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General General

Artificial Intelligence and the Common Sense of Animals.

In Trends in cognitive sciences ; h5-index 93.0

The problem of common sense remains a major obstacle to progress in artificial intelligence. Here, we argue that common sense in humans is founded on a set of basic capacities that are possessed by many other animals, capacities pertaining to the understanding of objects, space, and causality. The field of animal cognition has developed numerous experimental protocols for studying these capacities and, thanks to progress in deep reinforcement learning (RL), it is now possible to apply these methods directly to evaluate RL agents in 3D environments. Besides evaluation, the animal cognition literature offers a rich source of behavioural data, which can serve as inspiration for RL tasks and curricula.

Shanahan Murray, Crosby Matthew, Beyret Benjamin, Cheke Lucy

2020-Oct-02

General General

"Individualized learning in a course with a tight schedule".

In Procedia computer science

The article presents a solution supporting individualised learning in courses with a tight schedule. Such courses pose additional organisational challenges and require appropriate tools. The presented solution is based on an Intelligent Tutoring System immersed in repository of e-learning content, which enables selection of content immediately before its provision to the student instead of at the beginning of a course. Thanks to this, the system, having identified the student's needs, is able to make available the most suitable repository content at a given stage of education. The flexibility of the system is guaranteed by modularisation of content and its logical division using the UCTS taxonomy. The content has been described by means of concepts arranged according to the specificity of the domain to which the resources belong in order to ensure that the ITS is able to select relevant content. The proposed solution was used to set up an Applications of Fuzzy Logic course, which was part of an Artificial Intelligence class. The course was conducted within a very limited time frame resulting from the COVID-19 epidemic.

Marciniak Jacek, SzczepaƄski Marcin

2020

Intelligent Tutoring Systems, content repositories, individualized learning

General General

SilencerDB: a comprehensive database of silencers.

In Nucleic acids research ; h5-index 217.0

Gene regulatory elements, including promoters, enhancers, silencers, etc., control transcriptional programs in a spatiotemporal manner. Though these elements are known to be able to induce either positive or negative transcriptional control, the community has been mostly studying enhancers which amplify transcription initiation, with less emphasis given to silencers which repress gene expression. To facilitate the study of silencers and the investigation of their potential roles in transcriptional control, we developed SilencerDB (http://health.tsinghua.edu.cn/silencerdb/), a comprehensive database of silencers by manually curating silencers from 2300 published articles. The current version, SilencerDB 1.0, contains (1) 33 060 validated silencers from experimental methods, and (ii) 5 045 547 predicted silencers from state-of-the-art machine learning methods. The functionality of SilencerDB includes (a) standardized categorization of silencers in a tree-structured class hierarchy based on species, organ, tissue and cell line and (b) comprehensive annotations of silencers with the nearest gene and potential regulatory genes. SilencerDB, to the best of our knowledge, is the first comprehensive database at this scale dedicated to silencers, with reliable annotations and user-friendly interactive database features. We believe this database has the potential to enable advanced understanding of silencers in regulatory mechanisms and to empower researchers to devise diverse applications of silencers in disease development.

Zeng Wanwen, Chen Shengquan, Cui Xuejian, Chen Xiaoyang, Gao Zijing, Jiang Rui

2020-Oct-12

General General

Seeker: alignment-free identification of bacteriophage genomes by deep learning.

In Nucleic acids research ; h5-index 217.0

Recent advances in metagenomic sequencing have enabled discovery of diverse, distinct microbes and viruses. Bacteriophages, the most abundant biological entity on Earth, evolve rapidly, and therefore, detection of unknown bacteriophages in sequence datasets is a challenge. Most of the existing detection methods rely on sequence similarity to known bacteriophage sequences, impeding the identification and characterization of distinct, highly divergent bacteriophage families. Here we present Seeker, a deep-learning tool for alignment-free identification of phage sequences. Seeker allows rapid detection of phages in sequence datasets and differentiation of phage sequences from bacterial ones, even when those phages exhibit little sequence similarity to established phage families. We comprehensively validate Seeker's ability to identify previously unidentified phages, and employ this method to detect unknown phages, some of which are highly divergent from the known phage families. We provide a web portal (seeker.pythonanywhere.com) and a user-friendly Python package (github.com/gussow/seeker) allowing researchers to easily apply Seeker in metagenomic studies, for the detection of diverse unknown bacteriophages.

Auslander Noam, Gussow Ayal B, Benler Sean, Wolf Yuri I, Koonin Eugene V

2020-Oct-12

General General

INTEDE: interactome of drug-metabolizing enzymes.

In Nucleic acids research ; h5-index 217.0

Drug-metabolizing enzymes (DMEs) are critical determinant of drug safety and efficacy, and the interactome of DMEs has attracted extensive attention. There are 3 major interaction types in an interactome: microbiome-DME interaction (MICBIO), xenobiotics-DME interaction (XEOTIC) and host protein-DME interaction (HOSPPI). The interaction data of each type are essential for drug metabolism, and the collective consideration of multiple types has implication for the future practice of precision medicine. However, no database was designed to systematically provide the data of all types of DME interactions. Here, a database of the Interactome of Drug-Metabolizing Enzymes (INTEDE) was therefore constructed to offer these interaction data. First, 1047 unique DMEs (448 host and 599 microbial) were confirmed, for the first time, using their metabolizing drugs. Second, for these newly confirmed DMEs, all types of their interactions (3359 MICBIOs between 225 microbial species and 185 DMEs; 47 778 XEOTICs between 4150 xenobiotics and 501 DMEs; 7849 HOSPPIs between 565 human proteins and 566 DMEs) were comprehensively collected and then provided, which enabled the crosstalk analysis among multiple types. Because of the huge amount of accumulated data, the INTEDE made it possible to generalize key features for revealing disease etiology and optimizing clinical treatment. INTEDE is freely accessible at: https://idrblab.org/intede/.

Yin Jiayi, Li Fengcheng, Zhou Ying, Mou Minjie, Lu Yinjing, Chen Kangli, Xue Jia, Luo Yongchao, Fu Jianbo, He Xu, Gao Jianqing, Zeng Su, Yu Lushan, Zhu Feng

2020-Oct-12

General General

iModulonDB: a knowledgebase of microbial transcriptional regulation derived from machine learning.

In Nucleic acids research ; h5-index 217.0

Independent component analysis (ICA) of bacterial transcriptomes has emerged as a powerful tool for obtaining co-regulated, independently-modulated gene sets (iModulons), inferring their activities across a range of conditions, and enabling their association to known genetic regulators. By grouping and analyzing genes based on observations from big data alone, iModulons can provide a novel perspective into how the composition of the transcriptome adapts to environmental conditions. Here, we present iModulonDB (imodulondb.org), a knowledgebase of prokaryotic transcriptional regulation computed from high-quality transcriptomic datasets using ICA. Users select an organism from the home page and then search or browse the curated iModulons that make up its transcriptome. Each iModulon and gene has its own interactive dashboard, featuring plots and tables with clickable, hoverable, and downloadable features. This site enhances research by presenting scientists of all backgrounds with co-expressed gene sets and their activity levels, which lead to improved understanding of regulator-gene relationships, discovery of transcription factors, and the elucidation of unexpected relationships between conditions and genetic regulatory activity. The current release of iModulonDB covers three organisms (Escherichia coli, Staphylococcus aureus and Bacillus subtilis) with 204 iModulons, and can be expanded to cover many additional organisms.

Rychel Kevin, Decker Katherine, Sastry Anand V, Phaneuf Patrick V, Poudel Saugat, Palsson Bernhard O

2020-Oct-12