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Exploration of dark chemical genomics space via portal learning: Applied to targeting the undruggable genome and COVID-19 anti-infective polypharmacology

arXiv

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arXiv 2021年

作者： Cai, Tian Xie, Li Chen, Muge Liu, Yang He, Di Zhang, Shuo Mura, Cameron Bourne, Philip E. Xie, Lei Ph.D. Program in Computer Science The Graduate Center The City University of New York New York10016 United States Department of Computer Science Hunter College The City University of New York New York10065 United States Master Program in Computer Science Courant Institute of Mathematical Sciences New York University United States School of Data Science Department of Biomedical Engineering University of Virginia VA22903 United States Helen and Robert Appel Alzheimer’s Disease Research Institute Feil Family Brain & Mind Research Institute Weill Cornell Medicine Cornell University New York10021 United States

Advances in biomedicine are largely fueled by exploring uncharted territories of human biology. Machine learning can both enable and accelerate discovery, but faces a fundamental hurdle when applied to unseen data with distributions that differ from previously observed ones—a common dilemma in scientific inquiry. We have developed a new deep learning framework, called Portal Learning, to explore dark chemical and biological space. Three key, novel components of our approach include: (i) end-to-end, step-wise transfer learning, in recognition of biology’s sequence-structure-function paradigm, (ii) out-of-cluster meta-learning, and (iii) stress model selection. Portal Learning provides a practical solution to the out-of-distribution (OOD) problem in statistical machine learning. Here, we have implemented Portal Learning to predict chemical-protein interactions on a genome-wide scale. Systematic studies demonstrate that Portal Learning can effectively assign ligands to unexplored gene families (unknown functions), versus existing state-of-the-art methods, thereby allowing us to target previously "undruggable" proteins and design novel polypharmacological agents for disrupting interactions between SARS-CoV-2 and human proteins. Portal Learning is general-purpose and can be further applied to other areas of scientific *** Codes 68T07 © 2021, CC BY.

关键词： Genes

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Comparative analysis of disaster information website based on web usability evaluation and quality content of disaster information 4

Comparative analysis of disaster information website based o...

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4th International Conference on science and Technology, ICST 2018

作者： Agus Riyanto, Indra Abdatush Sholihah, Ratri Cahyadi, Ahmad Master Program on Planning and Management of Coastal Area and Watershed Faculty of Geography Universitas Gadjah Mada Indonesia Computer Science Computer Science and Electronics Department Universitas Gadjah Mada Indonesia Environmental Geography Department Universitas Gadjah Mada Indonesia

Disaster is one of the most important researches because every country in the world has their own disaster potential. Disaster assessment improves over time due to technological and information advancements;and it is very reliant to a long period of data record. Disaster information is already available online in various websites, and is presented in form of map, data, video and multimedia contents. Each country has different disaster information standards, the Web Usability and quality of disaster-related content. This research objective is to find good model WEBUSE and quality content both national and international disaster website. National disaster website includes: Geospatial Information Agency (Badan Informasi Geospasial/BIG), Meteorological, Climatological and Geophysical Agency (Badan Meteorologi Klimatologi dan Geofisika/BMKG), National Disaster Management Authority (Badan Nasional Penanggulangan Bencana/BNPB);while international website includes: National Oceanic and Atmospheric Administration (NOAA) and United States Geological Survey (USGS). Usability comparison method uses the WEBUSE questionnaire with 4 categories, content organization and readability, navigation and links, user interface design, and performance and effectiveness;while the disaster-related content quality is measured using scoring method, with 4 categories, the geospatial information, data quality, product, and accessibility, according to United Nations Office for Disaster Risk Reduction (UNISDR). The usability level analysis for all 5 websites are classified as "good", with USGS achieving the highest score of 0.703. As for the disaster-related quality content analysis result, USGS fulfills all categories. © The Authors, published by EDP sciences, 2019.

关键词： Disasters

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Real-time Weed Identification Using Machine Learning and Image Processing in Oil Palm Plantations

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IOP Conference Series: Earth and Environmental science 2022年第1期998卷

作者： Erick Firmansyah Teddy Suparyanto Alam Ahmad Hidayat Bens Pardamean Agricultural Engineering Department Institute of Agricultural STIPER Yogyakarta 55282 Indonesia Bioinformatics and Data Science Research Center Bina Nusantara University Jakarta Indonesia 11480 Computer Science Department BINUS Graduate Program – Master of Computer Science Bina Nusantara University Jakarta Indonesia 11480

The effectiveness and efficiency of the operation of oil palm plantations are considered to be the most crucial factor to develop the productivity and profitability of the palm oil business. One of the major obstacles for the plants to optimally produce crops based on their capacity is caused by the presence of noxious weeds in the plantation area. However, weed control via chemical processes may potentially harm the surrounding environment if it is not properly managed. Therefore, an automatic system to assist the farmers to identify and control the weeds is required to minimize harmful impacts on the environment. Machine Learning (ML) and Artificial Intelligence (AI)-based systems provide powerful tools to perform such tasks. In this work, we aim for an ML-based system design to perform an automatic weed recognition task. The methodology can provide an effort for environmental sustainability in oil palm plantations. The weed identification involves the description, the local names, and tolerance class of the weeds as well as suggestions to control them. The flow of this work consists of weed and herbicide data acquisition, data labeling, model configurations, and data training. Further, the proposed system can be adopted as an android-based application in mobile devices that can deploy the trained model to predict weed category in both real-time and non-real-time tasks.

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Smart IoT CPO Storage Tank

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IOP Conference Series: Earth and Environmental science 2022年第1期998卷

作者： Rengga Arnalis Renjani Hermantoro Prima Okta Adi Nugraha Kartika Purwandari Teddy Suparyanto Bens Pardamean Department of Agricultural Engineering Institute of Agriculture STIPER Yogyakarta Indonesia 55281 Bioinformatics and Data Science Research Center Bina Nusantara University Jakarta Indonesia 11480 Computer Science Department BINUS Graduate Program - Master of Computer Science Bina Nusantara University Jakarta Indonesia 11480

Indonesia's crude palm oil (CPO) production from year to year continues to increase, at the end of 2020 it reached 17.35 million tons, up 3.6% from the previous year. Increasing production will result in more CPO stock and require good storage. The storage process that occurs is to maintain the temperature of the CPO so that the quality is not damaged. This temperature regulation is still done manually and raises the risk of work accidents. The purpose of this research is to create a temperature control system and automatic volume measurement that can be monitored from a smartphone. The manufacture of a control system used ESP8266 NodeMCu microcontroller, temperature sensor, proximity sensor, and 1000-Watt heater. programming used the Arduino IDE and C++. The result of this study was an IoT CPO Storage Tank design equipped with sensors and microcontrollers. The temperature was measured with the DS18B20 sensor had a data accuracy of 99.19% while the volume measured with the HC-SR04 sensor had an accuracy of 99.78%. Data obtained from the sensor could be seen through the Thingspeak application from a computer or smartphone.

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Websocket to Support Real Time Smart Home Applications

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Procedia computer science 2019年 157卷 560-566页

作者： Benfano Soewito Christian Fergyanto E. Gunawan Diana I Gede Putra Kusuma Computer Science Department BINUS Graduate Program - Master of Computer Science Bina Nusantara University Jakarta Indonesia 11480 Industrial Engineering Department BINUS Graduate Program - Master of Industrial Engineering Bina Nusantara University Jakarta Indonesia 11480

As we already know, the IoT (Internet of Thing) system has developed and is used in many fields, such as agriculture, security, industry etc. The IoT system requires real time monitoring and this is one of the problems that exists today. Transfering data from the sensor via the internet network to a monitoring device must be less than 300 ms. One process that can cause non-fulfillment of these requirements is a method for displaying the data on a monitor. There are several methods for delivering data from the sensor to a monitor. This paper has been compared between two methods, namely the polling method and the websocket method. The experiment was conducted to compare these two method. The result obtained that the websocket method was better in presenting real time data compared to the polling method. It can be shown in bandwidth usage and memory usage. In the experiment was found that the average of bandwidth usage is 478KB for polling method, and 91KB for web socket method in web based and the memory consumption of websocket less as much as 16% compared to polling method. In android smartphone the average bandwidth usage is 5.1 KB for web socket method and 15 KB for polling method and the memory consumption of websocket less as much as 22% compared to polling method.

关键词： Websocket realtime IoT smart home polling method

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Data Mining for Predicting Customer Satisfaction Using Clustering Techniques

Data Mining for Predicting Customer Satisfaction Using Clust...

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International Conference on Information Management and Technology (ICIMTech)

作者： Kartika Purwandari Join W. C. Sigalingging Muhammad Fhadli Shinta Nur Arizky Bens Pardamean Bioinformatics & Data Science Research Center Bina Nusantara University Jakarta Indonesia National Central University Taoyuan Taiwan BINUS Graduate Program – Master of Computer Science Bina Nusantara University Jakarta Indonesia

ISBN: (数字)9781728170718

ISBN: (纸本)9781728170725

Managing customer satisfaction has become an important business trend, including restaurants business. This study aims to determine the application of the K-means, Spectral Clustering (SC), and Agglomerative Clustering (AC) method for measuring customer satisfaction on a family restaurant in Taiwan. We contribute the data collection process and application of data mining in a family restaurant. The clustering analysis based on agglomerative clustering approach performs as well as the K-means approach to cluster the same characteristics of the customers. At last, this study shows the measurement result of customer satisfaction and provides improvement suggestion to the restaurant concerned.

关键词： Data mining Customer satisfaction Principal component analysis computer science Reliability Data visualization

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The use of android smart phones as a tool for absences

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Procedia computer science 2019年 157卷 238-246页

作者： Benfano Soewito Fergyanto E. Gunawan Indra Permana Rusli Computer Science Department BINUS Graduate Program - Master of Computer Science Bina Nusantara University Jakarta Indonesia 11480 Industrial Engineering Department BINUS Graduate Program - Master of Industrial Engineering Bina Nusantara University Jakarta Indonesia 11480

One of the issues of current attendance system is not being able to record employees who working outside the office. This work discussed a solution designed to solve common operational project problems, especially in attendance and tasklist management activity in companies who has employee working outside of the office. Considering the number of employees who have to visit various places, conventional attendance system is no longer feasible as be the solution. Similar problem occured in tasklist management, with so many employees to manage in different places will make it difficult for Project Manager to assign task and monitor the progress. Based on the problems, solusion was then designed using SDLC Waterfall using the field research data. This research conclude that by using the proposed application, complexity can be reduced and effectiveness can be improved in performing day-to-day operational task and make sure that they can be monitored. Moreover, the number of employee who responsible to make reports of projects and attendance can be reduced about 50%.

关键词： Attendance system Android Smartphone HTML5 Mobile application

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A Vehicle Forensics framework with Post-Quantum Cryptography and Blockchain

A Vehicle Forensics framework with Post-Quantum Cryptography...

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IEEE International Symposium on Intelligent Signal Processing and Communication Systems (ISPACS)

作者： Chien-Lung Hsu Yi-Cheih Hsu Yu-Jen Shih Cheng-Wei Wu Fu-Hau Hsu Graduate Institute of Management Chang Gung University Taiwan Taoyuan R.O.C Department of Information Management Chang Gung University Taiwan Taoyuan R.O.C Healthy Aging Research Center Chang Gung University Taiwan Taoyuan R.O.C Department of Visual Communication Design Ming-Chi University of Technology Taiwan Taoyuan R.O.C Chang-Gung Memorial Hospital Taiwan Taoyuan R.O.C Department of Master of Science Degree Program in Innovation for Smart Medicine Chang Gung University Taiwan Taoyuan R.O.C Computer Science& Information Engineering National Central University Taiwan Taoyuan R.O.C Information Management Chang Gung University Taiwan Taoyuan R.O.C

ISBN: (数字)9798350389210

ISBN: (纸本)9798350389227

With the recent rapid development of the Internet of Vehicles and autonomous driving technology, the demand for digital vehicle identification has also increased. Coupled with the gradual maturity of quantum technology, the Internet of Vehicles, which mainly uses wireless communication technology for data transmission, will face the threat of quantum computing and privacy issues. This research proposes the Vehicle Forensics Cloud System (VFCS), a framework combining post-quantum cryptography and blockchain technology to address these issues and provide a comprehensive solution for vehicle digital forensics.

关键词： Wireless communication Quantum computing Digital forensics Insurance Signal processing Blockchains Safety Autonomous vehicles Interoperability Internet of Vehicles

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The role of input imaging combination and ADC threshold on segmentation of acute ischemic stroke lesion using U-Net

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European Radiology 2023年第9期33卷 6157-6167页

作者： Li, Ya-Hui Lin, Shao-Chieh Chung, Hsiao-Wen Chang, Chia-Ching Peng, Hsu-Hsia Huang, Teng-Yi Shen, Wu-Chung Tsai, Chon-Haw Lo, Yu-Chien Lee, Tung-Yang Juan, Cheng-Hsuan Juan, Cheng-En Chang, Hing-Chiu Liu, Yi-Jui Juan, Chun-Jung Graduate Institute of Biomedical Electronics and Bioinformatics National Taiwan University Taipei Taiwan Department of Medical Imaging China Medical University Hsinchu Hospital No. 199 Sec. 1 Xinglong Rd. Zhubei City Hsinchu County 302 Hsinchu Taiwan Ph.D. Program in Electrical and Communication Engineering Feng Chia University Taichung Taiwan Department of Electrical Engineering National Taiwan University Taipei Taiwan Department of Management Science National Yang Ming Chiao Tung University Hsinchu Taiwan Department of Biomedical Engineering and Environmental Sciences National Tsing Hua University Hsinchu Taiwan Department of Electrical Engineering National Taiwan University of Science and Technology Taipei Taiwan Department of Radiology School of Medicine College of Medicine China Medical University Taichung Taiwan Department of Medical Imaging Medical University Hospital Taichung Taiwan Department of Neurology China Medical University Hospital Taichung Taiwan Cheng Ching Hospital Taichung Taiwan Master’s Program of Biomedical Informatics and Biomedical Engineering Feng Chia University Taichung Taiwan Department of Automatic Control Engineering Feng Chia University No. 100 Wenhwa Rd. Seatwen Taichung 40724 Taiwan Department of Biomedical Engineering The Chinese University of Hong Kong ERB1112 11/F William M.W. Mong Engineering Building N.T Shatin Hong Kong Multi-Scale Medical Robotics Center The Chinese University of Hong Kong N.T Shatin Hong Kong Department of Biomedical Engineering National Defense Medical Center Taipei Taiwan Department of Computer Science and Information Engineering National Taiwan University Taipei Taiwan

Background: To evaluate the effect of the weighting of input imaging combo and ADC threshold on the performance of the U-Net and to find an optimized input imaging combo and ADC threshold in segmenting acute ischemic stroke (AIS) lesion. Methods: This study retrospectively enrolled a total of 212 patients having AIS. Four combos, including ADC-ADC-ADC (AAA), DWI-ADC-ADC (DAA), DWI-DWI-ADC (DDA), and DWI-DWI-DWI (DDD), were used as input images, respectively. Three ADC thresholds including 0.6, 0.8 and 1.8 × 10^–3 mm²/s were applied. Dice similarity coefficient (DSC) was used to evaluate the segmentation performance of U-Nets. Nonparametric Kruskal–Wallis test with Tukey–Kramer post-hoc tests were used for comparison. A p <.05 was considered statistically significant. Results: The DSC significantly varied among different combos of images and different ADC thresholds. Hybrid U-Nets outperformed uniform U-Nets at ADC thresholds of 0.6 × 10^–3 mm²/s and 0.8 × 10^–3 mm²/s (p <.001). The U-Net with imaging combo of DDD had segmentation performance similar to hybrid U-Nets at an ADC threshold of 1.8 × 10^–3 mm²/s (p =.062 to 1). The U-Net using the imaging combo of DAA at the ADC threshold of 0.6 × 10^–3 mm²/s achieved the highest DSC in the segmentation of AIS lesion. Conclusions: The segmentation performance of U-Net for AIS varies among the input imaging combos and ADC thresholds. The U-Net is optimized by choosing the imaging combo of DAA at an ADC threshold of 0.6 × 10^–3 mm²/s in segmentating AIS lesion with highest DSC. Key Points: • Segmentation performance of U-Net for AIS differs among input imaging combos. • Segmentation performance of U-Net for AIS differs among ADC thresholds. • U-Net is optimized using DAA with ADC = 0.6 × 10^–3mm²/s. © 2023, The Author(s), under exclusive licence to European Society of Radiology.

关键词： Deep Learning Diffusion Magnetic Resonance Imaging Ischemic Stroke Neural Networks, computer Retrospective Study

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Location embedding based pairwise distance learning for fine-grained diagnosis of urinary stones

arXiv

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arXiv 2024年

作者： Jin, Qiangguo Huang, Jiapeng Sun, Changming Cui, Hui Xuan, Ping Su, Ran Wei, Leyi Wu, Yu-Jie Wu, Chia-An Duh, Henry B.L. Lu, Yueh-Hsun School of Software Northwestern Polytechnical University Shaanxi China Yangtze River Delta Research Institute of NPU Suzhou China CSIRO Data61 Sydney Australia Department of Computer Science and Information Technology La Trobe University Melbourne Australia School of Engineering Shantou University Guangdong China College of Intelligence and Computing Tianjin University Tianjin China School of Information Xiamen University Xiamen China AIDD Faculty of Applied Science Macao Polytechnic University China Department of Radiology Shuang-Ho Hospital Taipei Medical University Taipei Taiwan Master of Public Health Program National Yang Ming Chiao Tung University Taipei Taiwan School of Design The Hong Kong Polytechnic University Hong Kong Department of Radiology School of Medicine College of Medicine Taipei Medical University Taipei Taiwan

The precise diagnosis of urinary stones is crucial for devising effective treatment strategies. The diagnostic process, however, is often complicated by the low contrast between stones and surrounding tissues, as well as the variability in stone locations across different patients. To address this issue, we propose a novel location embedding based pairwise distance learning network (LEPD-Net) that leverages low-dose abdominal X-ray imaging combined with location information for the fine-grained diagnosis of urinary stones. LEPD-Net enhances the representation of stone-related features through context-aware region enhancement, incorporates critical location knowledge via stone location embedding, and achieves recognition of fine-grained objects with our innovative fine-grained pairwise distance learning. Additionally, we have established an in-house dataset on urinary tract stones to demonstrate the effectiveness of our proposed approach. Comprehensive experiments conducted on this dataset reveal that our framework significantly surpasses existing state-of-the-art methods. Copyright © 2024, The Authors. All rights reserved.

关键词： Embeddings

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