Suhas Srinivasan, Ph.D.

All Publications

• Unravelling psychiatric heterogeneity and predicting suicide attempts in women with trauma-related dissociation using artificial intelligence EUROPEAN JOURNAL OF PSYCHOTRAUMATOLOGY Srinivansan, S., Harnett, N. G., Zhang, L., Dahlgren, M., Jang, J., Lu, S., Nephew, B. C., Palermo, C. A., Pan, X., Eltabakh, M. Y., Frederick, B. B., Gruber, S. A., Kaufman, M. L., King, J., Ressler, K. J., Winternitz, S., Korkin, D., Lebois, L. M. 2022; 13 (2)

  View details for DOI 10.1080/20008066.2022.2143693

  View details for Web of Science ID 000889608300001

• Computational protein modeling and the next viral pandemic NATURE METHODS Narykov, O., Srinivasan, S., Korkin, D. 2021; 18 (5): 439-440

  View details for DOI 10.1038/s41592-021-01144-0

  View details for Web of Science ID 000648344900010

  View details for PubMedID 33963340

• Structural Genomics and Interactomics of SARS-COV2: Decoding Basic Building Blocks of the Coronavirus Virus Bioinformatics Gao, Z., Lu, S., Narykov, O., Srinivasan, S., Korkin, D. Chapman and Hall/CRC. 2021; 1: 121-139
• A hybrid deep clustering approach for robust cell type profiling using single-cell RNA-seq data RNA Srinivasan, S., Leshchyk, A., Johnson, N. T., Korkin, D. 2020; 26 (10): 1303-1319

  Abstract

  Single-cell RNA sequencing (scRNA-seq) is a recent technology that enables fine-grained discovery of cellular subtypes and specific cell states. Analysis of scRNA-seq data routinely involves machine learning methods, such as feature learning, clustering, and classification, to assist in uncovering novel information from scRNA-seq data. However, current methods are not well suited to deal with the substantial amount of noise that is created by the experiments or the variation that occurs due to differences in the cells of the same type. To address this, we developed a new hybrid approach, deep unsupervised single-cell clustering (DUSC), which integrates feature generation based on a deep learning architecture by using a new technique to estimate the number of latent features, with a model-based clustering algorithm, to find a compact and informative representation of the single-cell transcriptomic data generating robust clusters. We also include a technique to estimate an efficient number of latent features in the deep learning model. Our method outperforms both classical and state-of-the-art feature learning and clustering methods, approaching the accuracy of supervised learning. We applied DUSC to a single-cell transcriptomics data set obtained from a triple-negative breast cancer tumor to identify potential cancer subclones accentuated by copy-number variation and investigate the role of clonal heterogeneity. Our method is freely available to the community and will hopefully facilitate our understanding of the cellular atlas of living organisms as well as provide the means to improve patient diagnostics and treatment.

  View details for DOI 10.1261/rna.074427.119

  View details for Web of Science ID 000570798100002

  View details for PubMedID 32532794

  View details for PubMedCentralID PMC7491323

• Structural Genomics of SARS-CoV-2 Indicates Evolutionary Conserved Functional Regions of Viral Proteins VIRUSES-BASEL Srinivasan, S., Cui, H., Gao, Z., Liu, M., Lu, S., Mkandawire, W., Narykov, O., Sun, M., Korkin, D. 2020; 12 (4)

  Abstract

  During its first two and a half months, the recently emerged 2019 novel coronavirus, SARS-CoV-2, has already infected over one-hundred thousand people worldwide and has taken more than four thousand lives. However, the swiftly spreading virus also caused an unprecedentedly rapid response from the research community facing the unknown health challenge of potentially enormous proportions. Unfortunately, the experimental research to understand the molecular mechanisms behind the viral infection and to design a vaccine or antivirals is costly and takes months to develop. To expedite the advancement of our knowledge, we leveraged data about the related coronaviruses that is readily available in public databases and integrated these data into a single computational pipeline. As a result, we provide comprehensive structural genomics and interactomics roadmaps of SARS-CoV-2 and use this information to infer the possible functional differences and similarities with the related SARS coronavirus. All data are made publicly available to the research community.

  View details for DOI 10.3390/v12040360

  View details for Web of Science ID 000539525300002

  View details for PubMedID 32218151

  View details for PubMedCentralID PMC7232164

• Enriching Human Interactome with Functional Mutations to Detect High-Impact Network Modules Underlying Complex Diseases GENES Cui, H., Srinivasan, S., Korkin, D. 2019; 10 (11)

  Abstract

  Rapid progress in high-throughput -omics technologies moves us one step closer to the datacalypse in life sciences. In spite of the already generated volumes of data, our knowledge of the molecular mechanisms underlying complex genetic diseases remains limited. Increasing evidence shows that biological networks are essential, albeit not sufficient, for the better understanding of these mechanisms. The identification of disease-specific functional modules in the human interactome can provide a more focused insight into the mechanistic nature of the disease. However, carving a disease network module from the whole interactome is a difficult task. In this paper, we propose a computational framework, Discovering most IMpacted SUbnetworks in interactoMe (DIMSUM), which enables the integration of genome-wide association studies (GWAS) and functional effects of mutations into the protein-protein interaction (PPI) network to improve disease module detection. Specifically, our approach incorporates and propagates the functional impact of non-synonymous single nucleotide polymorphisms (nsSNPs) on PPIs to implicate the genes that are most likely influenced by the disruptive mutations, and to identify the module with the greatest functional impact. Comparison against state-of-the-art seed-based module detection methods shows that our approach could yield modules that are biologically more relevant and have stronger association with the studied disease. We expect for our method to become a part of the common toolbox for the disease module analysis, facilitating the discovery of new disease markers.

  View details for DOI 10.3390/genes10110933

  View details for Web of Science ID 000502296000099

  View details for PubMedID 31731769

  View details for PubMedCentralID PMC6895925

• Assessment of network module identification across complex diseases NATURE METHODS Choobdar, S., Ahsen, M. E., Crawford, J., Tomasoni, M., Fang, T., Lamparter, D., Lin, J., Hescott, B., Hu, X., Mercer, J., Natoli, T., Narayan, R., Aicheler, F., Amoroso, N., Arenas, A., Azhagesan, K., Baker, A., Banf, M., Batzoglou, S., Baudot, A., Bellotti, R., Bergmann, S., Boroevich, K. A., Brun, C., Cai, S., Caldera, M., Calderone, A., Cesareni, G., Chen, W., Chichester, C., Cowen, L., Cui, H., Phuong Dao, De Domenico, M., Dhroso, A., Didier, G., Divine, M., del Sol, A., Feng, X., Flores-Canales, J. C., Fortunato, S., Gitter, A., Gorska, A., Guan, Y., Guenoche, A., Gomez, S., Hamza, H., Hartmann, A., He, S., Heijs, A., Heinrich, J., Hu, Y., Huang, X., Hughitt, V., Jeon, M., Jeub, L., Johnson, N. T., Joo, K., Joung, I., Jung, S., Kalko, S. G., Kamola, P. J., Kang, J., Kaveelerdpotjana, B., Kim, M., Kim, Y., Kohlbacher, O., Korkin, D., Krzysztof, K., Kunji, K., Kutalik, Z., Lage, K., Lang-Brown, S., Thuc Duy Le, Lee, J., Lee, S., Lee, J., Li, D., Li, J., Liu, L., Loizou, A., Luo, Z., Lysenko, A., Ma, T., Mall, R., Marbach, D., Mattia, T., Medvedovic, M., Menche, J., Micarelli, E., Monaco, A., Mueller, F., Narykov, O., Norman, T., Park, S., Perfetto, L., Perrin, D., Pirro, S., Przytycka, T. M., Qian, X., Raman, K., Ramazzotti, D., Ramsahai, E., Ravindran, B., Rennert, P., Saez-Rodriguez, J., Scharfe, C., Sharan, R., Shi, N., Shin, W., Shu, H., Sinha, H., Slonim, D. K., Spinelli, L., Srinivasan, S., Subramanian, A., Suver, C., Szklarczyk, D., Tangaro, S., Thiagarajan, S., Tichit, L., Tiede, T., Tripathi, B., Tsherniak, A., Tsunoda, T., Turei, D., Ullah, E., Vahedi, G., Valdeolivas, A., Vivek, J., von Mering, C., Waagmeester, A., Wang, B., Wang, Y., Weir, B. A., White, S., Winkler, S., Xu, K., Xu, T., Yan, C., Yang, L., Yu, K., Yu, X., Zaffaroni, G., Zaslavskiy, M., Zeng, T., Zhang, J. D., Zhang, L., Zhang, W., Zhang, L., Zhang, X., Zhang, J., Zhou, X., Zhou, J., Zhu, H., Zhu, J., Zuccon, G., Stolovitzky, G., Kutalik, Z., Lage, K., Slonim, D. K., Saez-Rodriguez, J., Cowen, L. J., Bergmann, S., Marbach, D., DREAM Module Identification Ch 2019; 16 (9): 843-+

  Abstract

  Many bioinformatics methods have been proposed for reducing the complexity of large gene or protein networks into relevant subnetworks or modules. Yet, how such methods compare to each other in terms of their ability to identify disease-relevant modules in different types of network remains poorly understood. We launched the 'Disease Module Identification DREAM Challenge', an open competition to comprehensively assess module identification methods across diverse protein-protein interaction, signaling, gene co-expression, homology and cancer-gene networks. Predicted network modules were tested for association with complex traits and diseases using a unique collection of 180 genome-wide association studies. Our robust assessment of 75 module identification methods reveals top-performing algorithms, which recover complementary trait-associated modules. We find that most of these modules correspond to core disease-relevant pathways, which often comprise therapeutic targets. This community challenge establishes biologically interpretable benchmarks, tools and guidelines for molecular network analysis to study human disease biology.

  View details for DOI 10.1038/s41592-019-0509-5

  View details for Web of Science ID 000484044700022

  View details for PubMedID 31471613

  View details for PubMedCentralID PMC6719725