pp. 2322-2324). IEEE.

  10. Selection Finder (SelFi): A computational metabolic engineering tool to enable directed evolution of enzymes

    Hassanpour, N., Ullah, E., Yousofshahi, M., Nair, N.U. and Hassoun, S., 2017.

    Metabolic Engineering Communications, 4, pp.37-47.

  11. Discovery and functional prioritization of Parkinson’s disease candidate genes from large-scale whole exome sequencing

    Jansen, I.E., Ye, H., .., IPDGC consortium members and affiliations: .., Saad, M., .., .., Shulman, J.M. and Heutink, P., 2017.

    Genome biology, 18(1), p.22.

  12. Excessive burden of lysosomal storage disorder gene variants in Parkinson’s disease

    Robak, L.A., Jansen, I.E., Van Rooij, J., Uitterlinden, A.G., Kraaij, R., Jankovic, J., International Parkinson’s Disease Genomics Consortium (IPDGC): .., Saad, M., .., Heutink, P. and Shulman, J.M., 2017.

    Brain, 140(12), pp.3191-3203.

  13. Association Score Testing for Rare Variants and Binary Traits in Family Data with Common Controls

    Saad, M. and Wijsman, E.M., 2017.

    Briefings in bioinformatics.

  14. Accounting for Cryptic Relatedness across Families in Family-based Association Testing

    Wijsman, E.M., Blue, E.E., Day, T.R., Nato Jr, A.Q., Sohi, H.K., Horimoto, A.R., Nafikov, R., Saad, M.H. and Thornton, T.A., 2017, November.

    GENETIC EPIDEMIOLOGY (Vol. 41, No. 7, pp. 707-707). 111 RIVER ST, HOBOKEN 07030-5774, NJ USA: WILEY.

  15. Predictive and Comparative Network Analysis of the Gut Microbiota in Type 2 Diabetes

    Abbas, M. and EL-Manzalawy, Y., 2017, August.

    Proceedings of the 8th ACM International Conference on Bioinformatics, Computational Biology, and Health Informatics (pp. 313-320). ACM.

  16. Parallelizing Partial Digest Problem on Multicore System

    Bahig, H.M., Abbas, M.M. and Mohie-Eldin, M.M., 2017, April.

    International Conference on Bioinformatics and Biomedical Engineering (pp. 95-104). Springer, Cham.

  17. In-Memory Distributed Matrix Computation Processing and Optimization

    Yu, Y., Tang, M., Aref, W.G., Malluhi, Q.M., Abbas, M.M. and Ouzzani, M., 2017, April.

    Data Engineering (ICDE), 2017 IEEE 33rd International Conference on (pp. 1047-1058). IEEE.


  1. Denoised Kernel Spectral Data Clustering

    Mall, R., Bensmail, H., Langone, R., Varon, C. and Suykens, J.A., 2016, July.

    Neural Networks (IJCNN), 2016 International Joint Conference on (pp. 3709-3716). IEEE..

  2. Metabolomic Data Profiling for Diabetes Research in Qatar

    Mall, R., Berti-Equille, L. and Bensmail, H., 2016, September

    Database and Expert Systems Applications (DEXA), 2016 27th International Workshop on (pp. 39-43). IEEE.

  3. COUSCOus: improved protein contact prediction using an empirical Bayes covariance estimator

    Rawi, R., Mall, R., Kunji, K., El Anbari, M., Aupetit, M., Ullah, E. and Bensmail, H., 2016

    BMC bioinformatics, 17(1), p.533.

  4. A design study to identify inconsistencies in kinship information: The case of the 1000 Genomes project

    Aupetit, M., Ullah, E., Rawi, R. and Bensmail, H., 2016, April

    2016 IEEE (pp. 254-258). IEEE.

  5. Fast in-memory spectral clustering using a fixed-size approach

    Langone, R., Mall, R., Vilen Jumutc, V. and Suykens, J., 2016, April

    Proc. of the 24th european symposium on artificial neural networks, computational intelligence and machine learning (pp. 557-562).

  6. Statistical and Network Analysis of Metabolomics Data

    Ullah, E., Mall, R., Rawi, R. and Bensmail, H., 2016, October

    Proceedings of the 7th ACM International Conference on Bioinformatics, Computational Biology, and Health Informatics (pp. 544-545). ACM.

  7. Kernel spectral clustering and applications

    Langone, R., Mall, R., Alzate, C. and Suykens, J.A., 2016

    In Unsupervised Learning Algorithms (pp. 135-161). Springer, Cham.

  8. Cell Line Screens Identify Biomarkers of Drug Sensitivity in GLIOMA Cancer

    Al Muftah, N., Rawi, R., Thompson, R. and Bensmail, H. , 2016

    World Academy of Science, Engineering and Technology, International Journal of Bioengineering and Life Sciences, 3(2).

  9. gEFM: an algorithm for computing elementary flux modes using graph traversal

    Ullah, E., Aeron, S. and Hassoun, S., 2016

    IEEE/ACM transactions on computational biology and bioinformatics, 13(1), pp.122-134.

  10. A fast exact sequential algorithm for the partial digest problem

    Abbas, M.M. and Bahig, H.M., 2016

    BMC bioinformatics, 17(19), p.510.

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