scholarly journals Inferring microRNA-Environmental Factor Interactions Based on Multiple Biological Information Fusion

Molecules ◽  
2018 ◽  
Vol 23 (10) ◽  
pp. 2439 ◽  
Author(s):  
Haiqiong Luo ◽  
Wei Lan ◽  
Qingfeng Chen ◽  
Zhiqiang Wang ◽  
Zhixian Liu ◽  
...  
Keyword(s):  
Information Fusion ◽  
Chemical Structure ◽  
State Of The Art ◽  
Anatomical Therapeutic Chemical ◽  
Computational Method ◽  
Biological Information ◽  
Mirna Sequence ◽  
Chemical Information ◽  
Factor Interactions ◽  
The Relationship

Accumulated studies have shown that environmental factors (EFs) can regulate the expression of microRNA (miRNA) which is closely associated with several diseases. Therefore, identifying miRNA-EF associations can facilitate the study of diseases. Recently, several computational methods have been proposed to explore miRNA-EF interactions. In this paper, a novel computational method, MEI-BRWMLL, is proposed to uncover the relationship between miRNA and EF. The similarities of miRNA-miRNA are calculated by using miRNA sequence, miRNA-EF interaction, and the similarities of EF-EF are calculated based on the anatomical therapeutic chemical information, chemical structure and miRNA-EF interaction. The similarity network fusion is used to fuse the similarity between miRNA and the similarity between EF, respectively. Further, the multiple-label learning and bi-random walk are employed to identify the association between miRNA and EF. The experimental results show that our method outperforms the state-of-the-art algorithms.

Prediction of Disease Comorbidity Using HeteSim Scores based on Multiple Heterogeneous Networks

2019 ◽  
Vol 19 (4) ◽  
pp. 232-241 ◽  
Author(s):  
Xuegong Chen ◽  
Wanwan Shi ◽  
Lei Deng
Keyword(s):  
Protein Interactions ◽  
Experimental Studies ◽  
Treatment Strategies ◽  
Computational Method ◽  
Biological Information ◽  
Support Vector ◽  
Protein Protein Interactions ◽  
Efficient Treatment ◽  
Disease Associations ◽  
Previous State

Background: Accumulating experimental studies have indicated that disease comorbidity causes additional pain to patients and leads to the failure of standard treatments compared to patients who have a single disease. Therefore, accurate prediction of potential comorbidity is essential to design more efficient treatment strategies. However, only a few disease comorbidities have been discovered in the clinic. Objective: In this work, we propose PCHS, an effective computational method for predicting disease comorbidity. Materials and Methods: We utilized the HeteSim measure to calculate the relatedness score for different disease pairs in the global heterogeneous network, which integrates six networks based on biological information, including disease-disease associations, drug-drug interactions, protein-protein interactions and associations among them. We built the prediction model using the Support Vector Machine (SVM) based on the HeteSim scores. Results and Conclusion: The results showed that PCHS performed significantly better than previous state-of-the-art approaches and achieved an AUC score of 0.90 in 10-fold cross-validation. Furthermore, some of our predictions have been verified in literatures, indicating the effectiveness of our method.

scholarly journals An investigation of the interrelationships between linear and nonlinear polarizabilities and bond-length alternation in conjugated organic molecules

1993 ◽  
Vol 90 (23) ◽  
pp. 11297-11301 ◽  
Author(s):  
C B Gorman ◽  
S R Marder
Keyword(s):  
Bond Length ◽  
Organic Molecules ◽  
Computational Method ◽  
Bond Length Alternation ◽  
Conjugated Molecules ◽  
Conjugated Organic Molecules ◽  
Alpha Beta ◽  
Relationship Of ◽  
The Relationship ◽  
Observable Parameter

A computational method was devised to explore the relationship of charge separation, geometry, molecular dipole moment (mu), polarizability (alpha), and hyperpolariz-abilities (beta, gamma) in conjugated organic molecules. We show that bond-length alternation (the average difference in length between single and double bonds in the molecule) is a key structurally observable parameter that can be correlated with hyperpolarizabilities and is thus relevant to the optimization of molecules and materials. By using this method, the relationship of bond-length alternation, mu, alpha, beta, and gamma for linear conjugated molecules is illustrated, and those molecules with maximized alpha, beta, and gamma are described.

Thermal Expansion and Laser Trim Stability of Ruthenium Based Thick Film Resistors on Alumina and on Multilayer Dielectric

1982 ◽  
Vol 1 (1) ◽  
pp. 24-27 ◽  
Author(s):  
I. Taitl
Keyword(s):  
Thermal Expansion ◽  
Thick Film ◽  
Chemical Structure ◽  
Coefficient Of Thermal Expansion ◽  
Ruthenium Dioxide ◽  
Theoretical Conclusion ◽  
Film Resistor ◽  
Thick Film Resistor ◽  
The Relationship ◽  
Multilayer Dielectric

Fired resistors exhibit variations which are minimised by abrasive and laser trimming. The latter may cause unstable behaviour which is further aggravated by thermal shock. The chemical structure of a thick film resistor is analysed with respect to mechanical stress, and the theoretical conclusion that the coefficient of thermal expansion of the resistor should be equal to or smaller than that of the substrate is verified experimentally. The thermal behaviour of ruthenium dioxide is examined and a range of CTE values are determined for materials of varying chemical composition. The relationship between CTE and post laser trimming stability is demonstrated on four thick film resistors which differ in thermal expansion. It is pointed out that formulations with high metallic content can absorb tensile stress by elastic deformation, thus minimising the formation or propagation of laser induced cracks.

DESIGNING A HOLISTIC ADAPTIVE RECOMMENDER SYSTEM (HARS) FOR CUSTOMER RELATIONSHIP DEVELOPMENT: A CONCEPTUAL FRAMEWORK

2021 ◽  
Vol IV (2) ◽  
pp. 84-97
Author(s):  
Alina Popa ◽  
Keyword(s):  
Recommender System ◽  
State Of The Art ◽  
Customer Behavior ◽  
Relationship Development ◽  
Customer Relationship ◽  
Limited Data ◽  
Buying Behavior ◽  
Integrative View ◽  
Client Relationship ◽  
The Relationship

With the recent COVID-19 pandemic, the world we knew changed significantly. The buying behavior shifted as well and is reflected by a growing transition to online interaction, higher media consumption and massive turn to online shopping. Companies that aim to remain top of mind to customers should ensure that their way of interacting with user is both relevant and highly adaptive. Companies should invest in state-of-the-art technologies that help manage and optimize the relationship with the client based on both online and offline data. One of the most popular applications that companies use to develop the client relationship is a Recommender System. The vast majority of traditional recommender systems consider the recommendation as a static procedure and focus either on a specific type of recommendation or on some limited data. In this paper, it is proposed a novel Reinforcement Learning-based recommender system that has an integrative view over data and recommendation landscape, as well as it is highly adaptive to changes in customer behavior, the Holistic Adaptive Recommender System (HARS). From system design to detailed activities, it was attempted to present a comprehensive way of designing and developing a HARS system for an e-commerce company use-case as well as giving a suite of metrics that could be used for its evaluation.

scholarly journals Inference of an Integrative, Executable Network for Rheumatoid Arthritis Combining Data-Driven Machine Learning Approaches and a State-of-the-Art Mechanistic Disease Map

2021 ◽  
Vol 11 (8) ◽  
pp. 785
Author(s):  
Quentin Miagoux ◽  
Vidisha Singh ◽  
Dereck de Mézquita ◽  
Valerie Chaudru ◽  
Mohamed Elati ◽  
...  
Keyword(s):  
Rheumatoid Arthritis ◽  
Machine Learning ◽  
State Of The Art ◽  
Biological Information ◽  
Response To Treatment ◽  
Patient Specific ◽  
Learning Approaches ◽  
Data Types ◽  
Disease Heterogeneity ◽  
Combining Data

Rheumatoid arthritis (RA) is a multifactorial, complex autoimmune disease that involves various genetic, environmental, and epigenetic factors. Systems biology approaches provide the means to study complex diseases by integrating different layers of biological information. Combining multiple data types can help compensate for missing or conflicting information and limit the possibility of false positives. In this work, we aim to unravel mechanisms governing the regulation of key transcription factors in RA and derive patient-specific models to gain more insights into the disease heterogeneity and the response to treatment. We first use publicly available transcriptomic datasets (peripheral blood) relative to RA and machine learning to create an RA-specific transcription factor (TF) co-regulatory network. The TF cooperativity network is subsequently enriched in signalling cascades and upstream regulators using a state-of-the-art, RA-specific molecular map. Then, the integrative network is used as a template to analyse patients’ data regarding their response to anti-TNF treatment and identify master regulators and upstream cascades affected by the treatment. Finally, we use the Boolean formalism to simulate in silico subparts of the integrated network and identify combinations and conditions that can switch on or off the identified TFs, mimicking the effects of single and combined perturbations.

scholarly journals Expanding the chemical information science gateway

F1000Research ◽  
2017 ◽  
Vol 6 ◽  
pp. 1764 ◽  
Author(s):  
Jürgen Bajorath
Keyword(s):  
Biological Activity ◽  
Chemical Structure ◽  
Information Science ◽  
Open Science ◽  
Chemical Information ◽  
High Quality ◽  
Activity Data ◽  
Science Gateway ◽  
Interdisciplinary Field ◽  
Scientific Environment

Broadly defined, chemical information science (CIS) covers chemical structure and data analysis including biological activity data as well as processing, organization, and retrieval of any form of chemical information. The CIS Gateway (CISG) of F1000Research was created to communicate research involving the entire spectrum of chemical information, including chem(o)informatics. CISG provides a forum for high-quality publications and a meaningful alternative to conventional journals. This gateway is supported by leading experts in the field recognizing the need for open science and a flexible publication platform enabling off-the-beaten path contributions. This editorial aims to further rationalize the scope of CISG, position it within its scientific environment, and open it up to a wider audience. Chemical information science is an interdisciplinary field with high potential to interface with experimental work.

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