A method to automatically push keywords for biological information searching in bio-inspired design

2020 ◽  
pp. 095440622093259
Author(s):  
Chen Chen ◽  
Yan Li ◽  
Ye Tao ◽  
Jiadui Chen ◽  
Qiyu Liu ◽  
...  
Keyword(s):  
Biological Information ◽  
Biological Knowledge ◽  
Data Normalization ◽  
Information Searching ◽  
Promising Alternative ◽  
Search Result ◽  
Design Engineers ◽  
Preliminary Validation ◽  
Similarity Computation ◽  
Computation Data

In bio-inspired design, identifying keywords is an important step of biological information searching. Based on existing information retrieval approaches, the amount of relevant biological information in a search result largely depends on the identified keywords. Due to the limitation of biological knowledge, design engineers are difficult to identify the appropriate keywords that can find the biological information related to engineering requirements. To address this issue, we present an algorithm that can calculate the Composite Correlation Intension of functionally combined words, which integrates semantic similarity computation, data normalization, and corpus technology. Based on the algorithm, a method that automatically pushes keywords for biological information searching in bio-inspired design is also proposed. The method decomposes engineering requirements and structures functionally combined words, calculates the Composite Correlation Intension values of all functionally combined words, ranks the functionally combined words by the Composite Correlation Intension values, takes the functionally combined words with larger Composite Correlation Intension values as keywords, and pushes them. Through these keywords, design engineers retrieve the relevant biological information and discover the required design knowledge. In order to facilitate the use of proposed method, an auxiliary tool was developed in Python environment. Finally, the possibility of proposed method was demonstrated by a preliminary validation and an application case. The results show that the proposed method would be a promising alternative to identify keywords for biological information searching in bio-inspired design.

scholarly journals The Principle of Continuous Biological Information Flow as the Fundamental Foundation for the Biological Sciences. Implications for Ageing Research

2021 ◽  
Author(s):  
Xavi Marsellach
Keyword(s):  
Biological Sciences ◽  
Biological Information ◽  
Biological Knowledge ◽  
Phenotypic Characteristics ◽  
Apparent Contradiction ◽  
Current State ◽  
The Face ◽  
Unique Definition ◽  
Definition Of ◽  
Flow Of Information

The current state of biological knowledge contains an unresolved paradox: life as a continuity in the face of the phenomena of ageing. In this manuscript I propose a theoretical framework that offers a solution for this apparent contradiction. The framework proposed is based on a rethinking of what ageing is at a molecular level, as well as on a rethinking of the mechanisms in charge of the flow of information from one generation to the following ones. I propose an information-based conception of ageing instead of the widely accepted damage-based conception of ageing and propose a full recovery of the chromosome theory of inheritance to describe the intergenerational flow of information. Altogether the proposed framework allows a precise and unique definition of what life is: a continuous flow of biological information. The proposed framework also implies that ageing is merely a consequence of the way in which epigenetically-coded phenotypic characteristics are passed from one generation to the next ones.

Incorporating Biological Information as a Prior in an Empirical Bayes Approach to Analyzing Microarray Data

2005 ◽  
Vol 4 (1) ◽  
Author(s):  
Wei Pan
Keyword(s):  
Mixture Model ◽  
Microarray Data ◽  
Statistical Power ◽  
Empirical Bayes ◽  
Biological Information ◽  
Biological Knowledge ◽  
False Discovery Rates ◽  
Proteomic Data ◽  
Prior Biological Knowledge ◽  
A Genome

Currently the practice of using existing biological knowledge in analyzing high throughput genomic and proteomic data is mainly for the purpose of validations. Here we take a different approach of incorporating biological knowledge into statistical analysis to improve statistical power and efficiency. Specifically, we consider how to fuse biological information into a mixture model to analyze microarray data. In contrast to a standard mixture model where it is assumed that all the genes come from the same (marginal) distribution, including an equal prior probability of having an event, such as having differential expression or being bound by a transcription factor (TF), our proposed mixture model allows the genes in different groups to have different distributions while the grouping of the genes reflects biological information. Using a list of about 800 putative cell cycle-regulated genes as prior biological knowledge, we analyze a genome-wide location data to detect binding sites of TF Fkh1. We find that our proposal improves over the standard approach, resulting in reduced false discovery rates (FDR), and hence it is a useful alternative to the current practice.

Function-based, biologically inspired concept generation

2010 ◽  
Vol 24 (4) ◽  
pp. 521-535 ◽  
Author(s):  
Jacquelyn K.S. Nagel ◽  
Robert L. Nagel ◽  
Robert B. Stone ◽  
Daniel A. McAdams
Keyword(s):  
Engineering Design ◽  
Biological Systems ◽  
Natural World ◽  
Biological Information ◽  
Biological Knowledge ◽  
Concept Generation ◽  
Biologically Inspired ◽  
Modeling Methodology ◽  
Functional Representation ◽  
Engineering Designs

AbstractThe natural world provides numerous cases for inspiration in engineering design. Biological organisms, phenomena, and strategies, which we refer to as biological systems, provide a rich set of analogies. These systems provide insight into sustainable and adaptable design and offer engineers billions of years of valuable experience, which can be used to inspire engineering innovation. This research presents a general method for functionally representing biological systems through systematic design techniques, leading to the conceptualization of biologically inspired engineering designs. Functional representation and abstraction techniques are used to translate biological systems into an engineering context. The goal is to make the biological information accessible to engineering designers who possess varying levels of biological knowledge but have a common understanding of engineering design. Creative or novel engineering designs may then be discovered through connections made between biology and engineering. To assist with making connections between the two domains concept generation techniques that use biological information, engineering knowledge, and automatic concept generation software are employed. Two concept generation approaches are presented that use a biological model to discover corresponding engineering components that mimic the biological system and use a repository of engineering and biological information to discover which biological components inspire functional solutions to fulfill engineering requirements. Discussion includes general guidelines for modeling biological systems at varying levels of fidelity, advantages, limitations, and applications of this research. The modeling methodology and the first approach for concept generation are illustrated by a continuous example of lichen.

scholarly journals Development and preliminary validation of a Five Factor Model measure of Machiavellianism

2018 ◽  
Author(s):  
Katherine Collison ◽  
Colin Vize ◽  
Josh Miller ◽  
Donald Lynam
Keyword(s):  
Personality Traits ◽  
Big Five ◽  
Factor Model ◽  
Five Factor Model ◽  
Big Five Personality Traits ◽  
Big Five Personality ◽  
Promising Alternative ◽  
Preliminary Validation ◽  
Expert Ratings

Machiavellianism is characterized by planfulness, the ability to delay gratification, and interpersonal antagonism (i.e., manipulativeness and callousness). Although its theoretically positive relations with facets of conscientiousness should help distinguish Machiavellianism from psychopathy, current measurements of Machiavellianism are indistinguishable from those of psychopathy due mostly to their assessment of low conscientiousness. The goal of the present study was to create a measure of Machiavellianism that is more in line with theory using an expert-derived profile based on the thirty facets of the Five Factor Model (FFM) and then test the validity of that measure by comparing it to relevant constructs. Previously collected expert ratings of the prototypical Machiavellian individual on FFM facets yielded a profile of 13 facets including low agreeableness and high conscientiousness. Items were written to represent each facet, resulting in a 201-item Five Factor Machiavellianism Inventory (FFMI). Across two studies, with a total of 710 participants recruited via MTurk, the FFMI was reduced to its final 52-item form and was shown to relate as expected to measures of Big Five personality traits, current Machiavellianism measures, psychopathy, narcissism, ambition, and impulsivity. The FFMI is a promising alternative Machiavellianism measure.

scholarly journals The Principle of Continuous Biological Information Flow as the Fundamental Foundation for the Biological Sciences. Implications for Ageing Research

2020 ◽  
Author(s):  
Xavi Marsellach
Keyword(s):  
Biological Sciences ◽  
Biological Information ◽  
Biological Knowledge ◽  
Phenotypic Characteristics ◽  
Apparent Contradiction ◽  
Current State ◽  
The Face ◽  
Unique Definition ◽  
Definition Of ◽  
Flow Of Information

The current state of biological knowledge contains an unresolved paradox: life as a continuity in the face of the phenomena of ageing. In this manuscript I propose a theoretical framework that offers a solution for this apparent contradiction. The framework proposed is based on a rethinking of what ageing is at a molecular level, as well as on a rethinking of the mechanisms in charge of the flow of information from one generation to the following ones. I propose an information-based conception of ageing instead of the widely accepted damage-based conception of ageing and propose a full recovery of the chromosome theory of inheritance to describe the intergenerational flow of information. Altogether the proposed framework allows a precise and unique definition of what life is: a continuous flow of biological information. The proposed framework also implies that ageing is merely a consequence of the way in which epigenetically-coded phenotypic characteristics are passed from one generation to the next ones.

scholarly journals Complementarity of network and sequence information in homologous proteins

2010 ◽  
Vol 7 (3) ◽  
pp. 275-289 ◽  
Author(s):  
Vesna Memišević ◽  
Tijana Milenković ◽  
Nataša Pržulj
Keyword(s):  
Network Topology ◽  
Biological Information ◽  
Biological Knowledge ◽  
Sequence Information ◽  
Ppi Network ◽  
Homology Detection ◽  
Homologous Proteins ◽  
Sequence Identity ◽  
Homology Information ◽  
Network Similarity

Summary Traditional approaches for homology detection rely on finding sufficient similarities between protein sequences. Motivated by studies demonstrating that from non-sequence based sources of biological information, such as the secondary or tertiary molecular structure, we can extract certain types of biological knowledge when sequence-based approaches fail, we hypothesize that protein-protein interaction (PPI) network topology and protein sequence might give insights into different slices of biological information. Since proteins aggregate to perform a function instead of acting in isolation, analyzing complex wirings around a protein in a PPI network could give deeper insights into the protein’s role in the inner working of the cell than analyzing sequences of individual genes. Hence, we believe that one could lose much information by focusing on sequence information alone. We examine whether the information about homologous proteins captured by PPI network topology differs and to what extent from the information captured by their sequences. We measure how similar the topology around homologous proteins in a PPI network is and show that such proteins have statistically significantly higher network similarity than nonhomologous proteins. We compare these network similarity trends of homologous proteins with the trends in their sequence identity and find that network similarities uncover almost as much homology as sequence identities. Although none of the two methods, network topology and sequence identity, seems to capture homology information in its entirety, we demonstrate that the two might give insights into somewhat different types of biological information, as the overlap of the homology information that they uncover is relatively low. Therefore, we conclude that similarities of proteins’ topological neighborhoods in a PPI network could be used as a complementary method to sequence-based approaches for identifying homologs, as well as for analyzing evolutionary distance and functional divergence of homologous proteins.

scholarly journals On Crowd-verification of Biological Networks

2013 ◽  
Vol 7 ◽  
pp. BBI.S12932 ◽  
Author(s):  
Sam Ansari ◽  
Jean Binder ◽  
Stephanie Boue ◽  
Anselmo Di Fabio ◽  
William Hayes ◽  
...  
Keyword(s):  
Biological Networks ◽  
Network Models ◽  
Biological Information ◽  
Current Status ◽  
Biological Knowledge ◽  
Web Based ◽  
Domain Experts ◽  
Graphical Interfaces ◽  
Continuous Use ◽  
Complexity Increase

Biological networks with a structured syntax are a powerful way of representing biological information generated from high density data; however, they can become unwieldy to manage as their size and complexity increase. This article presents a crowd-verification approach for the visualization and expansion of biological networks. Web-based graphical interfaces allow visualization of causal and correlative biological relationships represented using Biological Expression Language (BEL). Crowdsourcing principles enable participants to communally annotate these relationships based on literature evidences. Gamification principles are incorporated to further engage domain experts throughout biology to gather robust peer-reviewed information from which relationships can be identified and verified. The resulting network models will represent the current status of biological knowledge within the defined boundaries, here processes related to human lung disease. These models are amenable to computational analysis. For some period following conclusion of the challenge, the published models will remain available for continuous use and expansion by the scientific community.

scholarly journals Thermo-Hydraulic Modelling and Experimental Validation of an Electro-Hydraulic Compact Drive

Energies ◽  
2021 ◽  
Vol 14 (9) ◽  
pp. 2375
Author(s):  
Søren Ketelsen ◽  
Sebastian Michel ◽  
Torben O. Andersen ◽  
Morten Kjeld Ebbesen ◽  
Jürgen Weber ◽  
...  
Keyword(s):  
Thermal Behaviour ◽  
Passive Cooling ◽  
Limited Information ◽  
Hydraulic Simulation ◽  
Promising Alternative ◽  
Application Range ◽  
Design Engineers ◽  
Wide Range ◽  
Good Energy ◽  
Space Requirements

Electro-hydraulic compact drives (ECDs) are an emerging technology for linear actuation in a wide range of applications. Especially within the low power range of 5–10 kW, the plug-and-play capability, good energy efficiency and small space requirements of ECDs render this technology a promising alternative to replace conventional valve-controlled linear drive solutions. In this power range, ECDs generally rely on passive cooling to keep oil and system temperatures within the tolerated range. When expanding the application range to larger power classes, passive cooling may not be sufficient. Research investigating the thermal behaviour of ECDs is limited but indeed required for a successful expansion of the application range. In order to obtain valuable insights into the thermal behaviour of ECDs, thermo-hydraulic simulation is an important tool. This may enable system design engineers to simulate thermal behaviour and thus develop proper thermal designs during the early design phase, especially if such models contain few parameters that can be determined with limited information available. Our paper presents a lumped thermo-hydraulic model derived from the conservation of mass and energy. The derived model was experimentally validated based on experimental data from an ECD prototype. Results show good accuracy between measured and simulated temperatures. Even a simple thermal model containing only a few thermal resistances may be sufficient to predict steady-state and transient temperatures with reasonable accuracy. The presented model may be used for further investigations into the thermal behaviour of ECDs and thus toward proper thermal designs required to expand the application range.

scholarly journals Integrating Pathway Knowledge with Deep Neural Networks to Reduce the Dimensionality in Single-Cell RNA-Seq Data

2021 ◽  
Author(s):  
Pelin Gundogdu ◽  
Carlos Loucera ◽  
Inmaculada Alamo-Alvarez ◽  
Joaquin Dopazo ◽  
Isabel Nepomuceno
Keyword(s):  
Neural Networks ◽  
Single Cell ◽  
Deep Neural Networks ◽  
Cell Types ◽  
Cellular Heterogeneity ◽  
Biological Information ◽  
Superior Performance ◽  
Biological Knowledge ◽  
Additional Advantage ◽  
Functional Relationships

Abstract BackgroundSingle-cell RNA sequencing (scRNA-seq) data provides valuable insights into cellular heterogeneity which is significantly improving the current knowledge on biology and human disease. One of the main applications of scRNA-seq data analysis is the identification of new cell types and cell states. Deep neural networks (DNNs) are among the best methods to address this problem. However, this performance comes with the trade-off for a lack of interpretability in the results. In this work we propose an intelligible pathway-driven neural network to correctly solve cell-type related problems at single-cell resolution while providing a biologically meaningful representation of the data.ResultsIn this study, we explored the deep neural networks constrained by several types of prior biological information, e.g. signaling pathway information, as a way to reduce the dimensionality of the scRNA-seq data. We have tested the proposed biologically-based architectures on thousands of cells of human and mouse origin across a collection of public datasets in order to check the performance of the model. Specifically, we tested the architecture across different validation scenarios that try to mimic how unknown cell types are clustered by the DNN and how it correctly annotates cell types by querying a database in a retrieval problem. Moreover, our approach demonstrated to be comparable to other less interpretable DNN approaches constrained by using protein-protein interactions gene regulation data. Finally, we show how the latent structure learned by the network could be used to visualize and to interpret the composition of human single cell datasets.ConclusionsHere we demonstrate how the integration of pathways, which convey fundamental information on functional relationships between genes, with DNNs, that provide an excellent classification framework, results in an excellent alternative to learn a biologically meaningful representation of scRNA-seq data. In addition, the introduction of prior biological knowledge in the DNN reduces the size of the network architecture. Comparative results demonstrate a superior performance of this approach with respect to other similar approaches. As an additional advantage, the use of pathways within the DNN structure enables easy interpretability of the results by connecting features to cell functionalities by means of the pathway nodes, as demonstrated with an example with human melanoma tumor cells.

scholarly journals Integrating pathway knowledge with deep neural networks to reduce the dimensionality in single-cell RNA-seq data

2022 ◽  
Vol 15 (1) ◽  
Author(s):  
Pelin Gundogdu ◽  
Carlos Loucera ◽  
Inmaculada Alamo-Alvarez ◽  
Joaquin Dopazo ◽  
Isabel Nepomuceno
Keyword(s):  
Neural Networks ◽  
Single Cell ◽  
Deep Neural Networks ◽  
Cell Types ◽  
Cellular Heterogeneity ◽  
Biological Information ◽  
Superior Performance ◽  
Biological Knowledge ◽  
Additional Advantage ◽  
Functional Relationships

Abstract Background Single-cell RNA sequencing (scRNA-seq) data provide valuable insights into cellular heterogeneity which is significantly improving the current knowledge on biology and human disease. One of the main applications of scRNA-seq data analysis is the identification of new cell types and cell states. Deep neural networks (DNNs) are among the best methods to address this problem. However, this performance comes with the trade-off for a lack of interpretability in the results. In this work we propose an intelligible pathway-driven neural network to correctly solve cell-type related problems at single-cell resolution while providing a biologically meaningful representation of the data. Results In this study, we explored the deep neural networks constrained by several types of prior biological information, e.g. signaling pathway information, as a way to reduce the dimensionality of the scRNA-seq data. We have tested the proposed biologically-based architectures on thousands of cells of human and mouse origin across a collection of public datasets in order to check the performance of the model. Specifically, we tested the architecture across different validation scenarios that try to mimic how unknown cell types are clustered by the DNN and how it correctly annotates cell types by querying a database in a retrieval problem. Moreover, our approach demonstrated to be comparable to other less interpretable DNN approaches constrained by using protein-protein interactions gene regulation data. Finally, we show how the latent structure learned by the network could be used to visualize and to interpret the composition of human single cell datasets. Conclusions Here we demonstrate how the integration of pathways, which convey fundamental information on functional relationships between genes, with DNNs, that provide an excellent classification framework, results in an excellent alternative to learn a biologically meaningful representation of scRNA-seq data. In addition, the introduction of prior biological knowledge in the DNN reduces the size of the network architecture. Comparative results demonstrate a superior performance of this approach with respect to other similar approaches. As an additional advantage, the use of pathways within the DNN structure enables easy interpretability of the results by connecting features to cell functionalities by means of the pathway nodes, as demonstrated with an example with human melanoma tumor cells.

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