Javad Rahimikollu

SLIDE: Statistical Learning for Interpretable Disease Endotypes

Rahimikollu J et al. (2024)

Modern multiomic technologies can generate deep multiscale profiles. However, differences in data modalities, multicollinearity of the data, and large numbers of irrelevant features make analyses and integration of high-dimensional omic datasets challenging. Here we present Significant Latent Factor Interaction Discovery and Exploration (SLIDE), a first-in-class interpretable machine learning technique for identifying significant interacting latent factors underlying outcomes of interest from high-dimensional omic datasets. SLIDE makes no assumptions regarding data-generating mechanisms, comes with theoretical guarantees regarding identifiability of the latent factors/corresponding inference, and has rigorous false discovery rate control. Using SLIDE on single-cell and spatial omic datasets, we uncovered significant interacting latent factors underlying a range of molecular, cellular and organismal phenotypes. SLIDE outperforms/performs at least as well as a wide range of state-of-the-art approaches, including other latent factor approaches. More importantly, it provides biological inference beyond prediction that other methods do not afford. Thus, SLIDE is a versatile engine for biological discovery from modern multiomic datasets.

Deep humoral profiling coupled to interpretable machine learning unveils diagnostic markers and pathophysiology of schistosomiasis

Anushka Saha, Trirupa chakraborty, Rahimikollu J et al. (2024)

Schistosomiasis, a highly prevalent parasitic disease, affects more than 200 million people worldwide. Current diagnostics based on parasite egg detection in stool detect infection only at a late stage, and current antibody-based tests cannot distinguish past from current infection. Here, we developed and used a multiplexed antibody profiling platform to obtain a comprehensive repertoire of antihelminth humoral profiles including isotype, subclass, Fc receptor (FcR) binding, and glycosylation profiles of antigen-specific antibodies. Using Essential Regression (ER) and SLIDE, interpretable machine learning methods, we identified latent factors (context-specific groups) that move beyond biomarkers and provide insights into the pathophysiology of different stages of schistosome infection. By comparing profiles of infected and healthy individuals, we identified modules with unique humoral signatures of active disease, including hallmark signatures of parasitic infection such as elevated immunoglobulin G4 (IgG4). However, we also captured previously uncharacterized humoral responses including elevated FcR binding and specific antibody glycoforms in patients with active infection, helping distinguish them from those without active infection but with equivalent antibody titers. This signature was validated in an independent cohort. Our approach also uncovered two distinct endotypes, nonpatent infection and prior infection, in those who were not actively infected. Higher amounts of IgG1 and FcR1/FcR3A binding were also found to be likely protective of the transition from nonpatent to active infection. Overall, we unveiled markers for antibody-based diagnostics and latent factors underlying the pathogenesis of schistosome infection. Our results suggest that selective antigen targeting could be useful in early detection, thus controlling infection severity.

Rahimikollu J, Das J. (2022)

Review of methodologies for integrating diverse biomedical data types. This paper provides a comprehensive overview of current approaches and future directions in multi-omics integration.

Multi-modal integration of protein interactomes with genomic and molecular data discovers distinct RA endotypes

Rahimikollu J et al. (2025)

Preprint posted August 5, 2025; manuscript in revision at Arthritis and Rheumatology.

DataRemix: A universal data transformation for optimal inference from gene expression datasets

Mao W, Rahimikollu J et al. (2021)

RNA-seq technology provides unprecedented power in the assessment of the transcription abundance and can be used to perform a variety of downstream tasks such as inference of gene-correlation network and eQTL discovery.

Predictors of patency for arteriovenous fistulae and grafts in pediatric hemodialysis patients

Onder AM, ..., Rahimikollu J et al. (2019)

Identification of key factors in disease progression using advanced statistical methods. This work was conducted in collaboration with the midwest pediatric nephrology consortium.

Predictors of time to first cannulation for arteriovenous fistula in pediatric hemodialysis patients

Onder AM, ..., Rahimikollu J et al. (2020)

Midwest Pediatric Nephrology Consortium study on identifying predictive factors for arteriovenous fistula cannulation timing.

Towards a cleaner future: A novel approach to enhance energy efficiency in the US manufacturing industry with fuzzy logic-TOPSIS

Abolhassani A, Rahimikollu J, Gopalakrishnan B (2025)

Novel approach to enhance energy efficiency in the US manufacturing industry using fuzzy logic and TOPSIS methodology.

Hydroclimatic sustainability assessment of changing climate on cholera in the Ganges-Brahmaputra basin

Nasr-Azadani F et al., Rahimikollu J et al. (2017)

Assessment of climate change impacts on cholera prevalence in the Ganges-Brahmaputra basin using hydroclimatic modeling.

Comparison of first aggregation and last aggregation in fuzzy group TOPSIS

Roghanian E, Rahimi J, Ansari A (2010)

Comparative analysis of aggregation methods in fuzzy group decision-making using TOPSIS methodology.