Combined kinome inhibition states are predictive of cancer cell line sensitivity to kinase inhibitor combination therapies
Pac Symp Biocomput. 2024;29:276-290.ABSTRACTProtein kinases are a primary focus in targeted therapy development for cancer, owing to their role as regulators in nearly all areas of cell life. Recent strategies targeting the kinome with combination therapies have shown promise, such as trametinib and dabrafenib in advanced melanoma, but empirical design for less characterized pathways remains a challenge. Computational combination screening is an attractive alternative, allowing in-silico filtering prior to experimental testing of drastically fewer leads, increasing efficiency and effectiveness of drug development pipelines...
Source: Pacific Symposium on Biocomputing - December 31, 2023 Category: Bioinformatics Authors: Chinmaya U Joisa Kevin A Chen Samantha Beville Timothy Stuhlmiller Matthew E Berginski Denis Okumu Brian T Golitz Michael P East Gary L Johnson Shawn M Gomez Source Type: research
Creation of a Curated Database of Experimentally Determined Human Protein Structures for the Identification of Its Targetome
Pac Symp Biocomput. 2024;29:291-305.ABSTRACTAssembling an "integrated structural map of the human cell" at atomic resolution will require a complete set of all human protein structures available for interaction with other biomolecules - the human protein structure targetome - and a pipeline of automated tools that allow quantitative analysis of millions of protein-ligand interactions. Toward this goal, we here describe the creation of a curated database of experimentally determined human protein structures. Starting with the sequences of 20,422 human proteins, we selected the most representative structure for each protein ...
Source: Pacific Symposium on Biocomputing - December 31, 2023 Category: Bioinformatics Authors: Armand Ovanessians Carson Snow Thomas Jennewein Susanta Sarkar Gil Speyer Judith Klein-Seetharaman Source Type: research
Modeling Path Importance for Effective Alzheimer's Disease Drug Repurposing
Pac Symp Biocomput. 2024;29:306-321.ABSTRACTRecently, drug repurposing has emerged as an effective and resource-efficient paradigm for AD drug discovery. Among various methods for drug repurposing, network-based methods have shown promising results as they are capable of leveraging complex networks that integrate multiple interaction types, such as protein-protein interactions, to more effectively identify candidate drugs. However, existing approaches typically assume paths of the same length in the network have equal importance in identifying the therapeutic effect of drugs. Other domains have found that same length paths...
Source: Pacific Symposium on Biocomputing - December 31, 2023 Category: Bioinformatics Authors: Shunian Xiang Patrick J Lawrence Bo Peng ChienWei Chiang Dokyoon Kim Li Shen Xia Ning Source Type: research
Session Introduction: Overcoming health disparities in precision medicine
ConclusionAcknowledgments.PMID:38160289 (Source: Pacific Symposium on Biocomputing)
Source: Pacific Symposium on Biocomputing - December 31, 2023 Category: Bioinformatics Authors: Francisco M De La Vega Kathleen C Barnes Keolu Fox Alexander Ioannidis Eimear Kenny Rasika A Mathias Bogdan Pasaniuc Source Type: research
PopGenAdapt: Semi-Supervised Domain Adaptation for Genotype-to-Phenotype Prediction in Underrepresented Populations
Pac Symp Biocomput. 2024;29:327-340.ABSTRACTThe lack of diversity in genomic datasets, currently skewed towards individuals of European ancestry, presents a challenge in developing inclusive biomedical models. The scarcity of such data is particularly evident in labeled datasets that include genomic data linked to electronic health records. To address this gap, this paper presents PopGenAdapt, a genotype-to-phenotype prediction model which adopts semi-supervised domain adaptation (SSDA) techniques originally proposed for computer vision. PopGenAdapt is designed to leverage the substantial labeled data available from indivi...
Source: Pacific Symposium on Biocomputing - December 31, 2023 Category: Bioinformatics Authors: Mar çal Comajoan Cara Daniel Mas Montserrat Alexander G Ioannidis Source Type: research
LA-GEM: imputation of gene expression with incorporation of Local Ancestry
Pac Symp Biocomput. 2024;29:341-358.ABSTRACTGene imputation and TWAS have become a staple in the genomics medicine discovery space; helping to identify genes whose regulation effects may contribute to disease susceptibility. However, the cohorts on which these methods are built are overwhelmingly of European Ancestry. This means that the unique regulatory variation that exist in non-European populations, specifically African Ancestry populations, may not be included in the current models. Moreover, African Americans are an admixed population, with a mix of European and African segments within their genome. No gene imputati...
Source: Pacific Symposium on Biocomputing - December 31, 2023 Category: Bioinformatics Authors: Mrinal Mishra Layan Nahlawi Yizhen Zhong Tanima De Guang Yang Cristina Alarcon Minoli A Perera Source Type: research
Cluster Analysis reveals Socioeconomic Disparities among Elective Spine Surgery Patients
Pac Symp Biocomput. 2024;29:359-373.ABSTRACTThis work demonstrates the use of cluster analysis in detecting fair and unbiased novel discoveries. Given a sample population of elective spinal fusion patients, we identify two overarching subgroups driven by insurance type. The Medicare group, associated with lower socioeconomic status, exhibited an over-representation of negative risk factors. The findings provide a compelling depiction of the interwoven socioeconomic and racial disparities present within the healthcare system, highlighting their consequential effects on health inequalities. The results are intended to guide ...
Source: Pacific Symposium on Biocomputing - December 31, 2023 Category: Bioinformatics Authors: Alena Orlenko Philip J Freda Attri Ghosh Hyunjun Choi Nicholas Matsumoto Tiffani J Bright Corey T Walker Tayo Obafemi-Ajayi Jason H Moore Source Type: research
Evidence of recent and ongoing admixture in the U.S. and influences on health and disparities
Pac Symp Biocomput. 2024;29:374-388.ABSTRACTMany researchers in genetics and social science incorporate information about race in their work. However, migrations (historical and forced) and social mobility have brought formerly separated populations of humans together, creating younger generations of individuals who have more complex and diverse ancestry and race profiles than older age groups. Here, we sought to better understand how temporal changes in genetic admixture influence levels of heterozygosity and impact health outcomes. We evaluated variation in genetic ancestry over 100 birth years in a cohort of 35,842 indi...
Source: Pacific Symposium on Biocomputing - December 31, 2023 Category: Bioinformatics Authors: Hannah M Seagle Jacklyn N Hellwege Brian S Mautz Chun Li Yaomin Xu Siwei Zhang Dan M Roden Tracy L McGregor Digna R Velez Edwards Todd L Edwards Source Type: research
Evaluating the relationships between genetic ancestry and the clinical phenome
In this study, we leverage the biobank from Vanderbilt University Medical Center, BioVU, to investigate relationships between genetic ancestry proportion and the clinical phenome. For all samples in BioVU, we calculated six ancestry proportions based on 1000 Genomes references: eastern African (EAFR), western African (WAFR), northern European (NEUR), southern European (SEUR), eastern Asian (EAS), and southern Asian (SAS). From PheWAS, we found phecode categories significantly enriched neoplasms for EAFR, WAFR, and SEUR, and pregnancy complication in SEUR, NEUR, SAS, and EAS (p < 0.003). We then selected phenotypes hyper...
Source: Pacific Symposium on Biocomputing - December 31, 2023 Category: Bioinformatics Authors: Jacqueline A Piekos Jeewoo Kim Jacob M Keaton Jacklyn N Hellwege Todd L Edwards Digna R Velez Edwards Source Type: research
Machine Learning Strategies for Improved Phenotype Prediction in Underrepresented Populations
This study introduces an adaptable machine learning toolkit that integrates multiple existing methodologies and novel techniques to enhance the prediction accuracy for underrepresented populations in genomic datasets. By leveraging machine learning techniques, including gradient boosting and automated methods, coupled with novel population-conditional re-sampling techniques, our method significantly improves the phenotypic prediction from single nucleotide polymorphism (SNP) data for diverse populations. We evaluate our approach using the UK Biobank, which is composed primarily of British individuals with European ancestry...
Source: Pacific Symposium on Biocomputing - December 31, 2023 Category: Bioinformatics Authors: David Bonet May Levin Daniel Mas Montserrat Alexander G Ioannidis Source Type: research
Quantifying Health Outcome Disparity in Invasive Methicillin-Resistant Staphylococcus aureus Infection using Fairness Algorithms on Real-World Data
In conclusion, this work demonstrates a practical utility of fairness AI methods in public health settings.PMID:38160296 (Source: Pacific Symposium on Biocomputing)
Source: Pacific Symposium on Biocomputing - December 31, 2023 Category: Bioinformatics Authors: Inyoung Jun Sarah E Ser Scott A Cohen Jie Xu Robert J Lucero Jiang Bian Mattia Prosperi Source Type: research
Imputation of race and ethnicity categories using genetic ancestry from real-world genomic testing data
This study introduces two methods-one heuristic and the other machine learning-based-to impute race and ethnicity from genetic ancestry using tumor profiling data. Analyzing de-identified data from over 100,000 cancer patients sequenced with the Tempus xT panel, we demonstrate that both methods outperform existing geolocation and surname-based methods, with the machine learning approach achieving high recall (range: 0.859-0.993) and precision (range: 0.932-0.981) across four mutually exclusive race and ethnicity categories. This work presents a novel pathway to enhance RWD utility in studying racial disparities in healthca...
Source: Pacific Symposium on Biocomputing - December 31, 2023 Category: Bioinformatics Authors: Brooke Rhead Paige E Haffener Yannick Pouliot Francisco M De La Vega Source Type: research
Session Introduction: Precision Medicine: Innovative methods for advanced understanding of molecular underpinnings of disease
Pac Symp Biocomput. 2024;29:446-449.ABSTRACTPrecision medicine, also often referred to as personalized medicine, targets the development of treatments and preventative measures specific to the individual's genomic signatures, lifestyle, and environmental conditions. The series of Precision Medicine sessions in PSB has continuously highlighted the advances in this field. Our 2024 collection of manuscripts showcases algorithmic advances that integrate data from distinct modalities and introduce innovative approaches to extract new, medically relevant information from existing data. These evolving technology and analytical me...
Source: Pacific Symposium on Biocomputing - December 31, 2023 Category: Bioinformatics Authors: Yana Bromberg Hannah Carter Steven E Brenner Source Type: research
Enhancing Spatial Transcriptomics Analysis by Integrating Image-Aware Deep Learning Methods
We present a novel method integrating spatial transcriptomics and histopathological image data to better capture biologically meaningful patterns in patient data, focusing on aggressive cancer types such as glioblastoma and triple-negative breast cancer. We used a ResNet-based deep learning model to extract key morphological features from high-resolution whole-slide histology images. Spot-level PCA-reduced vectors of both the ResNet-50 analysis of the histological image and the spatial gene expression data were used in Louvain clustering to enable image-aware feature discovery. Assessment of features from image-aware clust...
Source: Pacific Symposium on Biocomputing - December 31, 2023 Category: Bioinformatics Authors: Jiarong Song Josh Lamstein Vivek Gopal Ramaswamy Michelle Webb Gabriel Zada Steven Finkbeiner David W Craig Source Type: research
Spatial Omics Driven Crossmodal Pretraining Applied to Graph-based Deep Learning for Cancer Pathology Analysis
Pac Symp Biocomput. 2024;29:464-476.ABSTRACTGraph-based deep learning has shown great promise in cancer histopathology image analysis by contextualizing complex morphology and structure across whole slide images to make high quality downstream outcome predictions (ex: prognostication). These methods rely on informative representations (i.e., embeddings) of image patches comprising larger slides, which are used as node attributes in slide graphs. Spatial omics data, including spatial transcriptomics, is a novel paradigm offering a wealth of detailed information. Pairing this data with corresponding histological imaging loca...
Source: Pacific Symposium on Biocomputing - December 31, 2023 Category: Bioinformatics Authors: Zarif L Azher Michael Fatemi Yunrui Lu Gokul Srinivasan Alos B Diallo Brock C Christensen Lucas A Salas Fred W Kolling Laurent Perreard Scott M Palisoul Louis J Vaickus Joshua J Levy Source Type: research
