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

Procedia Computer Science 2024年 245卷 127-136页

作者： Christian Kenneth Rudi Nirwantono Bens Pardamean Bioinformatics and Data Science Research Center Bina Nusantara University Jakarta Indonesia 11480 Biotechnology Department Faculty of Engineering Bina Nusantara University Jakarta Indonesia 11480 Computer Science Department BINUS Graduate Program – Master of Computer Science Program Bina Nusantara University Jakarta Indonesia 11480

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.

关键词： bioinformatics network pharmacology Calamus draco colorectal cancer drug discovery

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Quantum variational autoencoder utilizing regularized mixed-state latent representations

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Physical Review A 2025年第4期111卷 042416-042416页

作者： Gaoyuan Wang Jonathan Warrell Prashant S. Emani Mark Gerstein Program in Computational Biology and Bioinformatics Yale University New Haven Connecticut 06520 USA Department of Molecular Biophysics and Biochemistry Yale University New Haven Connecticut 06520 USA NEC Laboratories America Princeton New Jersey 08540 USA Department of Computer Science Yale University New Haven Connecticut 06520 USA Department of Statistics & Data Science Yale University New Haven Connecticut 06520 USA Department of Biomedical Informatics & Data Science Yale University New Haven Connecticut 06520 USA

A major challenge in near-term quantum computing is its application to large real-world datasets due to scarce quantum hardware resources. One approach to enabling tractable quantum models for such datasets involves finding low-dimensional representations that preserve essential information for downstream analysis. In classical machine learning, variational autoencoders (VAEs) facilitate efficient data compression, representation learning for subsequent tasks, and novel data generation. However, no quantum model has been proposed that exactly captures all of these features for direct application to quantum data on quantum computers. Some existing quantum models for data compression lack regularization of latent representations, thus preventing direct use for generation and control of generalization. Others are hybrid models with only some internal quantum components, impeding direct training on quantum data. To address this, we present a fully quantum framework, ζ-QVAE, which encompasses all the capabilities of classical VAEs and can be directly applied to map both classical and quantum data to a lower-dimensional space, while effectively reconstructing much of the original state from it. Our model utilizes regularized mixed states to attain optimal latent representations. It accommodates various divergences for reconstruction and regularization. Furthermore, by accommodating mixed states at every stage, it can utilize the full data density matrix and allow for a training objective defined on probabilistic mixtures of input data. Doing so, in turn, makes efficient optimization possible and has potential implications for private and federated learning. In addition to exploring the theoretical properties of ζ-QVAE, we demonstrate its performance on representative genomics and synthetic data. Our results indicate that ζ-QVAE consistently learns representations that better utilize the capacity of the latent space and exhibits similar or better performance compared with

关键词： Quantum computers

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Are dogs not susceptible to retroviral infections?

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Animal Diseases 2024年第3期4卷 217-223页

作者： Jorge Casseb João Henrique Campos Luciano Rodrigo Lopes Laboratory of Investigation in Dermatology and Immunodefciencies Department of DermatologyUniversity of São Paulo Medical SchoolSão PauloSPBrazil Igen-Institute of Immunogenetics São PauloSPBrazil Bioinformatics and Biomedical Data Science Division Health Informatics DepartmentUniversidade Federal de São Paulo–UNIFESP.Rua Botucatu 862Prédio Leal PradotérreoVila ClementinoCEP 04023-062São PauloSPBrazil

Retroviruses have been proven to cause infections and diseases in a series of mammalian hosts but not in ***,this letter discussed the dog susceptibility to retrovirus infection,encompassing arguments to understand why dogs may have not been infected by retroviruses thus *** potential resistance of retrovirus in dogs enables this provocative short communication to discuss this question,looking at some evolutive *** lineage of canids has shown,throughout its evolutionary history,a smaller accumulation of retroviruses in canid genomes,classifed as endogenous *** this context,the genomes of canids seem to ofer obstacles,which have been evolutionarily conserved,in the face of retroviral infection.

关键词： Retrovirus dogs Endogenous retrovirus

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Automatic question generation for bahasa indonesia examination using copynet

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Procedia Computer Science 2024年 245卷 953-962页

作者： Matthew Martianus Henry Gregorius Natanael Elwirehardja Bens Pardamean Bioinformatics and Data Science Research Center Bina Nusantara University Jakarta Indonesia 11480 Computer Science Department School of Computer Science Bina Nusantara University Jakarta Indonesia 11480 Computer Science Department BINUS Graduate Program - Master of Computer Science Bina Nusantara University Jakarta Indonesia 11480

In educational institutions, an educator is responsible for assessing the student's knowledge grasp through examination. Creating exam questions, even the low-level factoid questions, is time-consuming, especially for inexperienced educators. Therefore, this study aims to create a sequence-to-sequence model using CopyNet by exploiting its copying mechanism advantage to automatically generate Bahasa Indonesia factoid questions to ease the educator's burden. Indonesian records in the TyDi QA dataset are used as the model input. GRU and Bi-GRU are employed as the CopyNet encoder, while LSTM is used as the CopyNet decoder. The model that utilizes GRU as the encoder achieves BLEU1, BLEU2, BLEU3, BLEU4, and ROUGE-L scores of 0.28, 0.19, 0.14, 0.1, and 0.32, respectively. Bi-GRU utilization as the model encoder achieves BLEU1, BLEU2, BLEU3, BLEU4, and ROUGE-L scores of 0.26, 0.17, 0.12, 0.09, and 0.30, respectively. Models using either encoder still achieve low scores. However, compared with the previous work, the result is still on par regarding the BLEU score. Further examination found that the generated questions do not adhere to semantic and syntactical correctness. Adding more records to the dataset and utilizing a more advanced architecture like CopyBERT are encouraged to improve the model performance in future work. Despite the result, this study has shown that CopyNet, primarily designed for text summarization or single-turn dialogue, can be tailored for factoid question generation.

关键词： Automatic question generation Bahasa Indonesia Copying mechanism Natural language generation Sequence-to-sequence

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Encoding biological metaverse: Advancements and challenges in neural fields from macroscopic to microscopic

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The Innovation 2024年第3期5卷 30-32页

作者： Yantong Cai Wenbo Hu Yao Pei Hao Zhao Guangchuang Yu Dermatology Hospital Southern Medical UniversityGuangzhou 266003China Department of Bioinformatics School of Basic Medical SciencesSouthern Medical UniversityGuangzhou 510515China Tencent AI Lab Shenzhen 518071China State Key Laboratory of Emerging Infectious Diseases School of Public HealthThe University of Hong KongHong Kong SARChina Laboratory of Data Discovery for Health(D24H) Hong Kong Science ParkHong Kong SARChina Institute for AI Industry Research Tsinghua UniversityBeijing 100000China

Neural fields can efficiently encode three-dimensional(3D)scenes,providing a bridge between two-dimensional(2D)images and virtual *** method becomes a trendsetter in bringing the metaverse into vivo *** has initially captured the attention of macroscopic biology,as demonstrated by computed tomography and magnetic resonance imaging,which provide a 3D field of view for diagnostic biological ***,it has also opened up new research opportunities in microscopic imaging,such as achieving clearer de novo protein structure *** this method to the field of biology is particularly significant,as it is refining the approach to studying biological ***,many biologists have yet to fully appreciate the distinctive meaning of neural fields in transforming 2D images into 3D *** article discusses the application of neural fields in both microscopic and macroscopic biological images and their practical uses in biomedicine,highlighting the broad prospects of neural fields in the future biological *** stand at the threshold of an exciting new era,where the advancements in neural field technology herald the dawn of exploring the mysteries of life in innovative ways.

关键词： neural bringing innovative

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Spatial Confidence Regions for Excursion Sets with False Discovery Rate Control

arXiv

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arXiv 2025年

作者： Ryu, Howon Maullin-Sapey, Thomas Schwartzman, Armin Davenport, Samuel Division of Biostatistics and Bioinformatics University of California San Diego San DiegoCA United States Big Data Institute Nuffield Department of Population Health University of Oxford Oxford United Kingdom School of Mathematics University of Bristol Bristol United Kingdom Halıcıoğlu Data Science Institute University of California San Diego San DiegoCA United States

Identifying areas where the signal is prominent is an important task in image analysis, with particular applications in brain mapping. In this work, we develop confidence regions for spatial excursion sets above and below a given level. We achieve this by treating the confidence procedure as a testing problem at the given level, allowing control of the False Discovery Rate (FDR). Methods are developed to control the FDR, separately for positive and negative excursions, as well as jointly over both. Furthermore, power is increased by incorporating a two-stage adaptive procedure. Simulation results with various signals show that our confidence regions successfully control the FDR under the nominal α level. We showcase our methods with an application to functional magnetic resonance imaging (fMRI) data from the Human Connectome Project illustrating the improvement in statistical power over existing approaches. © 2025, CC BY-NC-SA.

关键词： Image analysis

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Deep learning for oil palm fruit ripeness classification with densenet 6

Deep learning for oil palm fruit ripeness classification wit...

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6th International Conference on Information Management and Technology, ICIMTech 2021

作者： Herman, Herman Cenggoro, Tjeng Wawan Susanto, Albert Pardamean, Bens Bina Nusantara University BINUS Graduate Program - Master of Computer Science Computer Science Department Jakarta11480 Indonesia Bina Nusantara University School of Computer Science Bioinformatics and Data Science Research Center Computer Science Department Jakarta11480 Indonesia Bina Nusantara University BINUS Graduate Program - Master of Computer Science Program Bioinformatics and Data Science Research Center Computer Science Department Jakarta11480 Indonesia

ISBN: (纸本)9781665449373

Oil palm fruit farming is one of the most leading agriculture industries in the South East Asia region. Unfortunately, most of the harvesting method is still done through manual labor. Multiple research has been conducted to help farmers automatically detect the ripeness level of the oil palm fruit. We conducted an experiment of detecting the ripeness level of oil palm fruit by using one of the state-of-the-art computer vision approaches, which was deep learning with a Convolutional Neural Network (CNN). The dataset used consists of 7 levels of ripeness with 400 images of oil palm fruits. The models used in this test scenario were both AlexNet and DenseNet. The result of this study showed that DenseNet has been proven to outperform AlexNet by 8.5% in terms of accuracy and 8% in F1 score. © 2021 IEEE.

关键词： Computer vision

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Ontology-based representation and analysis of conditional vaccine immune responses using Omics data 14

Ontology-based representation and analysis of conditional va...

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14th International Conference on Biomedical Ontologies, ICBO 2023

作者： Huffman, Anthony Ong, Edison Brunson, Tim Sanati, Nasim Zheng, Jie Masci, Anna Maria Wu, Guanming He, Yongqun Department of Computational Medicine and Bioinformatics University of Michigan Ann ArborMI United States GlaxoSmithKline Rixensart1330 Belgium Oregon Health & Science University PortlandOR United States Department of Genetics University of Pennsylvania Perelman School of Medicine PhiladelphiaPA United States Office of Data Science National Institute of Environmental Health Science DurhamNC United States Unit of Laboratory Animal Medicine University of Michigan Ann ArborMI United States Department of Microbiology and Immunology University of Michigan Ann ArborMI United States Center for Computational Medicine and Bioinformatics University of Michigan Ann ArborMI United States

ImmPort, the world’s largest repository of immunology data, includes many vaccine immune response datasets. ImmPort maps the metadata of these studies to ontology and database schema. As of February 28, 2023, our ImmPort data analysis identified 6.258 immune exposures using 47 vaccines in 4,607 human subjects, and 324 cohort studies from the ImmPort. We hypothesized that an integrative ontological representation of the data from these studies would enhance our understanding and analysis of these ImmPort vaccine studies, and with ontological classification and tools such as VIGET, we could further study the effects of different conditions such as vaccine types and host biological sex on the vaccine response gene expression profiles. Our Vaccine Ontology (VO) analysis classified these 37 vaccines into bacterial, viral, and protozoan vaccine types with different vaccine properties. The ImmPort metadata types were modeled with the Vaccine Investigation Ontology (VIO). Our new ontology-based pipeline extracted vaccine response data from the ImmPort database, annotated them based on ontology, obtained corresponding gene expression data from the GEO, and performed consistent omics data analysis. Our use case found gene profiles shared and differed from live and killed inactivated influenza vaccines. Furthermore, our Omics data analysis using the VIGET tool found that female and male human subjects have differential host responses for influenza vaccines. For example, our study showed much stronger early female responses to influenza vaccination than males, and males was able to show active immune responses at a later stage. Interestingly, the female (but not male) human subject group also showed significantly enriched neutrophil degranulation at Day 3 after influenza vaccination;however, males (but not females) displayed significantly enriched neutrophil degranulation at Day 14 after influenza vaccination. These mechanisms have been used to find differences between the gene

关键词： Ontology

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Automatic Smart Crawling on Twitter for Weather Information in Indonesia 8

Automatic Smart Crawling on Twitter for Weather Information ...

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8th International Conference on Computer Science and Computational Intelligence, ICCSCI 2023

作者： Purwandari, Kartika Perdana, Reza Bayu Sigalingging, Join W.C. Rahutomo, Reza Pardamean, Bens Bioinformatics and Data Science Research Center Bina Nusantara University Jakarta11480 Indonesia Database Center Division of BMKG Meteorological Climatological and Geophysical Agency Jakarta10720 Indonesia Information Systems Department School of Information Systems Bina Nusantara University Jakarta11480 Indonesia Computer Science Department BINUS Graduate Program - Master of Computer Science Bina Nusantara University Jakarta11480 Indonesia

Although Twitter is a popular platform for social interaction analysis and text data mining, it faces challenges with geolocation automation. To address this problem, the researchers propose the utilization of a Support Vector Machine (SVM) model to develop an automated Twitter crawling system. The system aims to collect data related to weather in Indonesia by employing Twin, a Python-based Twitter scraping software. To overcome null geolocations, the study incorporates aliases created based on the common practice of Indonesian users mentioning the country's location in tweets. The results demonstrate that the SVM model, combined with automated smart crawling, achieves an 85% accuracy rate. © 2023 The Authors. Published by Elsevier B.V.

关键词： smart crawling Social media SVM Twitter weather

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The effect of visual advanced driver assistance systems on a following human driver in a mixed-Traffic condition 7

The effect of visual advanced driver assistance systems on a...

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7th International Conference on Computer Science and Computational Intelligence, CSCI 2022

作者： Ariansyah, Dedy Pardamean, Bens Caruso, Giandomenico Bioinformatics and Data Science Research Centre Bina Nusantara University Jakarta11480 Indonesia Computer Science Department BINUS Graduate Program Master of Computer Science Program Bina Nusantara University Jakarta11480 Indonesia Department of Mechanical Engineering Politecnico di Milano Milan 20156 Italy

Rapid development in vehicular technology has caused more automated vehicle control to increase on the roads. Studies showed that driving in mixed traffic with an autonomous vehicle (AV) had a negative impact on the time headway (THW) of conventional vehicles (CVs) (i.e., driven by humans). To address this issue, there is a need to equip CV with visual advanced driver assistance systems (ADASs) that helps the driver maintain safe headway when driving near AVs. This study examines the perception of drivers using visual ADAS and their associated risk while driving behind the AV at constant and varying speeds. The preliminary results showed that while visual ADAS could help drivers keep the safe THW, it could affect drivers' ability to react to emergencies. This implies that visual modality alone might not be sufficient and therefore requires some other feedback or intelligent transport systems to help drivers maintain safe driving in a mixed-Traffic condition. © 2022 Elsevier B.V.. All rights reserved.

关键词： Advanced driver assistance systems

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