Related papers: Lessons learned to boost a bioinformatics knowledg…
Knowledge graphs, collectively as a knowledge network, have become critical tools for knowledge discovery in computable and explainable knowledge systems. Due to the semantic and structural complexities of biomedical data, these knowledge…
In the emerging eScience environment, repositories of papers, datasets, software, etc., should be the foundation of a global and natively-digital scholarly communications system. The current infrastructure falls far short of this goal.…
Concurrent advances across fields such as organoid technology, Microelectrode Arrays (MEAs), neuromorphic computing, and machine learning have given rise to a groundbreaking research paradigm: Synthetic Biological Intelligence (SBI). SBI…
Integrative analyses based on statistically relevant associations between genomics and a wealth of intermediary phenotypes (such as imaging) provide vital insights into their clinical relevance in terms of the disease mechanisms. Estimates…
The success of deep learning algorithms generally depends on large-scale data, while humans appear to have inherent ability of knowledge transfer, by recognizing and applying relevant knowledge from previous learning experiences when…
The popularity and wide spread of IoT technology has brought about a rich hardware infrastructure over which it is possible to run powerful applications that were not previously imagined. Among this infrastructure, there are the medical…
Question Answer (QA) systems for biomedical experiments facilitate cross-disciplinary communication, and serve as a foundation for downstream tasks, e.g., laboratory automation. High Information Density (HID) and Multi-Step Reasoning (MSR)…
Reproducibility is a fundamental requirement of the scientific process since it enables outcomes to be replicated and verified. Computational scientific experiments can benefit from improved reproducibility for many reasons, including…
Nowadays, scientific databases have become the bread-and-butter of particle physicists. These databases must be maintained and checked repeatedly to insure the accuracy of their content. The COMPETE collaboration aims at motivating data…
Biology is at the precipice of a new era where AI accelerates and amplifies the ability to study how cells operate, organize, and work as systems, revealing why disease happens and how to correct it. Organizations globally are prioritizing…
Pre-trained language models (PLMs) were considered to be able to store relational knowledge present in the training data. However, some relational knowledge seems to be discarded unsafely in PLMs due to \textbf{report bias}: low-frequency…
Guidelines for managing scientific data have been established under the FAIR principles requiring that data be Findable, Accessible, Interoperable, and Reusable. In many scientific disciplines, especially computational biology, both data…
The analysis of brain connectivity aims to understand the emergence of functional networks into the brain. This information can be used in the process of electroencephalographic (EEG) signal analysis and classification for a braincomputer…
Scientific data governance should prioritize maximizing the utility of data throughout the research lifecycle. Research software systems that enable analysis reproducibility inform data governance policies and assist administrators in…
Context. In less than a year practitioners and researchers witnessed a rapid and wide implementation of Generative Artificial Intelligence. The daily availability of new models proposed by practitioners and researchers has enabled quick…
Science is conducted collaboratively, often requiring the sharing of knowledge about computational experiments. When experiments include only datasets, they can be shared using Uniform Resource Identifiers (URIs) or Digital Object…
Large language models trained on source code can support a variety of software development tasks, such as code recommendation and program repair. Large amounts of data for training such models benefit the models' performance. However, the…
In this paper, we present BIMS (Biomedical Information Management System). BIMS is a software architecture designed to provide a flexible computational framework to manage the information needs of a wide range of biomedical research…
The way in which data are shared can affect their utility and reusability. Here, we demonstrate how data that we had previously shared in bulk can be mobilized further through a knowledge graph that allows for much more granular exploration…
Interdisciplinary PhD programs can be challenging as the vital information needed by students may not be readily available, it is scattered across university's websites, while tacit knowledge can be obtained only by interacting with people.…