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1201P Novel deep learning model and validation of whole slide images in lung cancer diagnosis

Annals of Oncology 2024年 35卷 S772-S772页

作者： A.A. Ahmed M. Fawi A. Brychcy M. Abouzid M. Witt E. Kaczmarek Bioinformatics and Computational Biology PUMS - Poznan University of Medical Sciences Poznan Poland Data Science Spider Silk Security DMCC Dubai United Arab Emirates Department of Clinical Patomorphology DeparHeliodor Swiecicki Clinical Hospital of the Poznan University of Medical Sciences Poznań Poland Department of Physical Pharmacy and Pharmacokinetics PUMS - Poznan University of Medical Sciences Poznan Poland Department of Anatomy PUMS - Poznan University of Medical Sciences Poznan Poland

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Micro-Chimerism Detected By Highly Sensitive AlloHeme TM Solution Is Associated with Treatment Response and Relapse after Allogeneic Stem Cell Transplantation

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Transplantation and Cellular Therapy 2024年第2期30卷 S403-S404页

作者： Kothari, Ashish S, MD, MS Dwivedi, Nishant, MD, PhD Chattopadhyay, Shrikanta, MD Wang, Yun, PhD Zhou, Mingming Vaskova, Evgeniya, PhD Viard, Thierry, PhD Wong, Lily, MS Gulbahce, Natali, PhD Qu, Kunbin, PhD Grskovic, Marica, PhD Kongtim, Piyanuch, MD, PhD Ciurea, Stefan O., MD Clinical Development CareDx Brisbane CA SVP Medical TScan Therapeutics Waltham MA TScan Therapeutics Waltham MA Biostatistics and Data Science CareDx Inc Brisbane CA R&D CareDx Brisbane CA CareDx Inc Brisbane CA Bioinformatics CareDx Brisbane CA Data Science and Biostatistics CareDx Inc Brisbane CA Cell Transplant Therapy CareDx Inc Brisbane CA Division of Hematology/Oncology Department of Medicine Hematopoietic Stem Cell Transplantation and Cellular Therapy Program University of California Irvine Orange CA

Post-transplant chimerism status is associated with graft survival and potentially disease relapse after allogeneic stem cell transplantation (allo-HCT). However, detection of low-level micro-chimerism (MC) following allo-HCT using the current standard with short tandem repeats (STR) PCR method has been challenging due to STR's poor limit of detection (LOD) of 1-5%. A more sensitive method of chimerism detection could help inform early treatment decisions. We present preliminary results from two multi-center trials using a next-generation sequencing (NGS)-based MC detection method, AlloHeme TM (AH), with a higher sensitivity (LOD 0.035-0.043%) (Table 1), as a translational biomarker of treatment response and early relapse, including minimal residual disease (MRD), after allo-HCT for acute leukemias and MDS. Trial 1: NCT04635384 (ACROBAT, CareDx Inc.) is a prospective multi-center observational cohort study. AlloHeme is the study's primary biomarker. ACROBAT has fully enrolled 289 subjects, with a median follow-up of 150 days. Trial 2: NCT05473910 (TSCAN-001, TScan Inc.) is a multi-arm non-randomized controlled Phase 1 study evaluating the safety and efficacy of TScan's TSC100 and TSC101 - allogeneic T-cell receptor-engineered T cells that target haematopoietically restricted antigens HA-1 and HA-2 respectively, both presented on HLA-A*02:01, in mismatched reduced-intensity conditioning (RIC)-haplo donor HCT recipients. AlloHeme is used as a translational biomarker. Complete chimerism (CC) was defined as >99.9% recipient chimerism. Early review of the ongoing ACROBAT study data with CD33 CC (N=73; median follow-up 270 days, range 72-608 days) revealed three relapses (4%), which compares favorably with 18-20% 6-month relapse rates in the CIBMTR data, suggestive of prognostic potential. This data is corroborated by the preliminary findings of Trial 2. Increased MC (iMC) was defined as ≥1% increase in recipient chimerism between two consecutive AH results. Patients with

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Employing best input SVR robust lost function with nature-inspired metaheuristics in wind speed energy forecasting

IAENG International Journal of Computer Science

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IAENG International Journal of Computer science 2020年第3期47卷 572-584页

作者： Caraka, Rezzy Eko Chen, Rung Ching Bakar, Sakhinah Abu Tahmid, Muhammad Toharudin, Toni Pardamean, Bens Huang, Su-Wen College of Informatics Chaoyang University of Technology Taichung City41349 Taiwan School of Mathematical Sciences The National University of Malaysia Bandar Baru Bangi Selangor43600 Malaysia Bioinformatics and Data Science Research Center Bina Nusantara University 11480 Indonesia Manado North Sulawesi91594 Indonesia Department of Statistics Padjadjaran University Jatinagor west java45363 Indonesia Bioinformatics and Data Science Research Center Bina Nusantara University and Computer Science Dept. BINUS Graduate Program-Master of Computer Science Program Bina Nusantara University Jakarta11480 Indonesia Taichung Veterans General Hospital Taiwan

Wind power has been experiencing a quick improvement. Without a doubt, wind is a variable asset that is hard to forecast. For instance, traditionally time series, extra holds are distributed to deal with this uncertainty. This paper presents a comparison of the performance of various Support Vector Regression (SVR) applied to short-term wind power forecasting. The analogy with BORUTA and multivariate adaptive regression splines (MARS) as judge best input and employ genetic algorithm and particle swarm optimization to find best parameter in Support Vector Regression with robust lost function. We measure the accuracy of this models by Symmetric means absolute percentage error (sMAPE) and we get the best model BORUTA-SVR-PSO with sMAPE 2.07155%. Moreover, we measure the energy conversion using Feedback Linearization Control (FLC). © International Association of Engineers.

关键词： Genetic algorithms

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Effects of Reminders from SanibitTM Electronic Hand Hygiene System and Incentives on Hand Hygiene Compliance - A Prospective, Four Phased, Observational Study

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The Journal of hospital infection 2025年

作者： Emma C Huang Qian Xu Yang Liu Peng Liu Maiying Kong Jiapeng Huang Department of Anesthesiology Duke University Durham NC USA Department of Anesthesiology & Perioperative Medicine School of Medicine University of Louisville Louisville KY USA. Department of Bioinformatics and Biostatistics School of Public Health and Information Sciences University of Louisville Louisville KY USA Division of Biometrics and Data Science Fosun Pharma Beijing People's Republic of China. Microsensor Labs LLC Chicago IL USA. Department of Bioinformatics and Biostatistics School of Public Health and Information Sciences University of Louisville Louisville KY USA. Microsensor Labs LLC Chicago IL USA. Electronic address: jiapeng.huang@louisville.edu.

BACKGROUND:We aim to comprehensively evaluate the implementation of the Sanibitsystem in the ICU using individualized reminders and incentives and study the impact of these interventions on hand hygiene compliance in a 4 phased, prospective trial at a tertiary care hospital in the USA. METHODS:An automated hand hygiene monitoring systems was installed in a 10-bed neurological intensive care unit. Phase 1: No reminder, Cash incentive. Phase 2a: Lights ONLY, Cash Incentive. Phase 2b: Lights + Vibrations, Cash Incentive. Phase 3: No reminder, Point System Incentive. Phase 4: Lights + Vibrations, Point System Incentive. RESULTS:The accuracy rate detected by the Sanibit, compared with direct observations, was 85.1%. Phase 2a with lights only and cash incentives achieved the most hand hygiene opportunities in the middle of the week. Phase 4 with lights and vibrations reminders and point system incentives achieved the best in terms of total, full and partial hand hygiene compliance while phase 2a with lights only and cash incentives had the highest hand hygiene opportunities. Hand hygiene compliance rates of going-out of room events were significantly lower than those of going-in room events for quick in-and-out room while the opposite held true for long in-and-out room events. CONCLUSIONS:Hand hygiene opportunities, compliance, and patient contact times varied significantly among different reminders/incentives, individual healthcare workers, date of the week and time of the day. Lights and vibration reminders and point system incentives achieved the best hand hygiene compliance. CLINICAL TRIAL REGISTRATION:NCT03948672.

关键词： Sanibit automated hand hygiene monitoring compliance hand hygiene infection

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Deep learning-based transcription factor activity for stratification of breast cancer patients

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Biochimica et Biophysica Acta - Gene Regulatory Mechanisms 2022年第6期1865卷 194838页

作者： Xiong, Yuqiang Wang, Shiyuan Wei, Haodong Li, Hanshuang Lv, Yingli Chi, Meng Su, Dongqing Lu, Qianzi Yu, Yao Zuo, Yongchun Yang, Lei College of Bioinformatics Science and Technology Harbin Medical University Harbin 150081 China The State key Laboratory of Reproductive Regulation and Breeding of Grassland Livestock College of Life Sciences Inner Mongolia University Hohhot 010070 China Department of Anesthesiology Harbin Medical University Cancer Hospital Harbin 150081 China Digital College Inner Mongolia Intelligent Union Big Data Academy Inner Mingolia Wesure Date Technology Co. Ltd. Hohhot 010010 China

Transcription factors directly bind to DNA and regulate the expression of the gene, causing epigenetic modification of the DNA. They often mediate epigenetic parameters of transcriptional and posttranscriptional mechanisms, and their expression activities can be used to characterize genomic aberrations in cancer cell. In this study, the activity profile of transcription factors inferred by VIPER algorithm. The autoencoder model was applied for compressing the transcription factor activity profile for obtaining more useful transformed features for stratifying patients into two different breast cancer subtypes. The deep learning-based subtypes exhibited superior prognostic value and yielded better risk-stratification than the transcription factor activity-based method. Importantly, according to transformed features, a deep neural network was constructed to predict the subtypes, and achieved the accuracy of 94.98% and area under the ROC curve of 0.9663, respectively. The proposed subtypes were found to be significantly associated with immune infiltration, tumor immunogenicity and so on. Furthermore, the ceRNA network was constructed for the breast cancer subtypes. Besides, 11 master regulators were found to be associated with patients in cluster 1. Given the robustness performance of our deep learning model over multiple breast cancer cohorts, we expected this model may be useful in the area of prognosis prediction and lead some possibility for personalized medicine in breast cancer patients. © 2022 Elsevier B.V.

关键词： Breast cancer Deep learning Prognosis Subtype Transcription factor

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Android-Based oil palm farmer’s income analysis to support precision agriculture

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AIP Conference Proceedings 2023年第1期2594卷

作者： M. P. Bimantio A. Ferhat L. Isnaini D. P. Putra T. Suparyanto B. Pardamean Department of Food and Agricultural Technology Institute of Agriculture STIPER Yogyakarta Indonesia 55281 Department of Agricultural Socio-Economics Institute of Agriculture STIPER Yogyakarta Indonesia 55281 Department of Agricultural Socio-Economics Gadjah Mada University Yogyakarta Indonesia 55281 Department of Agrotechnology Institute of Agriculture STIPER Yogyakarta Indonesia 55281 Computer Science Department BINUS Graduate Program – Master of Computer Science Bina Nusantara University Jakarta Indonesia 11480 Bioinformatics & Data Science Research Center Bina Nusantara University Jakarta Indonesia 11480

The purpose of this research is to create a simulator application in calculating farmer's income from the sale of fresh fruit bunches. Farmers can easily find out the outcomes based on how many kilograms of crops are sold through this application. This research was conducted in Mesuji Regency, Lampung Province, using primary data and descriptive analysis. The sampling technique utilized a proportional random sampling method and consisted of 88 oil palm farmers. The results indicated that the application made it simpler for farmers to find out their income. Farmers only needed to input the land area, the harvest number obtained on the harvest day that had been carried out, and the price of fresh fruit bunches so that they would know the estimated monthly and annual income calculations. These calculations can be used by farmers as a predicting and planning tool to avoid losses.

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O002 High strain evening exercise disrupts sleep: Insights from a real-world examination of ~7-million nights

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Sleep Advances 2023年第SUPPLEMENT_1期4卷 A1-A1页

作者： Leota, J Presby, D Czeisler, M Mascaro, L Capodilupo, E Drummond, S Rajaratnam, S Facer-Childs, E Turner Institute for Brain and Mental Health Monash University Melbourne Australia Department of Data Science and Research Whoop Inc. Boston United States of America Swiss Institute of Bioinformatics Lausanne Switzerland Department of Computational Biology University of Lausanne Lausanne Switzerland Francis Weld Peabody Society Harvard Medical School Boston United States of America

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Deep learning for temporal data representation in electronic health records: A systematic review of challenges and methodologies

arXiv

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arXiv 2021年

作者： Xie, Feng Yuan, Han Ning, Yilin Ong, Marcus Eng Hock Feng, Mengling Hsu, Wynne Chakraborty, Bibhas Liu, Nan Programme in Health Services and Systems Research Duke-NUS Medical School Singapore Singapore Centre for Quantitative Medicine Duke-NUS Medical School Singapore Singapore Department of Emergency Medicine Singapore General Hospital Singapore Singapore Saw Swee Hock School of Public Health National University of Singapore Singapore Singapore School of Computing National University of Singapore Singapore Singapore Institute of Data Science National University of Singapore Singapore Singapore Department of Statistics and Data Science National University of Singapore Singapore Singapore Department of Biostatistics and Bioinformatics Duke University DurhamNC United States Health Services Research Centre Singapore Health Services Singapore Singapore

Objective: Temporal electronic health records (EHRs) can be a wealth of information for secondary uses, such as clinical events prediction or chronic disease management. However, challenges exist for temporal data representation. We therefore sought to identify these challenges and evaluate novel methodologies for addressing them through a systematic examination of deep learning solutions. Methods: We searched five databases (PubMed, EMBASE, the Institute of Electrical and Electronics Engineers [IEEE] Xplore Digital Library, the Association for Computing Machinery [ACM] digital library, and Web of science) complemented with hand-searching in several prestigious computer science conference proceedings. We sought articles that reported deep learning methodologies on temporal data representation in structured EHR data from January 1, 2010, to August 30, 2020. We summarized and analyzed the selected articles from three perspectives: nature of time series, methodology, and model implementation. Results: We included 98 articles related to temporal data representation using deep learning. Four major challenges were identified, including data irregularity, data heterogeneity, data sparsity, and model opacity. We then studied how deep learning techniques were applied to address these challenges. Finally, we discuss some open challenges arising from deep learning. Conclusion: Temporal EHR data present several major challenges for clinical prediction modeling and data utilization. To some extent, current deep learning solutions can address these challenges. Future studies can consider designing comprehensive and integrated solutions. Moreover, researchers should incorporate additional clinical domain knowledge into study designs and enhance the interpretability of the model to facilitate its implementation in clinical practice. © 2021, CC BY.

关键词： Records management

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OncoEnrichR: cancer-dedicated gene set interpretation

arXiv

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arXiv 2021年

作者： Nakken, Sigve Gundersen, Sveinung Bernal, Fabian L.M. Polychronopoulos, Dimitris Hovig, Eivind Wesche, Jørgen Department of Tumor Biology Institute for Cancer Research Oslo University Hospital Norway Centre for Cancer Cell Reprogramming Institute of Clinical Medicine Faculty of Medicine University of Oslo Norway Centre for Bioinformatics Department of Informatics University of Oslo Norway University Center for Information Technology University of Oslo Norway Early Data Science and Computational Oncology Research and Early Development Oncology R&D AstraZeneca Cambridge United Kingdom

Genome-scale screening experiments in cancer produce long lists of candidate genes that require extensive interpretation for biological insight and prioritization for follow-up studies. Interrogation of gene lists frequently represents a significant and time-consuming undertaking, in which experimental biologists typically combine results from a variety of bioinformatics resources in an attempt to portray and understand cancer relevance. As a means to simplify and strengthen the support for this endeavor, we have developed oncoEnrichR, a flexible bioinformatics tool that allows cancer researchers to comprehensively interrogate a given gene list along multiple facets of cancer relevance. oncoEnrichR differs from general gene set analysis frameworks through the integration of an extensive set of prior knowledge specifically relevant for cancer, including ranked gene-tumor type associations, literature-supported proto-oncogene and tumor suppressor gene annotations, target druggability data, regulatory interactions, synthetic lethality predictions, as well as prognostic associations, gene aberrations, and co-expression patterns across tumor types. The software produces a structured and user-friendly analysis report as its main output, where versions of all underlying data resources are explicitly logged, the latter being a critical component for reproducible science. We demonstrate the usefulness of oncoEnrichR through interrogation of two candidate lists from proteomic and CRISPR screens. oncoEnrichR is freely available as a web-based workflow hosted by the Galaxy platform (https://***), and can also be accessed as a stand-alone R package (https://***/sigven/oncoEnrichR). © 2021, CC BY-NC-SA.

关键词： Tumors

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Correction to: Optimal Decision Criteria for the Study Design and Sample Size of a Biomarker‑Driven Phase III Trial

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Therapeutic innovation & regulatory science 2020年第5期54卷 1035-1036页

作者： Akira Takazawa Satoshi Morita Data Science Department ONO Pharmaceutical Co. Ltd 8-2 Kyutaromachi 1-Chome Chuo-ku Osaka 541-8564 Japan. takazawa@ono.co.jp. Department of Biomedical Statistics and Bioinformatics Kyoto University Graduate School of Medicine Kyoto Japan. takazawa@ono.co.jp. Department of Biomedical Statistics and Bioinformatics Kyoto University Graduate School of Medicine Kyoto Japan.

In the original article in the section "Application to the Motivating Example", Greenberg's trial data have been deleted from the historical data.

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