Raghvendra Mall

Raghvendra Mall
St. Jude Children's Research Hospital · Department of Immunology

Computational Biology: Applied Machine Learning, Big Data
In hot pursuit of translational research

About

130
Publications
19,190
Reads
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1,599
Citations
Citations since 2016
92 Research Items
1431 Citations
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Introduction
Life is all about learning and research. I am experienced machine learning researcher with expertise in Data Sciences, Systems Biology, Complex Networks, and Computational Immunology (7+ years post-Phd) -- working on multi-omics driven biological insights to understand disease vagaries, identify therapeutic targets, and translate to clinical practice. I specialize in scientific team leadership and cross-cultural collaborations, with experience managing international research projects.
Additional affiliations
January 2016 - June 2021
Qatar Computing Research Institute
Position
  • Researcher
Description
  • Develop methods for computational biology primarily focusing on network biology in cancer.
February 2015 - present
KU Leuven
Position
  • Research Assistant
Description
  • Artifical Neural Networks Course. I will be giving lab sessions and will be correcting the examination papers of students for this course
February 2014 - June 2014
KU Leuven
Position
  • Research Assistant
Description
  • Artifical Neural Networks Course. I was giving lab sessions and was correcting the examination papers of students for this course
Education
April 2012 - July 2015
KU Leuven
Field of study
  • Sparsity in Large Scale Machine Learning

Publications

Publications (130)
Article
Full-text available
Chromosomal translocations that generate in-frame oncogenic gene fusions are notable examples of the success of targeted cancer therapies. We have previously described gene fusions of FGFR3-TACC3 (F3-T3) in 3% of human glioblastoma cases. Subsequent studies have reported similar frequencies of F3-T3 in many other cancers, indicating that F3-T3 is a...
Article
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We propose a generic framework for gene regulatory network (GRN) inference approached as a feature selection problem. GRNs obtained using Machine Learning techniques are often dense, whereas real GRNs are rather sparse. We use a Tikonov regularization inspired optimal L-curve criterion that utilizes the edge weight distribution for a given target g...
Article
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1 Motivation Biological networks contribute effectively to unveil the complex structure of molecular interactions and to discover driver genes especially in cancer context. It can happen that due to gene mutations, as for example when cancer progresses, the gene expression network undergoes some amount of localised re-wiring. The ability to detect...
Article
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Least Squares Support Vector Machines (LSSVM) have been widely applied for classification and regression with comparable performance to SVMs. The LSSVM model lacks sparsity and is unable to handle large scale data due to compu-tational and memory constraints. A primal Fixed-Size LSSVM (PFS-LSSVM) was previously proposed in [1] to introduce sparsity...
Article
Motivation Protein 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. Me...
Article
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Resistance to programmed cell death (PCD) is a hallmark of cancer. While some PCD components are prognostic in cancer, the roles of many molecules can be masked by redundancies and crosstalks between PCD pathways, impeding the development of targeted therapeutics. Recent studies characterizing these redundancies have identified PANoptosis, a unique...
Article
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Non-alcoholic fatty liver disease (NAFLD) is a common liver lesion that is untreatable with medications. Glucagon-like peptide-1 receptor (GLP-1R) agonists have recently emerged as a potential NAFLD pharmacotherapy. However, the molecular mechanisms underlying these drugs’ beneficial effects are not fully understood. Using Fourier transform infrare...
Article
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Type I interferons (IFNs) are essential innate immune proteins that maintain tissue homeostasis through tonic expression and can be upregulated to drive antiviral resistance and inflammation upon stimulation. However, the mechanisms that inhibit aberrant IFN upregulation in homeostasis and the impacts of tonic IFN production on health and disease r...
Article
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Background Advances in our understanding of the tumor microenvironment have radically changed the cancer field, highlighting the emerging need for biomarkers of an active, favorable tumor immune phenotype to aid treatment stratification and clinical prognostication. Numerous immune-related gene signatures have been defined; however, their prognosti...
Article
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Background: Obesity coexists with variable features of metabolic syndrome, which is associated with dysregulated metabolic pathways. We assessed potential associations between serum metabolites and features of metabolic syndrome in Arabic subjects with obesity. Methods: We analyzed a dataset of 39 subjects with obesity only (OBO, n = 18) age-mat...
Article
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Background Obesity-associated dysglycemia is associated with metabolic disorders. MicroRNAs (miRNAs) are known regulators of metabolic homeostasis. We aimed to assess the relationship of circulating miRNAs with clinical features in obese Qatari individuals. Methods We analyzed a dataset of 39 age-matched patients that includes 18 subjects with obe...
Article
Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), the virus responsible for coronavirus disease 2019 (COVID-19), continues to cause significant morbidity and mortality in the ongoing global pandemic. Understanding the fundamental mechanisms that govern innate immune and inflammatory responses during SARS-CoV-2 infection is critical for...
Article
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Breast cancer largely dominates the global cancer burden statistics; however, there are striking disparities in mortality rates across countries. While socioeconomic factors contribute to population-based differences in mortality, they do not fully explain disparity among women of African ancestry (AA) and Arab ancestry (ArA) compared to women of E...
Article
We recently found by single-cell mass cytometry that ex vivo human B cells internalize graphene oxide (GO). The functional impact of such uptake on B cells remains unexplored. Here, we disclosed the effects of GO and amino-functionalized GO (GONH2) interacting with human B cells in vitro and ex vivo at the protein and gene expression levels. Moreov...
Article
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In tumor-bearing mice, cyclic fasting or fasting-mimicking diets (FMD) enhance the activity of antineoplastic treatments by modulating systemic metabolism and boosting antitumor immunity. Here we conducted a clinical trial to investigate the safety and biological effects of cyclic, five-day FMD in combination with standard antitumor therapies. In 1...
Article
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Traumatic brain injury (TBI) can be defined as a disorder in the function of the brain after a bump, blow, or jolt to the head, or penetrating head injury. Mild traumatic brain injury (mTBI) can cause devastating effects, such as the initiation of long-term neurodegeneration in brain tissue. In the current study, the effects of mTBI were investigat...
Article
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A cancer immune phenotype characterized by an active T-helper 1 (Th1)/cytotoxic response is associated with responsiveness to immunotherapy and favorable prognosis across different tumors. However, in some cancers, such an intratumoral immune activation does not confer protection from progression or relapse. Defining mechanisms associated with immu...
Preprint
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Poor sleep quality has been linked to several chronic, psychiatric, and neurological conditions that can burden the public health system. This paper aims to develop methods to assess sleep quality from physical activity data collected from wearable devices. Our methods can be deployed on a large scale and used by recommender systems to guide the pu...
Preprint
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We introduce Permutation and Structured Perturbation Inference (PSPI), a new problem formulation that abstracts many graph matching tasks that arise in systems biology. PSPI can be viewed as a robust formulation of the permutation inference or graph matching, where the objective is to find a permutation between two graphs under the assumption that...
Article
Motivation: A global effort is underway to identify compounds for the treatment of COVID-19. Since de novo compound design is an extremely long, time-consuming, and expensive process, efforts are underway to discover existing compounds that can be repurposed for COVID-19 and new viral diseases. Model: We propose a machine learning representation...
Article
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Mixing cations has been a successful strategy in perovskite synthesis by solution-processing, delivering improvements in the thermodynamic stability as well as in the lattice parameter control. Unfortunately, the relation between a given cation mixture and the associated structural deformation is not well-established, a fact that hinders an adequat...
Preprint
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Motivation: A global effort is underway to identify drugs for the treatment of COVID-19. Since de novo drug design is an extremely long, time-consuming, and expensive process, efforts are underway to discover existing drugs that can be repurposed for COVID-19. Model: We propose a machine learning representation framework that uses deep learning-i...
Article
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In recent years, there has been a significant expansion in the development and use of multi-modal sensors and technologies to monitor physical activity, sleep and circadian rhythms. These developments make accurate sleep monitoring at scale a possibility for the first time. Vast amounts of multi-sensor data are being generated with potential applic...
Article
Given the surging growth of artificial-intelligence-inspired computational methods in materials science, experimental laboratories around the globe have become open to adopting data-driven approaches for materials discovery. The field witnesses emerging machine-learning models trained over databases, of which data are collected from high-throughput...
Article
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Aims The progression from prediabetes to type 2 diabetes is preventable by lifestyle intervention and/or pharmacotherapy in a large fraction of individuals with prediabetes. Our objective was to develop a risk score to screen for prediabetes in Middle East, where diabetes prevalence is one of the highest in the world. Materials and methods In this...
Article
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Objective To develop a machine-based algorithm from clinical and demographic data, physical activity and glucose variability to predict hyperglycaemic and hypoglycaemic excursions in patients with type 2 diabetes on multiple glucose lowering therapies who fast during Ramadan. Patients and Methods Thirteen patients (10 males and three females) with...
Preprint
div>Motivation: A global effort is underway to identify drugs for the treatment of COVID-19. Since de novo drug design is an extremely long, time-consuming, and expensive process, efforts are underway to discover existing drugs that can be repurposed for COVID-19. Model: We propose a machine learning representation framework that uses deep learni...
Cover Page
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About the Cover: Guided by machine learning predictors and with minimal laboratory synthesis trial-and-error, mixed cation compositions of lead halide perovskites that recover the cubic structure at room temperature are revealed. http://dx.doi.org/10.1021/acs.chemmater.9b05342
Article
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Background An immune active cancer phenotype typified by a T helper 1 (Th-1) immune response has been associated with increased responsiveness to immunotherapy and favorable prognosis in some but not all cancer types. The reason of this differential prognostic connotation remains unknown. Methods To explore the contextual prognostic value of cance...
Article
Data-driven approaches for materials design and selection have accelerated materials discovery along with the upsurge of machine learning applications. We report here a prediction-to-lab-scale synthesis of cubic phase triple-cation lead halide perovskites guided by a machine-learning perovskite stability predictor. The starting double-cation perovs...
Article
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Amyloid fibrillation is the root cause of several neuro as well as non-neurological disorders. Understanding the molecular basis of amyloid aggregate formation is crucial for deciphering various neurodegenerative diseases. In our study, we have examined the lysozyme fibrillation process using nano-infrared spectroscopy (nanoIR). NanoIR enabled us t...
Article
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Broadly neutralizing antibodies (bNAbs) targeting the HIV-1 envelope glycoprotein (Env) have promising utility in prevention and treatment of HIV-1 infection, and several are currently undergoing clinical trials. Due to the high sequence diversity and mutation rate of HIV-1, viral isolates are often resistant to specific bNAbs. Currently, resistant...
Article
Motivation: X-ray crystallography has facilitated the majority of protein structures determined to date. Sequence-based predictors that can accurately estimate protein crystallization propensities would be highly beneficial to overcome the high expenditure, large attrition rate, and to reduce the trial-and-error settings required for crystallizati...
Article
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Interactions between programmed death-1 (PD-1) with its ligand PD-L1 on tumor cells can antagonize T cell responses. Inhibiting these interactions using immune checkpoint inhibitors has shown promise in cancer immunotherapy. MDA-MB-231 is a triple negative breast cancer cell line that expresses PD-L1. In this study, we investigated the biochemical...
Article
Forecasting the structural stability of hybrid organic/inorganic compounds, where polyatomic molecules replace atoms, is a challenging task; the composition space is vast and the reference structure for the organic molecules is ambiguously defined. In this work we use a range of machine-learning algorithms, constructed from state-of-the-art density...
Article
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Accurately measuring sleep and its quality with polysomnography (PSG) is an expensive task. Actigraphy, an alternative, has been proven cheap and relatively accurate. However, the largest experiments conducted to date, have had only hundreds of participants. In this work, we processed the data of the recently published Multi-Ethnic Study of Atheros...
Preprint
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Motivation: Protein solubility is a property associated with protein expression and is a critical determinant of the manufacturability of therapeutic proteins. It is thus imperative to design accurate in-silico sequence-based solubility predictors. Methods: In this study, we propose SolXplain, an extreme gradient boosting machine based protein solu...
Preprint
Full-text available
Background It is becoming clear that tumor immune T cell infiltration and its functional orientation have substantial effect on cancer progression, influencing both response to therapy and prognosis. In this pan-cancer study, the previously described Immunologic Constant of Rejection (ICR) signature is used to define opposing immune phenotypes (i.e...
Article
Correction for ‘Exploring new approaches towards the formability of mixed-ion perovskites by DFT and machine learning’ by Heesoo Park et al. , Phys. Chem. Chem. Phys. , 2019, DOI: 10.1039/c8cp06528d.
Preprint
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Identification of modules in molecular networks is at the core of many current analysis methods in biomedical research. However, how well different approaches identify disease-relevant modules in different types of gene and protein networks remains poorly understood. We launched the “Disease Module Identification DREAM Challenge”, an open competiti...
Article
Recent years have witnessed a growing effort in engineering and tuning the properties of hybrid halide perovskites as light absorbers. These have led to the successful enhancement of their stability, a feature that is often counterbalanced by a reduction of their power-conversion efficiency. In order to provide a systematic analysis of the structur...
Article
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Genotype imputation is widely used in genome-wide association studies to boost variant density, allowing increased power in association testing. Many studies currently include pedigree data due to increasing interest in rare variants coupled with the availability of appropriate analysis tools. The performance of population-based (subjects are unrel...
Article
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Fourier Transform Infrared Spectroscopy (FTIR) is a non-destructive analytical technique that has been employed in this research to characterize the biochemical make-up of various rat brain regions. The sensorimotor cortex, caudate putamen, thalamus, and the hippocampus were found to have higher olefinic content—an indicator of a higher degree of u...
Article
Motivation: Protein structure determination has primarily been performed using X-ray crystallography. To overcome the expensive cost, high attrition rate and series of trial-and-error settings, many in-silico methods have been developed to predict crystallization propensities of proteins based on their sequences. However, the majority of these meth...
Article
Full-text available
Following publication of the original article [1], the authors reported that one of the authors’ names was processed incorrectly. In this Correction the incorrect and correct author name are shown. The original publication of this article has been corrected.
Article
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Objective: Stroke is the main cause of adult disability in the world, leaving more than half of the patients dependent on daily assistance. Understanding the post-stroke biochemical and molecular changes are critical for patient survival and stroke management. The aim of this work was to investigate the photo-thrombotic ischemic stroke in male rats...
Data
The representative FTIR spectrum of white matter (WM) in a native control rat brain in the spectral range of 4000–700 cm-1 and mechanical properties of the native and lesioned brain sections. Representative FTIR spectra of WM in the native control rat brain from region 4000–700 cm-1.
Data
Fourier transform Infrared (FTIR) spectral bands and their corresponding bio-chemical assignment.
Preprint
Broadly neutralizing antibodies (bNAbs) targeting the HIV-1 envelope glycoprotein (Env) have promising utility in prevention and treatment of HIV-1 infection with several undergoing clinical trials. Due to high sequence diversity and mutation rate of HIV-1, viral isolates are often resistant to particular bNAbs. Resistant strains are commonly ident...
Data
This file describes the steps used in RGBM Framework for reverse-engineering GRN and using FGSEA along with differential expression of targets to identify the key master regulators. We then visualize the activity of the master regulators in the case vs the controls.