Towards Identify Selective Antibacterial Peptides Based on Abstracts Meaning

We present an Identify Selective Antibacterial Peptides (ISAP) approach based on abstracts meaning. Laboratories and researchers have significantly increased the report of their discoveries related to antibacterial peptides in primary publications. It is important to find antibacterial peptides that have been reported in primary publications because they can produce antibiotics of different generations that attack and destroy the bacteria. Unfortunately, researchers used heterogeneous forms of natural language to describe their discoveries (sometimes without the sequence of the peptides). Thus, we propose that learning the words meaning instead of the antibacterial peptides sequence is possible to identify and predict antibacterial peptides reported in the PubMed engine. The ISAP approach consists of two stages: training and discovering. ISAP founds that the 35% of the abstracts sample had antibacterial peptides and we tested in the updated Antimicrobial Peptide Database 2 (APD2). ISAP predicted that 45% of the abstracts had antibacterial peptides. That is, ISAP found that 810 antibacterial peptides were not classified like that, so they are not reported in APD2. As a result, this new search tool would complement the APD2 with a set of peptides that are candidates to be antibacterial. Finally, 20% of the abstracts were not semantic related to APD2.

A Sequential and Intensive Weighted Language Modeling Scheme for Multi-Task Learning-Based Natural Language Understanding

Applied Sciences ◽

10.3390/app11073095 ◽

2021 ◽

Vol 11 (7) ◽

pp. 3095

Author(s):

Suhyune Son ◽

Seonjeong Hwang ◽

Sohyeun Bae ◽

Soo Jun Park ◽

Jang-Hwan Choi

Keyword(s):

Natural Language ◽

Language Processing ◽

Empirical Investigation ◽

Natural Language Understanding ◽

Language Modeling ◽

Language Understanding ◽

Task Learning ◽

Language Representation ◽

Internal Transfer ◽

Multi-task learning (MTL) approaches are actively used for various natural language processing (NLP) tasks. The Multi-Task Deep Neural Network (MT-DNN) has contributed significantly to improving the performance of natural language understanding (NLU) tasks. However, one drawback is that confusion about the language representation of various tasks arises during the training of the MT-DNN model. Inspired by the internal-transfer weighting of MTL in medical imaging, we introduce a Sequential and Intensive Weighted Language Modeling (SIWLM) scheme. The SIWLM consists of two stages: (1) Sequential weighted learning (SWL), which trains a model to learn entire tasks sequentially and concentrically, and (2) Intensive weighted learning (IWL), which enables the model to focus on the central task. We apply this scheme to the MT-DNN model and call this model the MTDNN-SIWLM. Our model achieves higher performance than the existing reference algorithms on six out of the eight GLUE benchmark tasks. Moreover, our model outperforms MT-DNN by 0.77 on average on the overall task. Finally, we conducted a thorough empirical investigation to determine the optimal weight for each GLUE task.

The evolution of the antimicrobial peptide database ( APD ) over 18 years: Milestones and new features

Protein Science ◽

10.1002/pro.4185 ◽

2021 ◽

Author(s):

Guangshun Wang ◽

C. Michael Zietz ◽

Ashok Madgapalli ◽

Shuona Wang ◽

Zhe Wang

Keyword(s):

Antimicrobial Peptide ◽

Online decision aids: the role of decision-making styles and decision-making stages

International Journal of Retail & Distribution Management ◽

10.1108/ijrdm-02-2019-0068 ◽

2020 ◽

Vol 48 (6) ◽

pp. 555-574

Author(s):

Preeti Virdi ◽

Arti D. Kalro ◽

Dinesh Sharma

Keyword(s):

Decision Making ◽

Decision Aids ◽

Online Retail ◽

Consideration Set ◽

Choice Process ◽

Content Type ◽

Choice Making ◽

Search Tool ◽

Consideration Set Formation ◽

PurposeDecision aids (DAs) in online retail stores ease consumers' information processing. However, online consumers do not use all decision aids in purchase decision-making. While the literature has documented the effects of individual decision aids or two decision aids at a time, no study has compared the efficacy of multiple decision aids simultaneously. Also, very few studies have looked at the use of decision aids for consumers with maximizing and satisficing tendencies. Hence, this study aims to understand the preferences of maximizers and satisficers towards online decision aids during the choice-making process.Design/methodology/approachThis is an observational study with 60 individuals who were asked to purchase either a search-based or an experience-based product online. Participants' browsing actions and verbalizations during online shopping, were recorded and analysed using NVivo, and later the use of decision aids was mapped along their choice process.FindingsConsumer's preference of decision aids varies across the two stages of the choice process (that is, consideration set formation and evaluation & choice). In their choice formation, maximizers use different decision aids in both stages, that is, filter tool and in-website search tool for search products, and collaborative filtering-based recommender systems and eWOM for experience products. Satisficers used more decision aids as compared to maximizers across the two stages for both product types.Originality/valueThis study is an exploratory attempt to understand how consumers use multiple decision aids present on e-commerce websites.

Identification of Novel Human Immunodeficiency Virus Type 1-Inhibitory Peptides Based on the Antimicrobial Peptide Database

Antimicrobial Agents and Chemotherapy ◽

10.1128/aac.01448-09 ◽

2010 ◽

Vol 54 (3) ◽

pp. 1343-1346 ◽

Cited By ~ 65

Author(s):

Guangshun Wang ◽

Karen M. Watson ◽

Alan Peterkofsky ◽

Robert W. Buckheit

Keyword(s):

Antimicrobial Peptide ◽

Original Sequence ◽

Inhibitory Peptides ◽

Peptide Database ◽

Immunodeficiency Virus ◽

Antiviral Peptides ◽

Anti Hiv ◽

Hiv 1 ◽

Aurein 1.2

ABSTRACT To identify novel anti-HIV-1 peptides based on the antimicrobial peptide database (APD; http://aps.unmc.edu/AP/main.php ), we have screened 30 candidates and found 11 peptides with 50% effective concentrations (EC50) of <10 μM and therapeutic indices (TI) of up to 17. Furthermore, among the eight peptides (with identical amino acid compositions but different sequences) generated by shuffling the sequence of an aurein 1.2 analog, two had a TI twice that of the original sequence. Because antiviral peptides in the database have an arginine/lysine (R/K) ratio of >1, increases in the Arg contents of amphibian maximin H5 and dermaseptin S9 peptides and the database-derived GLK-19 peptide improved the TIs. These examples demonstrate that the APD is a rich resource and a useful tool for developing novel HIV-1-inhibitory peptides.

Characterization of Selective Antibacterial Peptides by Polarity Index

International Journal of Peptides ◽

10.1155/2012/585027 ◽

2012 ◽

Vol 2012 ◽

pp. 1-11 ◽

Cited By ~ 1

Author(s):

C. Polanco ◽

J. L. Samaniego ◽

T. Buhse ◽

F. G. Mosqueira ◽

A. Negron-Mendoza ◽

...

Keyword(s):

Pathogenic Bacteria ◽

Antibacterial Peptide ◽

Computational Method ◽

Antibacterial Peptides ◽

Double Blind ◽

Blind Test ◽

Bacterial Membranes ◽

Polarity Index ◽

Research Activities ◽

In the recent decades, antibacterial peptides have occupied a strategic position for pharmaceutical drug applications and became subject of intense research activities since they are used to strengthen the immune system of all living organisms by protecting them from pathogenic bacteria. This work proposes a simple and easy statistical/computational method through a peptide polarity index measure by which an antibacterial peptide subgroup can be efficiently identified, that is, characterized by a high toxicity to bacterial membranes but presents a low toxicity to mammal cells. These peptides also have the feature not to adopt to an alpha-helicoidal structure in aqueous solution. The double-blind test carried out to the whole Antimicrobial Peptide Database (November 2011) showed an accuracy of 90% applying the polarity index method for the identification of such antibacterial peptide groups.

Enhancing classroom instruction with online news

Aslib Journal of Information Management ◽

10.1108/ajim-11-2019-0309 ◽

2020 ◽

Vol 72 (5) ◽

pp. 725-744

Author(s):

Michael D. Ekstrand ◽

Katherine Landau Wright ◽

Maria Soledad Pera

Keyword(s):

Information Seeking ◽

Critical Incident ◽

Online News ◽

Future Research ◽

Informational Texts ◽

Information Need ◽

Content Type ◽

Search Tool ◽

K 12 ◽

PurposeThis paper investigates how school teachers look for informational texts for their classrooms. Access to current, varied and authentic informational texts improves learning outcomes for K-12 students, but many teachers lack resources to expand and update readings. The Web offers freely available resources, but finding suitable ones is time-consuming. This research lays the groundwork for building tools to ease that burden.Design/methodology/approachThis paper reports qualitative findings from a study in two stages: (1) a set of semistructured interviews, based on the critical incident technique, eliciting teachers' information-seeking practices and challenges; and (2) observations of teachers using a prototype teaching-oriented news search tool under a think-aloud protocol.FindingsTeachers articulated different objectives and ways of using readings in their classrooms, goals and self-reported practices varied by experience level. Teachers struggled to formulate queries that are likely to return readings on specific course topics, instead searching directly for abstract topics. Experience differences did not translate into observable differences in search skill or success in the lab study.Originality/valueThere is limited work on teachers' information-seeking practices, particularly on how teachers look for texts for classroom use. This paper describes how teachers look for information in this context, setting the stage for future development and research on how to support this use case. Understanding and supporting teachers looking for information is a rich area for future research, due to the complexity of the information need and the fact that teachers are not looking for information for themselves.

APD: the Antimicrobial Peptide Database

Nucleic Acids Research ◽

10.1093/nar/gkh025 ◽

2004 ◽

Vol 32 (90001) ◽

pp. 590D-592 ◽

Cited By ~ 442

Author(s):

Z. Wang

Keyword(s):

Antimicrobial Peptide ◽

Antimicrobial peptide identification using multi-scale convolutional network

BMC Bioinformatics ◽

10.1186/s12859-019-3327-y ◽

2019 ◽

Vol 20 (1) ◽

Author(s):

Xin Su ◽

Jing Xu ◽

Yanbin Yin ◽

Xiongwen Quan ◽

Han Zhang

Keyword(s):

Deep Learning ◽

Antimicrobial Peptide ◽

State Of The Art ◽

Peptide Identification ◽

The State ◽

Fusion Model ◽

Learning Methods ◽

Convolutional Network ◽

Multi Scale ◽

Abstract Background Antibiotic resistance has become an increasingly serious problem in the past decades. As an alternative choice, antimicrobial peptides (AMPs) have attracted lots of attention. To identify new AMPs, machine learning methods have been commonly used. More recently, some deep learning methods have also been applied to this problem. Results In this paper, we designed a deep learning model to identify AMP sequences. We employed the embedding layer and the multi-scale convolutional network in our model. The multi-scale convolutional network, which contains multiple convolutional layers of varying filter lengths, could utilize all latent features captured by the multiple convolutional layers. To further improve the performance, we also incorporated additional information into the designed model and proposed a fusion model. Results showed that our model outperforms the state-of-the-art models on two AMP datasets and the Antimicrobial Peptide Database (APD)3 benchmark dataset. The fusion model also outperforms the state-of-the-art model on an anti-inflammatory peptides (AIPs) dataset at the accuracy. Conclusions Multi-scale convolutional network is a novel addition to existing deep neural network (DNN) models. The proposed DNN model and the modified fusion model outperform the state-of-the-art models for new AMP discovery. The source code and data are available at https://github.com/zhanglabNKU/APIN.