A gradient boosting machine learning model for neo-epitopes classification - Barde, Romi; Islam, Mohammad; Al Seesi, Sahar (2025 Women in Bioinformatics Workshop (WIBI), 2025)
Keywords: Artificial intelligence; Boosting (machine learning); Computer science; Gradient boosting; Machine learning; Random forest; dept.csc

Do Online Firms Individualize Search Results? An Empirical Analysis of Individualization on Amazon - Ahmed, Rasha; Al Seesi, Sahar; Ruiz Sánchez, Gerardo (Journal of Theoretical and Applied Electronic Commerce Research, 2022)
Online markets offer sellers access to buyers’ information and, thus, the potential to alter prices and products accordingly. In light of this, we undertook an empirical analysis to test for individualization on Amazon.com. We collect data from individuals recruited to shop for household items. Our results indicate evidence of individualization of search results and net prices (via coupons). We found, contrary to what was expected, that demographic, geolocation, and account information play an insignificant role in individualization of search results. Thus, we conclude that individualization is based on more dynamic information, e.g., online browsing behavior. This highlights the fact that sellers’ need for (and use of) buyer information goes beyond the simple information accessible from the buyers’ accounts to a more rigorous monitoring of buyers’ online behavior.
Keywords: Amazon; dept.csc; individualization; peer-reviewed; price discrimination

GeNeo2: An updated suite of bioinformatics tools for identifying tumor-specific neoepitopes for personalized cancer immunotherapies 2268 - Shcheglova, Tatiana; Al Seesi, Sahar; Sherafat, Elham; Force, Jordan; Srivastava, Pramod; Măndoiu, Ion (The Journal of Immunology, 2025)
Abstract Description The identification and prioritization of cancer-specific neoepitopes from next-generation sequencing data for personalized immunotherapies such as cancer vaccines remains challenging and requires the use of complex bioinformatics approaches. Here, we present GeNeo2, an updated version with enhanced features of the GeNeo toolbox for predicting neoepitopes from matched tumor/normal exome sequencing data coupled with tumor RNA-Seq data (Al Seesi et al., 2023). Unlike GeNeo, which identifies neoepitopes generated by single nucleotide variants, GeNeo2 also predicts neoepitopes generated by somatic indels. A distinguishing feature in GeNeo2 is that it integrates tools for analyzing mass spectrometry immunepeptidomic data, which can reveal neoantigens derived from both canonical and noncanonical sources. Finally, GeNeo2 integrates novel machine-learning approaches to improve the accuracy of somatic variant calling and peptide identification from mass spectrometry data. GeNeo2 tools can be accessed via web-based interfaces deployed on a Galaxy portal accessible at https://neo.engr.uconn.edu/. A virtual machine image for running GeNeo2 locally is also available to academic users upon request. Topic Categories Computational and Systems Immunology (COMP)
Keywords: DNA sequencing; Exome; Exome sequencing; Identification (biology); Personalized medicine; Suite; Toolbox; Translational bioinformatics; dept.csc

A frameshift-generated cancer neoepitope that controls tumor burden in prophylaxis as well as therapy - George, Mariam; Brennick, Cory; Hagymasi, Adam; Shcheglova, Tatiana; Al Seesi, Sahar; Rosales, Tatiana; Baker, Brian; Măndoiu, Ion; Srivastava, Pramod (The Journal of Immunology, 2025)
Insertion or deletion of one or two base pairs within a coding region causes a frameshift, which has the potential to generate neoepitopes (InDel-generated neoepitopes) that lack a self-counterpart and are entirely novel. Despite the obvious appeal of InDel-generated neoepitopes, and the demonstration of such candidate neoepitopes that can elicit a CD8 T-cell response, no InDel-generated neoepitopes that actually control tumors in vivo have been reported thus far. Here, in a mouse colon carcinoma line, we identify 11 InDels, only one of which generates a neoepitope that elicits tumor control in vivo in models of prophylaxis as well as therapy. Although this neoepitope has no self-counterpart, it has a low affinity (IC50 33,937.60 nM) for its MHC I allele. Despite its low affinity for MHC I, this neoepitope elicits antitumor activity in vivo through CD8 T cells. Furthermore, CD8 T cells elicited by this InDel-generated neoepitope, like the neoepitopes created by point mutations, show notably less exhaustion than classical immunogenic epitopes. Ironically, this InDel-generated neoepitope follows the same rules as noted for most of the tumor control-mediating neoepitopes generated by point mutations that have a poor affinity for MHC I alleles.
Keywords: Biology; Cancer research; Frameshift mutation; Gene; Genetics; INDEL Mutation; Immunology; In vivo; Indel; Mutation; dept.csc