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Development of Machine Learning Models for Predicting Radiation Dermatitis in Breast Cancer Patients Using Clinical Risk Factors, Patient-Reported Outcomes, and Serum Cytokine Biomarkers

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Clinical Breast Cancer 2025年

作者： Lin, Neil Abbas-Aghababazadeh, Farnoosh Su, Jie Wu, Alison J. Lin, Cherie Shi, Wei Xu, Wei Haibe-Kains, Benjamin Liu, Fei-Fei Kwan, Jennifer Y.Y. Institute of Medical Science Temerty Faculty of Medicine University of Toronto Toronto Canada Temerty Faculty of Medicine University of Toronto Toronto Canada Princess Margaret Bioinformatics and Computational Genomics Laboratory University Health Network Toronto Canada Biostatistics Division Princess Margaret Cancer Centre Toronto Canada Research Institute Princess Margaret Cancer Centre Toronto Canada Department of Computer Science University of Toronto Toronto Canada Ontario Institute for Cancer Research Toronto Canada Vector Institute for Artificial Intelligence Toronto Canada Department of Radiation Oncology Temerty Faculty of Medicine University of Toronto Toronto Canada Department of Medical Biophysics Temerty Faculty of Medicine University of Toronto Toronto Canada Radiation Medicine Program Princess Margaret Cancer Centre Toronto Canada

Background: Radiation dermatitis (RD) is a significant side effect of radiotherapy experienced by breast cancer patients. Severe symptoms include desquamation or ulceration of irradiated skin, which impacts quality of life and increases healthcare costs. Early identification of patients at risk for severe RD can facilitate preventive management and reduce severe symptoms. This study evaluated the utility of subjective and objective factors, such as patient-reported outcomes (PROs) and serum cytokines, for predicting RD in breast cancer patients. The performance of machine learning (ML) and logistic regression-based models were compared. Patients and Methods: Data from 147 breast cancer patients who underwent radiotherapy was analyzed to develop prognostic models. ML algorithms, including neural networks, random forest, XGBoost, and logistic regression, were employed to predict clinically significant Grade 2+ RD. Clinical factors, PROs, and cytokine biomarkers were incorporated into the risk models. Model performance was evaluated using nested cross-validation with separate loops for hyperparameter tuning and calculating performance metrics. Results: Feature selection identified 18 predictors of Grade 2+ RD including smoking, radiotherapy boost, reduced motivation, and the cytokines interleukin-4, interleukin-17, interleukin-1RA, interferon-gamma, and stromal cell-derived factor-1a. Incorporating these predictors, the XGBoost model achieved the highest performance with an area under the curve (AUC) of 0.780 (95% CI: 0.701-0.854). This was not significantly improved over the logistic regression model, which demonstrated an AUC of 0.714 (95% CI: 0.629-0.798). Conclusion: Clinical risk factors, PROs, and serum cytokine levels provide complementary prognostic information for predicting severe RD in breast cancer patients undergoing radiotherapy. ML and logistic regression models demonstrated comparable performance for predicting clinically significant RD with AUC>0.70. ©

关键词： Cancer survivorship Oncology Predictive modeling Radiotherapy Skin toxicity

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Database Design for Indonesian Scholarship Recommender Systems 7

Database Design for Indonesian Scholarship Recommender Syste...

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7th International Conference on Information Management and Technology, ICIMTech 2022

作者： Elwirehardja, Gregorius Natanael Jason Dominic, Nicholas Pardamean, Bens BINUS Graduate Program - Master of Computer Science Bina Nusantara University Computer Science Department Jakarta11480 Indonesia Bioinformatics and Data Science Research Center Bina Nusantara University Jakarta11480 Indonesia

ISBN: (数字)9781665450904

ISBN: (纸本)9781665450904

Following the evolution of technology, researchers have conducted several studies to propose reliable scholarship recommender systems. However, few have explored the data storage systems, which are highly beneficial to collect huge volumes of real data in training the recommender systems. In this paper, a database design for collecting such data was proposed, equipped with integer encodings for categorical variables and a method to normalize variables with varying ranges. Using Connoly and Begg's database design method, the final design consisted of a normalized Entity Relationship Diagram (ERD) and data dictionaries. The design was also constructed to support usages of various data for determining scholarship recipients, including education history, achievements, and organizational experiences. The proposed design can be implemented on information systems to allow easier information access for scholarships in Indonesia. © 2022 IEEE.

关键词： Recommender systems

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Knowledge Graph-Enhanced Semantic Cache for Low-Latency and Cost-Effective Inference in Large Language Models 9

Knowledge Graph-Enhanced Semantic Cache for Low-Latency and ...

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9th International Conference on Information Management and Technology, ICIMTech 2024

作者： Dominic, Nicholas Pardamean, Bens Bina Nusantara University Bioinformatics & Data Science Research Center Jakarta11480 Indonesia BINUS Graduate Program - Master of Computer Science Bina Nusantara University Computer Science Department Jakarta11480 Indonesia

ISBN: (纸本)9798350390025

In organizational knowledge management, Large Language Model (LLM) caches act as a semantic repository gathered from previous LLM responses. Due to intensive calls from multiple users, LLM may suffer from high inference latency. While there are many prior available approaches to solve this problem, most of them are inherently complex. This paper introduced a Knowledge Graph-enhanced Semantic Cache mechanism as an alternative, lightweight technique to boost retrieval for similar prompts. The latest state-of-the-art open-source LLM, named Google's Gemma-2B-it, was used to generate sample prompts and responses as a draft, while a knowledge graph (KG) was built from Wikipedia sentences. To create embeddings of prompts and KG, all-MiniLM-L6-v2 from SentenceTransformer was used. This new cache system resulted in up to 28% improvement over a standard model. In particular, reinforcement with KG cache embeddings yielded more than 85% semantic cache accuracy. To map the next trajectory of this pilot study, an overview of the extended framework for LLM knowledge management was also presented in this paper. The framework includes the new KG- enhanced cache system equipped with scalable security and fallback mechanisms that can promote green technology through substantial improvements in latency, throughput, and overall LLM costs. © 2024 IEEE.

关键词： Knowledge graph

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Does domain knowledge matter for assertion annotation in clinical texts?

Does domain knowledge matter for assertion annotation in cli...

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2012 IEEE 2nd Conference on Healthcare Informatics, Imaging and Systems Biology, HISB 2012

作者： Mowery, Danielle L. Jordan, Pamela W. Wiebe, Janyce M. Chapman, Wendy W. Liu, Lin Biomedical Informatics Learning Resource and Development Center Intelligent Systems Program University of Pittsburgh Pittsburgh PA United States Biomedical Informatics Biostatistics and Bioinformatics University of California San Diego CA United States

This pilot study aims to determine how well subjects annotate assertions about problem mentions in clinical text and determine if a statistical difference exists between subjects with and without clinical domain knowl... 详细信息

ISBN: (纸本)9780769549217

关键词： Domain Knowledge

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bioinformatics exploration into potential mechanisms of Calamus draco for colorectal cancer treatment 9th

Bioinformatics exploration into potential mechanisms of Cala...

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9th International Conference on Computer Science and Computational Intelligence, ICCSCI 2024

作者： Kenneth, Christian Nirwantono, Rudi Pardamean, Bens Bioinformatics and Data Science Research Center Bina Nusantara University Jakarta11480 Indonesia Biotechnology Department Faculty of Engineering Bina Nusantara University Jakarta11480 Indonesia Computer Science Department BINUS Graduate Program Computer Science Program Bina Nusantara University Jakarta11480 Indonesia

As one of the cancer types with the highest incidence rates, colorectal cancer (CRC) would benefit from treatments with fewer side effects and reduced treatment-resistant potential. One of the options is to harness the anti-CRC potential of natural products. Previous studies have shown that Calamus draco exudate, dragon's blood, has anticancer activity in liver cancer and acute myeloid leukemia, but its bioactivity has not been studied in CRC. Here we conduct a bioinformatics study based on network pharmacology to explore the anti-CRC potential and mechanism of C. draco-derived compounds. The bioinformatics pipeline is composed of compound and target collection, biological network evaluation, and enrichment analysis. We found that there are 43 bioactive compounds from C. draco targeting 91 CRC-related targets, of which most compounds target MEN1, PTGS2, and IDH1. Further analyses show that the targets of C. draco are involved in the cellular response to hypoxia. By inhibiting those targets, C. draco bioactive compounds can potentially hinder angiogenesis and increase treatment response efficacy. © 2024 The Authors.

关键词： Lung cancer

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Super-resolution of mammograms

Super-resolution of mammograms

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2010 IEEE Symposium on Computational Intelligence in bioinformatics and Computational Biology, CIBCB 2010

作者： Zheng, Jun Fuentes, Olac Leung, Ming-Ying Department of Computer Science University of Texas at El Paso El Paso TX United States Department of Mathematical Sciences Bioinformatics Program University of Texas at El Paso El Paso TX United States

ISBN: (纸本)9781424467662

High-quality mammography is the most effective technology presently available for breast cancer screening. High resolution mammograms usually lead to more accurate diagnoses;however, they require large doses of radiation, which may have harmful effects. In this paper, we present a method to synthesize high-resolution mammograms from low-resolution inputs, which offers the potential of allowing accurate diagnoses while minimizing risks to patients. Our algorithm combines statistical machine learning methods and stochastic search to learn the mapping from low-resolution to high-resolution mammograms using a large dataset of training image pairs. Experimental results show that the super-resolution algorithm can generate high-quality, high-resolution breast mammograms from low-resolution input with no human intervention. © IEEE.

关键词： Optical resolving power

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Analysis of Protein Protein Dimeric Interfaces

Analysis of Protein Protein Dimeric Interfaces

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IEEE International Conference on bioinformatics and Biomedicine (BIBM)

作者： Feihong Wu Fadi Towfic Drena Dobbs Vasant Honavar Bioinformatics and Computational Biology Graduate Program Iowa State University Ames IA USA

We analyzed the structural properties and the local surface environment of surface amino acid residues of proteins using a large, non-redundant dataset of 2383 protein chains in dimeric complexes from PDB. We compared the interface residues and non-interface residues based on six properties: side chain orientation, surface roughness, solid angle, ex value, hydrophobicity and interface cluster size. The results of our analysis show that interface residues have side chains pointing inward; interfaces are rougher, tend to be flat, moderately convex or concave and protrude more relative to non-interface surface residues. Interface residues tend to be surrounded by hydrophobic neighbors and tend to form clusters consisting of three or more interfaces residues. These findings are consistent with previous published studies using much smaller datasets, while allowing for more qualitative conclusions due to our larger dataset. Preliminary results suggest the possibility of using the six the properties to identify putative interface residues.

关键词： Proteins Rough surfaces Surface roughness bioinformatics Amino acids Solids Sequences Biomedical computing Computer interfaces Biomedical measurements

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VizEpis: A visualization and mapping tool for interpreting epistasis

VizEpis: A visualization and mapping tool for interpreting e...

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IEEE International Conference on bioinformatics and Biomedicine

作者： Min-Seok Kwon Sungyoung Lee Yongkang Kim Taesung Park Interdisciplinary program in Bioinformatics Seoul National University Department of Statistics Seoul National University

ISBN: (纸本)9781467368001

An important issue in genetic association studies is gene-gene interaction (epistasis) underlying common complex diseases that are affected by multiple genetic factors. Although many methods have been proposed to analyze epistasis, the interpretation of the identified gene-gene interactions is not straightforward. In order to efficiently provide the statistical interpretation and biological evidences of gene-gene interactions, we developed the VizEpis, a tool for visualizing of gene-gene interactions in genetic association analysis and mapping of epistatic interaction to the biological evidence from public interaction databases. Using interaction network and circular plot, the VizEpis provides to explore the interaction network integrated with biological evidences in epigenetic regulation, splicing, transcription, translation and post-translation level. To aid statistical interaction in genotype level, the VizEpis provides pairwise checkerboard, forest, and funnel. VizEpis provides for the user-specified variants with statistical interaction to find biological evidences.

关键词： Vizualization Gene-gene interaction Genome-wide association study

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Exploring Recurrent Neural Network Models for Depression Detection Through Facial Expressions: A Systematic Literature Review 6

Exploring Recurrent Neural Network Models for Depression Det...

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6th International Conference on Computer and Informatics Engineering, IC2IE 2023

作者： Rumahorbo, Brilyan Nathanael Pardamean, Bens Elwirehardja, Gregorius Natanael Bina Nusantara University Binus Graduate Program - Master of Computer Science Computer Science Department Jakarta11480 Indonesia Bina Nusantara University Bioinformatics and Data Science Research Center Jakarta11480 Indonesia

ISBN: (纸本)9798350345162

Major Depressive Disorder (MDD) is a prevalent mental disorder, affecting a significant number of individuals, with estimates reaching 300 million cases worldwide. Currently, the diagnosis of this condition relies heavily on subjective assessments based on the experience of medical professionals. Therefore, researchers have turned to deep learning models to explore the detection of depression. The objective of this review is to gather information on detecting depression based on facial expressions in videos using deep learning techniques. Overall, this research found that RNN models achieved 7.22 MAE for AVEC2014. LSTM models produced 4.83 MAE for DAIC-WOZ, while GRU models achieved an accuracy of 89.77% for DAIC-WOZ. Features like Facial Action Units (FAU), eye gaze, and landmarks show great potential and need to be further analyzed to improve results. Analysis can include applying feature engineering techniques. Aggregation methods, such as mean calculation, are recommended as effective approaches for data processing. This Systematic Literature Review found that facial expressions do have relevant patterns related to MDD. © 2023 IEEE.

关键词： deep learning facial expression GRU LSTM major depressive disorder

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Using VisANT to analyze networks

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Current Protocols in bioinformatics 2014年第SUPPL.45期45卷 8.8.1-39页

作者： Hu, Zhenjun Bioinformatics Program Boston University Boston MA United States

VisANT is a Web-based workbench for the integrative analysis of biological networks that offers unique features such as exploratory navigation of interaction network and multi-scale visualization and inference with integrated hierarchical knowledge. It provides functionalities for convenient construction, visualization, and analysis of molecular and higher-order networks based on functional (e.g., expression profiles, phylogenetic profiles) and physical (e.g., yeast two-hybrid, chromatin-immunoprecipitation, and drug target) relations from either the Predictome database or user-defined data sets. Analysis capabilities include network structure analysis, over-representation analysis, expression enrichment analysis, etc. Additionally, networks can be saved, accessed, and shared online. VisANT is able to develop and display meta-networks for meta-nodes that are structural complexes, pathways, or any kind of sub-networks. Further, VisANT supports a growing number of standard exchange formats and database-referencing standards, e.g., PSI-MI, KGML, BioPAX, and SBML (in progress).Multiple species are supported to the extent that interactions or associations are available (i.e., public datasets or Predictome database). © 2014 by John Wiley & Sons, Inc.

关键词： Integration Interaction Meta-network Network Visualization

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