Computational Biology Commons^™

Mitochondrial Trna-Derived Fragments As Candidate Metastasis-Modifying Rna, Katy L. Swancutt, R. Mckinnon Walsh, Sydney Quijano, Emily Schueddig, Devin C. Koestler, Adam D. Scheid, Tony Vanden Bush, Yi Jing, Isidore Rigoutsos, Danny R. Welch

Computational Medicine Center Faculty Papers

UNLABELLED: How mitochondrial DNA (mtDNA) polymorphisms influence complex phenotypes remains poorly understood. Using mitochondrial-nuclear exchange mice, we previously showed that mtDNA single-nucleotide polymorphisms (SNP) modify metastasis, cardiovascular disease, and epigenetic marks independently of metabolic differences. The only mtDNA SNP correlating with these phenotypes resides in the gene encoding mitochondrial transfer RNA (tRNA)-arginine [mt-tRNAArg (UCG), mt-TR], suggesting a role for non-protein-coding loci. In this study, we identify and preliminarily characterize previously undescribed tRNA-derived fragments (tRF) generated from mt-TRs. Northern blotting revealed distinct tRF that are differentially expressed among mtDNA SNPs, between lung and liver, and between sexes. Surprisingly, small RNA sequencing …

Integrative Rna-Seq Analysis Reveals Stress Type-Dependent Lncrna-Centered Co-Expression Networks Across Human Cellular Stress Contexts, Christina Anastasiadi, Aggeliki Kasapi, Ioannis Sentementes, Vasileios Gouzouasis, Margaritis Tsifintaris, Antonis Giannakakis

Computational Medicine Center Faculty Papers

Long non-coding RNAs (lncRNAs) are emerging as important regulators of cellular adaptation to environmental and molecular stress, but the extent to which their responses remain reproducible and stress-type-dependent across human stress conditions remains unclear. Here, we performed an integrative transcriptomic meta-analysis of human stress-response datasets from ASTRA and GEO, focusing on normal, non-cancerous, wild-type human cell lines exposed to oxidative stress (H2O2), hypoxia, heat stress, or UV-induced DNA damage. Gene Ontology (GO) enrichment analysis of differentially expressed (DE) protein-coding mRNAs confirmed that the resulting stress-stratified comparison captured biologically coherent transcriptional programmes to oxidative stress signaling, hypoxic and metabolic adaptation, heat-induced …

Fraction-Seq: An Integrated Experimental And Computational Workflow For Determining The Localization And Abundance Of Small Non-Coding Rnas In Subcellular Compartments, Siddhartha Shah, Tess Cherlin, Yi Jing, Stepan Nersisyan, Benjamin E. Leiby, Isidore Rigoutsos

Computational Medicine Center Faculty Papers

Small non-coding RNAs (sncRNAs) have garnered considerable attention in recent years, following accumulating evidence of their critical roles in many cellular processes. Among sncRNAs, microRNAs (miRNAs) and their isoforms (isomiRs), tRNA-derived fragments (tRFs), rRNA-derived fragments (rRFs), and Y RNA-derived fragments (yRFs) account for more than 95% of all sncRNAs found in cells. Despite their critical regulatory roles, most sncRNAs remain uncharacterized because their functionalization is a lengthy and challenging undertaking. Knowing an sncRNA's abundance helps prioritize among the various choices, while knowing where it localizes in the cell greatly limits the number and identity of its potential targets and helps …

Regulation Of Müllerian Duct Mesenchyme Transcription During Mammalian Sex Differentiation, Haowen Li, Richard R Behringer, Rachel D Mullen

Dissertations and Theses (Open Access)

Sp7/Osterix (Osx) encodes a zinc-finger transcription factor of the Specificity-protein family discovered by Nakashima et al. at the MD Anderson Cancer Center. While primarily recognized for its role in osteogenesis, Osx has also been implicated in mammalian reproductive development, particularly in male sex differentiation, where Müllerian Duct (MD) regression occurs, mediated by anti-Müllerian hormone (AMH) signaling. AMH-induced regression signals are transduced by the mesenchymal tissue surrounding the ductal structure, known as the Müllerian Duct mesenchyme (MDM). It was discovered that AMH signaling is necessary and sufficient for driving Osx expression in MDM. A previous transgenic mouse reporter …

A Disorder-Aware Computational Framework To Identify Structurally Tractable Targets In Proliferative Vitreoretinopathy, Mak B. Djulbegovic, Nedym Hadzijahic, David J. Taylor Gonzalez, Michael Antonietti, Sidra Zafar, Ajay E. Kuriyan

Wills Eye Hospital Papers

OBJECTIVE: Proliferative vitreoretinopathy (PVR) remains a major cause of failure after rhegmatogenous retinal detachment repair and lacks effective pharmacologic therapies. Although epithelial-mesenchymal transition (EMT) is central to PVR pathogenesis, the structural determinants governing the tractability of EMT regulators, particularly those involving intrinsic disorder, remain poorly defined. We developed a disorder-aware, artificial intelligence-enabled computational framework to evaluate EMT-associated proteins in PVR and prioritize structurally tractable regulators for structure-based targeting.

DESIGN: A computational, hypothesis-generating study employing an in silico screening and structural modeling pipeline.

SUBJECTS: No human subjects or biological specimens were included. The dataset comprised 25 EMT-associated proteins implicated in PVR, …

Detecting Cancer Genes Using Graph Neural Networks, Marvin Masabo Nkaka

Posters - 2026

• Cancer survival prediction is challenging due to the complexity of genomic data and limited samples especially for rarer cancer types. • To address this challenge, we developed an Artificial Neural Network (ANN) model for survival analysis using RNA-sequencing gene expression data from The Cancer Genome Atlas (TCGA). • Moreover, a key concept we investigate was how transfer learning enhanced our model’s performance especially for rarer cancer types difficult to perform accurate survival analysis due to their limited samples.

Auditory Stimulation Rescues Cognitive Deficit In Fmr1-Ko Mice, Mohamed Ouardouz, Amanda E. Hernan, J. Matthew Mahoney, Rodney C. Scott

Department of Medicine Faculty Papers

Background/Objectives: Fragile X Syndrome (FXS) is a neurodevelopmental disorder caused by a triplet repeat expansion in the Fmr1 gene leading to the loss of Fragile X Messenger Ribonucleoprotein (Fmr1 protein). The loss of Fmr1 protein modulates many cell biological processes and leads to the emergence of intellectual disability and autism. FXS is modeled in Fmr1-KO mice that display features consistent with human FXS, including hypersensitivity, cognitive and learning deficits, hyperactivity and audiogenic seizures. Here, we investigated the effect of auditory stimulation during a range of developmental stages on recognition memory and sociability deficits in Fmr1-KO mice. Methods: Fmr1-KO mice were …

Metabolic Syndrome Beyond Diagnostic Criteria: Population-Scale Integrative Metabolomics Characterization, Marwa Talal

Theses and Dissertations

Background: Metabolic syndrome (MetS) is a complex cluster of interrelated metabolic abnormalities associated with elevated cardiometabolic risk. While diagnosis is based on well-established five clinical criteria, these may overlook early or atypical metabolic alterations. Large-scale metabolomic profiling offers an opportunity to identify biochemical signatures of MetS beyond diagnostic bias and to evaluate their relative importance across different presentations of the syndrome.

Methods: Data from 117,147 UK Biobank participants were analyzed in a cross-sectional design. High-throughput NMR quantified 75 circulating metabolites, for. Univariate analyses, MetS subtype stratification, and elastic net models with SHAP interpretation were applied to assess feature …

Privacy-Preserving Federated Learning With Optimized Ensemble Weighting And Knowledge Distillation For Covid-19 Detection From Non-Iid Medical Imaging Data, Richard Annan, Hong Qin, Robert Newman, Madhuri Siddula, Letu Qingge

Computer Science Faculty Publications

Medical imaging enables rapid and accurate diagnosis of COVID-19, with CT scans proving especially effective. However, data privacy concerns limit collaborative model development across hospitals. To address this issue, we introduce a novel federated learning framework. It is referred to as Independent Knowledge Distillation with post-Ensemble Federated Learning (IKDEFL). Differential Privacy (DP) is integrated into the framework to improve privacy guarantees. Three DP mechanisms are evaluated. These include Fixed Gaussian, Gaussian Adaptive, and Tree Adaptive. The evaluation has been conducted on heterogeneous and Non-Independent and Identically Distributed (Non-IID) datasets. These datasets reflect real-world hospital scenarios. Results show that IKDEFL significantly …

Recurrence And Co-Occurrence Of Enhancer-Promoter Interactions Across Human Samples, Satvik Gunjala

Honors Undergraduate Theses

Experimental mapping of enhancer-promoter interactions (EPIs) is resource-intensive, and current computational prediction methods struggle with intrinsic genomic complexity and reliance on limited training data. To address this bottleneck and provide insights for improved computational methods, this study systematically analyzed chromatin contact datasets to investigate the recurrence and co-occurrence of enhancer-promoter interactions across human samples. Putative interactions were evaluated across two HiChIP datasets comprising 218 total samples and one Hi-C dataset comprising 266 samples to assess recurrence across samples and assess sequencing depth related to unique EPIs. Additionally, a preliminary item-based collaborative filtering recommender model was developed to assess co-occurrence patterns …

Attention-Based Multi-Omics Fusion For Drug Synergy Prediction, Kusal Debnath, Pratip Rana, Preetam Ghosh

Computer Science Faculty Publications

Drug combination therapy in disease management gained popularity in the last few decades. Computational modeling of such combinations is an active area of research in the drug discovery domain. While earlier approaches solely emphasized on the structural features of participating drugs for designing synergistic models, they lack other crucial factors directly linked with drug administration - omics expressions. As differential omics expression is a downstream consequence of the administered drug combinations, utilizing such expressions while designing synergistic models promises robust and dynamic modeling. In this work, we propose SynergyLM that fuses multi-omics features with drug embeddings to build an omics-aware …

A Tissue Renewal-Based Mechanism Drives Colon Tumorigenesis, Ryan M. Boman, Gilberto Schleiniger, Christopher Raymond, Juan P. Palazzo, Anne Shehab, Bruce M. Boman

Department of Pathology, Anatomy, and Cell Biology Faculty Papers

Our Goal is to identify how colorectal cancer (CRC) arises in the single-layered cell epithelium (simple columnar epithelium) that lines the luminal surface of the large intestine. Background: We recently reported that the dynamic organization of cells in colonic epithelium is encoded by five biological rules and conjectured that colon tumorigenesis involves an autocatalytic tissue renewal reaction. Introduction Our objective was to define how altered crypt turnover explains tissue disorganization that leads to adenoma morphogenesis and CRC. Hypothesis: Changes in rate of tissue renewal-based cell polymerization leads to epithelial expansion and tissue disorganization during adenoma histogenesis. Methods: Accordingly, we created …

Organism-Specific Sequence Motifs Link Ribosomal Rnas To Brain Disorders, Isidore Rigoutsos, Stepan Nersisyan, Eric Londin, Iliza Nazeraj, Bonnie Dong, Anastasios Vourekas, Phillipe Loher

Computational Medicine Center Faculty Papers

We report that in humans, mice, fruit flies, and worms, the ribosomal RNAs and the transcribed spacers of 45S are densely packed with organism-specific sequence motifs that are primarily shared with nervous system genes. The human ribosomal RNAs and 45S spacers contain 1,723 such motifs. Specific combinations of these motifs are predominantly found in 3,430 human nervous system genes, of which 1,046 are genes associated with brain disorders, including autism spectrum disorder and schizophrenia. The sequences of the 1,723 motifs and their locations in the introns and exons of nervous system genes are unique to primates. Experimental evidence indicates that …

Dynamic Rewiring Of Microrna Networks In The Brainstem Autonomic Control Circuits During Hypertension Development In The Female Spontaneously Hypertensive Rat, Alison Moss, Ankita Srivastava, Lakshmi Kuttippurathu, James S. Schwaber, Rajanikanth Vadigepalli

Department of Pathology, Anatomy, and Cell Biology Faculty Papers

We describe global microRNA (miRNA) changes in the central autonomic control circuits during the development of neurogenic hypertension. Using the female spontaneously hypertensive rat (SHR) and the normotensive Wistar Kyoto (WKY), we analyzed the dynamic miRNA expression changes in three brainstem regions-the nucleus of the solitary tract, caudal ventrolateral medulla, and rostral ventrolateral medulla-as a time series beginning at 8 wk of age before hypertension onset through to extended chronic hypertension. Our analysis yielded nine miRNAs that were significantly differentially regulated in all three regions between SHR and WKY over time. We collated computationally predicted gene targets of these nine …

From Venom To Medicine: Harnessing Animal Toxins For Drug Discovery, Christine Vega, Ying Jia

Research Colloquium

Background: Drug development research has long focused on synthesizing novel therapeutics while overlooking isolation from naturally available sources such as animal venoms. Recently, the discovery of the therapeutic effects of highly bioavailable animal venom caused the field of venom research to soar in popularity. Animal venom serves as a natural, sophisticated tool optimized over millions of years by evolution with the ability to bind to ion channels such as nAChRs, non-specific ligand-gated ion channels distributed throughout the human nervous system. However, purifying individual venom toxins from crude venom is challenging due to its availability. Therefore, synthesizing large quantities of venom …

Resolution Of Physics And Deep Learning-Based Protein Engineering Filters: A Case Study With A Lipase For Industrial Substrate Hydrolysis, Spencer Gardiner, Peter Dollinger, Filip Kovacic, Jörge Pietruszka, Daniel Ess, Karl-Erich Jaeger, Gunnar F. Schröder, Dennis Della Corte

Faculty Publications

Computational enzyme design remains a powerful yet imperfect tool for optimizing biocatalysts, especially when targeting non-natural substrates. Using design tools we investigated Pseudomonas aeruginosa LipA, a lipase with a flexible lid domain crucial for substrate binding and turnover, aiming to enhance its hydrolysis of the industrially relevant substrate Roche ester. We generated an initial set of single-point mutations based on structural proximity to the active site and evaluated their effects using a computational pipeline integrating molecular dynamics (MD) simulations, density functional theory (DFT) calculations, and ensemble-based energy scoring. While we identified several active variants, attempts to rank them by activity …

Ai-Powered Multi-Omics Integration For Predictive Modeling Of Genotype-Environment-Phenotype Relationships, You Wu

Dissertations, Theses, and Capstone Projects

This dissertation presents a series of machine learning frameworks for modeling genotype–environment–phenotype relationships through integrative predictive modeling of multi-omics data. The work addresses three major axes of biological complexity: modeling biological information transmission cross-levels from genes to proteins to phenotypes, predicting molecular features cross-scale from cells to tissues to organisms, and translating phenotypes cross-species from model systems to humans. Each proposed method also tackles key machine learning (ML) challenges in the biomedical domain, including data scarcity, domain shift, out-of-distribution (OOD) generalization, and hierarchical modeling. Specifically, this dissertation introduces five novel deep learning algorithms: MultiDCP predicts drug-induced transcriptomic and viability responses …

The Swib Domain-Containing Dna Topoisomerase I Of Chlamydia Trachomatis Mediates Dna Relaxation, Li Shen, Abigail R. Swoboda, Caitlynn Diggs, Shomita Ferdous, Andrew Terrebonne, Amanda Santos, Noel Wolf, Luis Lorenzo Carvajal, Guangming Zhong, Scot P. Ouellette, Yuk Ching Tse-Dinh

School of Graduate Studies Faculty Publications

Chlamydia trachomatis has a DNA topoisomerase I with a unique C-terminal domain (CTD) homologous to eukaryotic SWIB domains. This study focused on determining the function of the SWIB domain-containing TopA from C. trachomatis (CtTopA). We demonstrated that, despite the lack of sequence similarity at the CTDs between CtTopA and TopA from Escherichia coli (EcTopA), full-length CtTopA removed negative DNA supercoils in vitro and complemented the growth defect of a topA mutant of E. coli. CtTopA is less processive in DNA relaxation than EcTopA in dose-response and time course studies. An antibody generated against the SWIB domain of CtTopA specifically recognized …

Quantile Index Predictors Using R Package Hyper.Gam, Tingting Zhan, Misung Yi, Inna Chervoneva

Department of Pharmacology, Physiology, and Cancer Biology Faculty Papers

MOTIVATION: Evaluation of single-cell protein expression from immunohistochemistry images is used increasingly in biomedical research. Many proteins are used solely for phenotyping cells in the tumor microenvironment. Other proteins with meaningfully quantitative expression levels provide so-called functional protein biomarkers. There is still a limited number of methods and software tools available for utilizing the entire distributions of single-cell expression levels.

RESULTS: We present the R package hyper.gam, providing a supervised learning framework for deriving biomarkers based on single-cell distribution quantiles. The single-cell data are first converted into sample quantile functions, which are then used as predictors in scalar-on-function regression models …

An Integrative Genomics Approach For The Discovery Of Potential Clinically Actionable Diagnostic And Prognostic Biomarkers In Colorectal Cancer, Mark Fertel, Duaa Mohammad Alawad, Chindo Hicks

School of Graduate Studies Faculty Publications

Background: Despite remarkable progress in clinical management of patients and intensified screening, colorectal cancer remains the second most common cause of cancer-related death in the United States. The recent surge of next generation sequencing has enabled genomic analysis of colorectal cancer genomes. However, to date, there is little information about leveraging gene expression data and integrating it with somatic mutation information to discover potential biomarkers and therapeutic targets. Here, we integrated gene expression data with somatic mutation information to discover potential diagnostic and prognostic biomarkers and molecular drivers of colorectal cancer. Methods: We used publicly available gene expression and somatic …

Cazyme Gene Cluster Diversity In Human Gut Microbiome, Yi Xing

Department of Food Science and Technology: Dissertations, Theses, and Student Research

In gut microbiome research, carbohydrate-active enzyme gene clusters (CGCs) have emerged as key functional units for understanding microbial glycan degradation. Unlike taxonomic or broad pathway annotations, CGCs offer gene-cluster-level resolution and capture substrate-specific microbial functions. However, their diversity and distribution in relation to host metabolic phenotypes, such as obesity, remain poorly characterized. This study tests the hypothesis that the composition and abundance of fiber-targeting CGCs vary between obese and healthy human gut microbiomes, reflecting distinct microbial carbohydrate utilization strategies. To examine this, we constructed a high-quality reference CGC dataset comprising 94,019 clusters from the Unified Human Gastrointestinal Genome and profiled …

Topodino: Self-Supervised Topological Representation Learning For Neuronal Morphologies, Yasser Binbisher

Master's Theses

Neuronal cell types are categorized by transcriptomic identity, yet their morphological heterogeneity defies this classification. In response, researchers have adopted unsupervised graph representation learning as a tool to reveal morphological variation within single-class transcriptomic types. However, the complex geometry of neuronal morphology—especially long axons and dense dendrites—challenges graph neural networks, which struggle with message propagation across extended structures. To mitigate this, current approaches enforce sub-sampling on neuronal graphs and omit axons entirely, sacrificing critical biological features for computational efficiency. To overcome this trade-off, this thesis introduces TopoDINO, a self-supervised, topology-aware representation learning model designed to preserve the full hierarchical organization …

Integrating Radiogenomics And Machine Learning In Musculoskeletal Oncology Care, Rahul Kumar, Kyle Sporn, Akshay Khanna, Phani Paladugu, Chirag Gowda, Alex Ngo, Ram Jagadeesan, Nasif Zaman, Alireza Tavakkoli

Department of Medicine Faculty Papers

Musculoskeletal tumors present a diagnostic challenge due to their rarity, histological diversity, and overlapping imaging features. Accurate characterization is essential for effective treatment planning and prognosis, yet current diagnostic workflows rely heavily on invasive biopsy and subjective radiologic interpretation. This review explores the evolving role of radiogenomics and machine learning in improving diagnostic accuracy for bone and soft tissue tumors. We examine integrating quantitative imaging features from MRI, CT, and PET with genomic and transcriptomic data to enable non-invasive tumor profiling. AI-powered platforms employing convolutional neural networks (CNNs) and radiomic texture analysis show promising results in tumor grading, subtype differentiation …

Developing A Small Molecule To Inhibit Hsf1 Expression In Cancer And Evaluating Natural Genetic Variation In Small Molecule Toxicity., Michaela Kendal Foley

Theses and Dissertations

Each year cancer affects nearly 20 million people worldwide and genetic differences across populations can impact cancer onset and progression. Specifically, tumors with high levels of HSF1, the master regulator of the cytoprotective heat shock response (HSR), are correlated with poor patient outcomes in multiple cancers such as prostate, breast, and melanoma. Subsequently, the development of pharmacological inhibitors of HSF1 represents a promising strategy for anticancer therapeutics. Using a luciferase-based transcriptional reporter, two small molecule libraries were screened for inhibitors of HSF1 expression in human embryonic kidney cells, yielding ten compounds that decrease HSF1 expression. To identify if cancer lines …

Elucidating The Multi-Omics Of Early-Onset Colorectal Cancer, Jumanah Alshenaifi

Dissertations and Theses (Open Access)

The incidence and mortality rates of sporadic early-onset colorectal cancer have increased in recent decades, but there is no clear etiological basis for this trend. EOCRC is commonly defined as colon and rectal cancers diagnosed before the age of 50 years. The rising incidence of EOCRC has made it the second most common cancer and the third leading cause of cancer death in this age group. The rising incidence of EOCRC is also documented internationally in more than 20 countries across different continents. Clinically, EOCRC has a distinct, more aggressive clinical profile than LOCRC. While approximately 15% of EOCRC cases …

Hierarchical Lineage Tracing To Unravel Mechanisms Of Cancer Treatment Resistance, Rachel Danielle Saxe

Dartmouth College Ph.D Dissertations

Cancer cells adapt to treatment, leading to the emergence of clones that are more aggressive and resistant to anti-cancer therapies. We have a limited understanding of the development of treatment resistance as we lack technologies to map the evolution of cancer under the selective pressure of treatment. To address this, we developed a hierarchical, dynamic lineage tracing method called FLARE (Following Lineage Adaptation and Resistance Evolution). We use this technique to track the progression of acute myeloid leukemia (AML) cell lines through exposure to Cytarabine (AraC), a front-line treatment in AML, in vitro and in vivo. We map distinct cellular …

Analysis Of Chromatin Accessibility Changes In Endothelial Cells Exposed To Plastic Contaminants, Mikhail Y. Salnikov, Carly Boye, David B. Witonsky, Gabrielle Garlicki, Adnan Alazizi, Francesca Luca, Roger Pique-Regi

Medical Student Research Symposium

Degradation products from everyday plastic products are known to bioaccumulate and have also been shown to contaminate drinking water and food sources. BPA and phthalates are endocrine disrupting chemicals and plastic components that have previously been associated with endothelial cell dysfunction, atherosclerotic and other adverse cardiovascular events. However, there is a limited understanding of the mechanisms underlying these associations, such as genome-wide chromatin accessibility changes in endothelial cells exposed to these compounds. The purpose of this study is to explore genome-wide changes in chromatin accessibility associated with plastic exposure, as well as the discovery of transcription factor binding motifs dysregulated …

Screening For Penicillin G Acylase (Pga)-Producing Bacteria And Gene Cloning Using Degenerate Oligonucleotide Primed-Pcr, Masdalifah Masdalifah, Sri Rezeki Wulandari, Gabriela Christy Sabbathini, Maria Ulfah, Dini Achnafani, Ahmad Wibisana, Feronika Heppy Sriherfyna, Is Helianti, Niknik Nurhayati

Makara Journal of Science

The growing concern over antibiotic resistance has driven global efforts to explore innovative solutions, including the use of Penicillin G acylase (PGA) to produce semisynthetic β-lactam antibiotics. This study screened four potential in-tracellular PGA-producing bacteria: Alcaligenes faecalis InaCC B444 (AfPGA), Kluyvera cryocrescens InaCC B850 (KcPGA), Providencia rettgeri InaCC B25 (Pr25PGA), and P. rettgeri InaCC B466 (Pr466PGA). Penicillin G Acylase encoding genes (pgas) were isolated from them using a Degenerate Oligonucleotide Primed-PCR (DOP-PCR) approach and sequenced. Microbiological assays confirmed all tested crude extracts to exhibit inhibitory effects. Penicillin G was used for evaluating hydrolytic activity and 6-Amino Penicillanic Acid (6-APA) coupled …

Gramseq-Dta: A Grammar-Based Drug-Target Affinity Prediction Approach Fusing Gene Expression Information, Kasul Debnath, Pratip Rana, Preetam Ghosh

Computer Science Faculty Publications

Drug–target affinity (DTA) prediction is a critical aspect of drug discovery. The meaningful representation of drugs and targets is crucial for accurate prediction. Using 1D string-based representations for drugs and targets is a common approach that has demonstrated good results in drug–target affinity prediction. However, these approach lacks information on the relative position of the atoms and bonds. To address this limitation, graph-based representations have been used to some extent. However, solely considering the structural aspect of drugs and targets may be insufficient for accurate DTA prediction. Integrating the functional aspect of these drugs at the genetic level can enhance …

Benchmarking Batch-Effect Correction Methods Towards The Construction Of A Triple-Negative Breast Cancer Cell Atlas, Peter Scheible, Amy H. Tang, Jing He, Jiangwen Sun

Computer Science Faculty Publications

Triple-negative breast cancer (TNBC) requires detailed cellular mapping given its aggressive nature, immense tumor heterogeneity and genetic diversity. We integrated 156,794 cells from six scRNA-seq datasets—including tumors, metastases, and cell lines—to build a TNBC scRNA cell atlas, focusing on batch effect mitigation while maintaining biological and molecular details. Preprocessing f ilters noise, normalizes data, and leverages PCA for integration readiness. We utilized scANVI, a semi-supervised tool, to align datasets, preserving TNBC’s complex tumor heterogeneity via marker annotations [1]. UMAPs demonstrate biological clustering in integrated data, contrasted with datasetdriven unintegrated patterns. Assessments verifying effective batch correction. This method aligns with NASA’s …