scholarly journals Coronavirus canSAR – a Data-Driven, AI-Enabled, Drug Discovery Resource for the Research Community

2020 ◽  
Author(s):  
Costas Mitsopoulos ◽  
Albert A. Antolin ◽  
Eloy Villasclaras Fernandez ◽  
Patrizio Di Micco ◽  
Ioan L. Micca ◽  
...  
Keyword(s):  
Drug Discovery ◽  
Research Community ◽  
Data Driven ◽  
International Effort ◽  
Objective Information ◽  
Great Knowledge

<p>We describe an AI-enabled, integrated Coronavirus drug discovery knowledgebase, free for the research community. Its goal is to make accessible up to date information relevant to drug discovery for SARS-CoV-2 and other coronaviruses. It builds on great knowledge from across therapeutic areas and provides unbiased, systematic, objective information to empower the international effort.</p>

scholarly journals Coronavirus canSAR – a Data-Driven, AI-Enabled, Drug Discovery Resource for the Research Community

2020 ◽  
Author(s):  
Costas Mitsopoulos ◽  
Albert A. Antolin ◽  
Eloy Villasclaras Fernandez ◽  
Patrizio Di Micco ◽  
Ioan L. Micca ◽  
...  
Keyword(s):  
Drug Discovery ◽  
Research Community ◽  
Data Driven ◽  
International Effort ◽  
Objective Information ◽  
Great Knowledge

<p>We describe an AI-enabled, integrated Coronavirus drug discovery knowledgebase, free for the research community. Its goal is to make accessible up to date information relevant to drug discovery for SARS-CoV-2 and other coronaviruses. It builds on great knowledge from across therapeutic areas and provides unbiased, systematic, objective information to empower the international effort.</p>

scholarly journals Learning Damage Representations with Sequence-to-Sequence Models

Sensors ◽  
2022 ◽  
Vol 22 (2) ◽  
pp. 452
Author(s):  
Qun Yang ◽  
Dejian Shen
Keyword(s):  
Damage Detection ◽  
Health Monitoring ◽  
Research Community ◽  
Regions Of Interest ◽  
Data Driven ◽  
Economic Losses ◽  
Shake Table ◽  
Learning Models ◽  
Shake Table Tests ◽  
Damage States

Natural hazards have caused damages to structures and economic losses worldwide. Post-hazard responses require accurate and fast damage detection and assessment. In many studies, the development of data-driven damage detection within the research community of structural health monitoring has emerged due to the advances in deep learning models. Most data-driven models for damage detection focus on classifying different damage states and hence damage states cannot be effectively quantified. To address such a deficiency in data-driven damage detection, we propose a sequence-to-sequence (Seq2Seq) model to quantify a probability of damage. The model was trained to learn damage representations with only undamaged signals and then quantify the probability of damage by feeding damaged signals into models. We tested the validity of our proposed Seq2Seq model with a signal dataset which was collected from a two-story timber building subjected to shake table tests. Our results show that our Seq2Seq model has a strong capability of distinguishing damage representations and quantifying the probability of damage in terms of highlighting the regions of interest.

scholarly journals Collaboration between Government and Research Community to Respond to COVID-19: Israel’s Case

2021 ◽  
Vol 7 (4) ◽  
pp. 208
Author(s):  
Mor Peleg ◽  
Amnon Reichman ◽  
Sivan Shachar ◽  
Tamir Gadot ◽  
Meytal Avgil Tsadok ◽  
...  
Keyword(s):  
Health Policy ◽  
Data Science ◽  
Research Community ◽  
Remote Access ◽  
Data Driven ◽  
Multidisciplinary Teams ◽  
Science Methods ◽  
Research Environments ◽  
Future Data ◽  
The Government

Triggered by the COVID-19 crisis, Israel’s Ministry of Health (MoH) held a virtual datathon based on deidentified governmental data. Organized by a multidisciplinary committee, Israel’s research community was invited to offer insights to help solve COVID-19 policy challenges. The Datathon was designed to develop operationalizable data-driven models to address COVID-19 health policy challenges. Specific relevant challenges were defined and diverse, reliable, up-to-date, deidentified governmental datasets were extracted and tested. Secure remote-access research environments were established. Registration was open to all citizens. Around a third of the applicants were accepted, and they were teamed to balance areas of expertise and represent all sectors of the community. Anonymous surveys for participants and mentors were distributed to assess usefulness and points for improvement and retention for future datathons. The Datathon included 18 multidisciplinary teams, mentored by 20 data scientists, 6 epidemiologists, 5 presentation mentors, and 12 judges. The insights developed by the three winning teams are currently considered by the MoH as potential data science methods relevant for national policies. Based on participants’ feedback, the process for future data-driven regulatory responses for health crises was improved. Participants expressed increased trust in the MoH and readiness to work with the government on these or future projects.

scholarly journals Open-source chemogenomic data-driven algorithms for predicting drug–target interactions

2018 ◽  
Vol 20 (4) ◽  
pp. 1465-1474 ◽  
Author(s):  
Ming Hao ◽  
Stephen H Bryant ◽  
Yanli Wang
Keyword(s):  
Drug Discovery ◽  
Human Genome ◽  
High Throughput Screening ◽  
Drug Target ◽  
Drug Targets ◽  
Drug Repositioning ◽  
Research Field ◽  
Data Driven ◽  
Source Codes ◽  
Novel Technologies

AbstractWhile novel technologies such as high-throughput screening have advanced together with significant investment by pharmaceutical companies during the past decades, the success rate for drug development has not yet been improved prompting researchers looking for new strategies of drug discovery. Drug repositioning is a potential approach to solve this dilemma. However, experimental identification and validation of potential drug targets encoded by the human genome is both costly and time-consuming. Therefore, effective computational approaches have been proposed to facilitate drug repositioning, which have proved to be successful in drug discovery. Doubtlessly, the availability of open-accessible data from basic chemical biology research and the success of human genome sequencing are crucial to develop effective in silico drug repositioning methods allowing the identification of potential targets for existing drugs. In this work, we review several chemogenomic data-driven computational algorithms with source codes publicly accessible for predicting drug–target interactions (DTIs). We organize these algorithms by model properties and model evolutionary relationships. We re-implemented five representative algorithms in R programming language, and compared these algorithms by means of mean percentile ranking, a new recall-based evaluation metric in the DTI prediction research field. We anticipate that this review will be objective and helpful to researchers who would like to further improve existing algorithms or need to choose appropriate algorithms to infer potential DTIs in the projects. The source codes for DTI predictions are available at: https://github.com/minghao2016/chemogenomicAlg4DTIpred.

COMPUTATIONAL APPROACHES FOR DRUG DISCOVERY FROM MEDICINAL PLANTS IN THE ERA OF DATA DRIVEN RESEARCH

INDIAN DRUGS ◽  
2021 ◽  
Vol 58 (08) ◽  
pp. 7-23
Author(s):  
Pratibha Pansari ◽  
Keyword(s):  
Drug Discovery ◽  
Medicinal Plants ◽  
In Silico ◽  
Drug Target ◽  
Target Gene ◽  
Scientific Work ◽  
Data Driven ◽  
Network Pharmacology ◽  
Gene Pathway ◽  
Topological Network

The significant scientific work on the development of bio-active compound databases, computational technologies, and the integration of Information Technology with Biotechnology has brought a revolution in the domain of drug discovery. These tools facilitate the medicinal plant-based in silico drug discovery, which has become the frontier of pharmacological science. In this review article, we elucidate the methodology of in silico drug discovery for the medicinal plants and present an outlook on recent tools and technologies. Further, we explore the multi-component, multi-target, and multi-pathway mechanism of the bio-active compounds with the help of Network Pharmacology, which enables us to create a topological network between drug, target, gene, pathway, and disease.

scholarly journals When Intelligent Transportation Systems Sensing Meets Edge Computing: Vision and Challenges

2021 ◽  
Vol 11 (20) ◽  
pp. 9680
Author(s):  
Xuan Zhou ◽  
Ruimin Ke ◽  
Hao Yang ◽  
Chenxi Liu
Keyword(s):  
Intelligent Transportation Systems ◽  
Intelligent Transportation ◽  
Transportation Systems ◽  
Research Community ◽  
Edge Computing ◽  
Data Driven ◽  
Future Directions ◽  
Critical Part ◽  
Modern Cities ◽  
Computing Vision

The widespread use of mobile devices and sensors has motivated data-driven applications that can leverage the power of big data to benefit many aspects of our daily life, such as health, transportation, economy, and environment. Under the context of smart city, intelligent transportation systems (ITS), such as a main building block of modern cities and edge computing (EC), as an emerging computing service that targets addressing the limitations of cloud computing, have attracted increasing attention in the research community in recent years. It is well believed that the application of EC in ITS will have considerable benefits to transportation systems regarding efficiency, safety, and sustainability. Despite the growing trend in ITS and EC research, a big gap in the existing literature is identified: the intersection between these two promising directions has been far from well explored. In this paper, we focus on a critical part of ITS, i.e., sensing, and conducting a review on the recent advances in ITS sensing and EC applications in this field. The key challenges in ITS sensing and future directions with the integration of edge computing are discussed.

scholarly journals Reintroducing mothur: 10 Years Later

2019 ◽  
Vol 86 (2) ◽  
Author(s):  
Patrick D. Schloss
Keyword(s):  
Sequence Analysis ◽  
Microbial Ecology ◽  
Software Package ◽  
Sequence Data ◽  
Environmental Microbiology ◽  
Research Community ◽  
Data Driven ◽  
Robust Methods ◽  
Simple Set ◽  
New Ideas

ABSTRACT More than 10 years ago, we published the paper describing the mothur software package in Applied and Environmental Microbiology. Our goal was to create a comprehensive package that allowed users to analyze amplicon sequence data using the most robust methods available. mothur has helped lead the community through the ongoing sequencing revolution and continues to provide this service to the microbial ecology community. Beyond its success and impact on the field, mothur’s development exposed a series of observations that are generally translatable across science. Perhaps the observation that stands out the most is that all science is done in the context of prevailing ideas and available technologies. Although it is easy to criticize choices that were made 10 years ago through a modern lens, if we were to wait for all of the possible limitations to be solved before proceeding, science would stall. Even preceding the development of mothur, it was necessary to address the most important problems and work backwards to other problems that limited access to robust sequence analysis tools. At the same time, we strive to expand mothur’s capabilities in a data-driven manner to incorporate new ideas and accommodate changes in data and desires of the research community. It has been edifying to see the benefit that a simple set of tools can bring to so many other researchers.

Sign in / Sign up

Export Citation Format

Share Document