scholarly journals BiCoN: Network-constrained biclustering of patients and omics data

2020 ◽  
Author(s):  
Olga Lazareva ◽  
Stefan Canzar ◽  
Kevin Yuan ◽  
Jan Baumbach ◽  
David B Blumenthal ◽  
...  
Keyword(s):  
Molecular Mechanisms ◽  
Supplementary Information ◽  
Learning Approaches ◽  
Web Interface ◽  
Molecular Interaction Networks ◽  
The Difference ◽  
Disease Associated Genes ◽  
Relationship Of ◽  
Robust To Noise ◽  
Patient Subgroups

Abstract Motivation Unsupervised learning approaches are frequently employed to stratify patients into clinically relevant subgroups and to identify biomarkers such as disease-associated genes. However, clustering and biclustering techniques are oblivious to the functional relationship of genes and are thus not ideally suited to pinpoint molecular mechanisms along with patient subgroups. Results We developed the network-constrained biclustering approach BiCoN (Biclustering Constrained by Networks) which (i) restricts biclusters to functionally related genes connected in molecular interaction networks and (ii) maximizes the difference in gene expression between two subgroups of patients. This allows BiCoN to simultaneously pinpoint molecular mechanisms responsible for the patient grouping. Network-constrained clustering of genes makes BiCoN more robust to noise and batch effects than typical clustering and biclustering methods. BiCoN can faithfully reproduce known disease subtypes as well as novel, clinically relevant patient subgroups, as we could demonstrate using breast and lung cancer datasets. In summary, BiCoN is a novel systems medicine tool that combines several heuristic optimization strategies for robust disease mechanism extraction. BiCoN is well-documented and freely available as a python package or a web interface. Availability and Implementation PyPI package: https://pypi.org/project/bicon Web interface https://exbio.wzw.tum.de/bicon Supplementary information Supplementary data are available at Bioinformatics online.

scholarly journals BiCoN: Network-constrained biclustering of patients and omics data

2020 ◽  
Author(s):  
Olga Lazareva ◽  
Hoan Van Do ◽  
Stefan Canzar ◽  
Kevin Yuan ◽  
Jan Baumbach ◽  
...  
Keyword(s):  
Gene Expression ◽  
Molecular Mechanisms ◽  
Cell Function ◽  
Immune Cell ◽  
Supplementary Information ◽  
Learning Approaches ◽  
Expression Data ◽  
Cancer Subtypes ◽  
Link Type ◽  
Patient Subgroups

AbstractMotivationUnsupervised learning approaches are frequently employed to identify patient subgroups and biomarkers such as disease-associated genes. Thus, clustering and biclustering are powerful techniques often used with expression data, but are usually not suitable to unravel molecular mechanisms along with patient subgroups. To alleviate this, we developed the network-constrained biclustering approach BiCoN (Biclustering Constrained by Networks) which (i) restricts biclusters to functionally related genes connected in molecular interaction networks and (ii) maximizes the difference in gene expression between two subgroups of patients.ResultsOur analyses of non-small cell lung and breast cancer gene expression data demonstrate that BiCoN clusters patients in agreement with known cancer subtypes while discovering gene subnetworks pointing to functional differences between these subtypes. Furthermore, we show that BiCoN is robust to noise and batch effects and can distinguish between high and low load of tumor-infiltrating leukocytes while identifying subnetworks related to immune cell function. In summary, BiCoN is a powerful new systems medicine tool to stratify patients while elucidating the responsible disease mechanism.AvailabilityPyPI package: https://pypi.org/project/biconWeb interface: https://exbio.wzw.tum.de/[email protected] informationSupplementary data are available at Bioinformatics online.

The Effect of Jigsaw Methode and Motivation on Students’ of Learning Outcomes

2020 ◽  
Vol 3 (1) ◽  
pp. 20-30
Author(s):  
Yunita Yunita ◽  
Hidayat Hidayat ◽  
Harun Sitompul
Keyword(s):  
Cooperative Learning ◽  
Student Learning ◽  
Learning Outcomes ◽  
Student Learning Outcomes ◽  
Learning Motivation ◽  
Learning Approaches ◽  
High Learning ◽  
Average Value ◽  
The Difference ◽  
Low Motivation

This study aims to: (1) investigate the effect of Jigsaw cooperative learning on students learning outcomes; (2) find the difference in learning outcomes between high and low learning motivation and (3) find the interaction between learning approaches and learning motivation towards learning outcomes. The population of the study is students of grade IVa, IVb, IVc at SD Kasih Ibu Patumbak and the sample in this study is grade IVa with 35 students and grade IVb with 35 students. The results show that: (1) the average student learning outcomes of jigsaw cooperative learning is 28.40 while conventional is 24.14. Thus, students learning outcomes that get cooperative learning of jigsaw type are higher than conventional learning, (2) Students who have high motivation get an average value = 30.74, while low motivation is 22.72. Thus, it can be concluded that there are differences in student learning outcomes having high learning motivation and low learning motivation, and (3) students learning outcomes  taught by jigsaw cooperative learning are high learning motivation groups (32.94), and low learning motivation groups (24.58), while students taught with conventional learning are high learning motivation groups (28.40 ), and low motivation groups (20,95). Thus, there is no interaction between learning approaches and learning motivation towards learning outcomes.

It Keeps Me Seeking

2018 ◽  
Author(s):  
Andrew Briggs ◽  
Hans Halvorson ◽  
Andrew Steane
Keyword(s):  
Evolutionary Biology ◽  
Quantum Physics ◽  
Natural World ◽  
Theological Reflection ◽  
Fine Tuning ◽  
Good Argument ◽  
The Difference ◽  
Argument From Design ◽  
Relationship Of

Two scientists and a philosopher aim to show how science both enriches and is enriched by Christian faith. The text is written around four themes: 1. God is a being to be known, not a hypothesis to be tested; 2. We set a high bar on what constitutes good argument; 3. Uncertainty is OK; 4. We are allowed to open up the window that the natural world offers us. This is not a work of apologetics. Rather, the text takes an overview of various themes and gives reactions and responses, intended to place science correctly as a valued component of the life of faith. The difference between philosophical analysis and theological reflection is expounded. Questions of human identity are addressed from philosophy, computer science, quantum physics, evolutionary biology and theological reflection. Contemporary physics reveals the subtle and open nature of physical existence, and offers lessons in how to learn and how to live with incomplete knowledge. The nature and role of miracles is considered. The ‘argument from design’ is critiqued, especially arguments from fine-tuning. Logical derivation from impersonal facts is not an appropriate route to a relationship of mutual trust. Mainstream evolutionary biology is assessed to be a valuable component of our understanding, but no exploratory process can itself fully account for the nature of what is discovered. To engage deeply in science is to seek truth and to seek a better future; it is also an activity of appreciation, as one may appreciate a work of art.

scholarly journals MODER2: first-order Markov modeling and discovery of monomeric and dimeric binding motifs

2020 ◽  
Vol 36 (9) ◽  
pp. 2690-2696
Author(s):  
Jarkko Toivonen ◽  
Pratyush K Das ◽  
Jussi Taipale ◽  
Esko Ukkonen
Keyword(s):  
Markov Models ◽  
Expectation Maximization Algorithm ◽  
Software Tool ◽  
Specific Weight ◽  
Training Data ◽  
Supplementary Information ◽  
Markov Modeling ◽  
Binding Motifs ◽  
The Difference ◽  
Probability Matrices

Abstract Motivation Position-specific probability matrices (PPMs, also called position-specific weight matrices) have been the dominating model for transcription factor (TF)-binding motifs in DNA. There is, however, increasing recent evidence of better performance of higher order models such as Markov models of order one, also called adjacent dinucleotide matrices (ADMs). ADMs can model dependencies between adjacent nucleotides, unlike PPMs. A modeling technique and software tool that would estimate such models simultaneously both for monomers and their dimers have been missing. Results We present an ADM-based mixture model for monomeric and dimeric TF-binding motifs and an expectation maximization algorithm MODER2 for learning such models from training data and seeds. The model is a mixture that includes monomers and dimers, built from the monomers, with a description of the dimeric structure (spacing, orientation). The technique is modular, meaning that the co-operative effect of dimerization is made explicit by evaluating the difference between expected and observed models. The model is validated using HT-SELEX and generated datasets, and by comparing to some earlier PPM and ADM techniques. The ADM models explain data slightly better than PPM models for 314 tested TFs (or their DNA-binding domains) from four families (bHLH, bZIP, ETS and Homeodomain), the ADM mixture models by MODER2 being the best on average. Availability and implementation Software implementation is available from https://github.com/jttoivon/moder2. Supplementary information Supplementary data are available at Bioinformatics online.

Dynamic network intelligent hybrid recommendation algorithm and its application in online shopping platform

2021 ◽  
pp. 1-13
Author(s):  
Yuxuan Gao ◽  
Haiming Liang ◽  
Bingzhen Sun
Keyword(s):  
Online Shopping ◽  
Rapid Development ◽  
Dynamic Network ◽  
Topsis Method ◽  
Recommendation Algorithm ◽  
Time Periods ◽  
The Difference ◽  
Relationship Of ◽  
Hybrid Recommendation ◽  
Novel Model

With the rapid development of e-commerce, whether network intelligent recommendation can attract customers has become a measure of customer retention on online shopping platforms. In the literature about network intelligent recommendation, there are few studies that consider the difference preference of customers in different time periods. This paper proposes the dynamic network intelligent hybrid recommendation algorithm distinguishing time periods (DIHR), it is a integrated novel model combined with the DEMATEL and TOPSIS method to solved the problem of network intelligent recommendation considering time periods. The proposed method makes use of the DEMATEL method for evaluating the preference relationship of customers for indexes of merchandises, and adopt the TOPSIS method combined with intuitionistic fuzzy number (IFN) for assessing and ranking the merchandises according to the indexes. We specifically introduce the calculation steps of the proposed method, and then calculate its application in the online shopping platform.

The impact of Traditional Chinese Medicine on mitophagy in disease models

2021 ◽  
Vol 27 ◽  
Author(s):  
Li-Ping Yu ◽  
Ting-Ting Shi ◽  
Yan-Qin Li ◽  
Jian-Kang Mu ◽  
Ya-Qin Yang ◽  
...  
Keyword(s):  
Chinese Medicine ◽  
Traditional Chinese Medicine ◽  
Molecular Mechanisms ◽  
Human Diseases ◽  
Regulatory Mechanisms ◽  
Disease Models ◽  
Relationship Of ◽  
The Impact ◽  
The Relationship

: Mitophagy plays an important role in maintaining mitochondrial quality and cell homeostasis through the degradation of damaged, aged, and dysfunctional mitochondria and misfolded proteins. Many human diseases, particularly neurodegenerative diseases, are related to disorders of mitochondrial phagocytosis. Exploring the regulatory mechanisms of mitophagy is of great significance for revealing the molecular mechanisms underlying the related diseases. Herein, we summarize the major mechanisms of mitophagy, the relationship of mitophagy with human diseases, and the role of traditional Chinese medicine (TCM) in mitophagy. These discussions enhance our knowledge of mitophagy and its potential therapeutic targets using TCM.

SMILE: Mutual Information Learning for Integration of Single-cell Omics Data

2021 ◽  
Author(s):  
Yang Xu ◽  
Priyojit Das ◽  
Rachel Patton McCord
Keyword(s):  
Deep Learning ◽  
Mutual Information ◽  
Single Cell ◽  
Learning Algorithm ◽  
Cellular Systems ◽  
Supplementary Information ◽  
Omics Data ◽  
Learning Approaches ◽  
Rna Seq ◽  
Integrate Data

Abstract Motivation Deep learning approaches have empowered single-cell omics data analysis in many ways and generated new insights from complex cellular systems. As there is an increasing need for single cell omics data to be integrated across sources, types, and features of data, the challenges of integrating single-cell omics data are rising. Here, we present an unsupervised deep learning algorithm that learns discriminative representations for single-cell data via maximizing mutual information, SMILE (Single-cell Mutual Information Learning). Results Using a unique cell-pairing design, SMILE successfully integrates multi-source single-cell transcriptome data, removing batch effects and projecting similar cell types, even from different tissues, into the shared space. SMILE can also integrate data from two or more modalities, such as joint profiling technologies using single-cell ATAC-seq, RNA-seq, DNA methylation, Hi-C, and ChIP data. When paired cells are known, SMILE can integrate data with unmatched feature, such as genes for RNA-seq and genome wide peaks for ATAC-seq. Integrated representations learned from joint profiling technologies can then be used as a framework for comparing independent single source data. Supplementary information Supplementary data are available at Bioinformatics online. The source code of SMILE including analyses of key results in the study can be found at: https://github.com/rpmccordlab/SMILE.

The Performance of Sailing Yachts in Oblique Seas

1974 ◽  
Vol 11 (04) ◽  
pp. 383-392
Author(s):  
David R. Pedrick
Keyword(s):  
Wave Length ◽  
Added Resistance ◽  
Regular Waves ◽  
Sea State ◽  
Hull Form ◽  
Rough Water ◽  
Rational Procedure ◽  
The Difference ◽  
Relationship Of ◽  
The Relationship

The difference in the effects of rough water on similar sailing yachts has been one of the intriguing puzzles that sailors, designers, and researchers have long tried to understand. It is not uncommon for two yachts of equal performance in smooth-sea conditions to have their speed or pointing ability reduced by different amounts when encountering waves. To investigate the causes of such behavior, it is important to have a rational procedure to analyze how changes in hull form, weight distribution, rig, and other design features affect the speed and motions of sailing yachts. This paper discusses the relationship of wind to rough water and of motions and added resistance to wave length and height. It then describes a procedure to predict motions, sailing speed, and speed-made-good to windward in realistic windward sailing conditions. The procedure utilizes results of heeled and yawed model tests of 12-metre yachts in oblique regular waves to predict performance in a Pierson-Moskowitz sea state corresponding closely to the equilibrium true wind speed.

Organizational Support for Professionalism, Organizational Citizenship Behavior (OCB), and Performance

2021 ◽  
Vol 10 (1) ◽  
pp. 1-12
Author(s):  
Noviana Norrohmat ◽  
Umar Nimran ◽  
Kusdi Raharjo ◽  
Hamidah Nayati Utami ◽  
Endang Siti Astuti
Keyword(s):  
Organizational Citizenship ◽  
Organizational Citizenship Behavior ◽  
Organizational Support ◽  
Citizenship Behavior ◽  
Research Approach ◽  
The Difference ◽  
And Performance ◽  
Relationship Of ◽  
Descriptive Survey ◽  
The Relationship

The purpose of this research is to determine the organizational support for professionalism that has never been done before. The research approach is to conceptualize the structure of the relationship of variables from a study. Verification research is to test the hypothesis through data collection in the field using two methods, namely descriptive survey and explanatory survey. The use of both methods aims to analyze the causality relationship between research variables in accordance with the hypothesis quantitatively. There is significant influence between the variables of organizational support to professional variables. However, different results are found on the influence of organizational support variables on OCB and performance that have no significant effect. There is also an indirect influence between organizational support variables on OCB and performance through intermediary intervening professionalism variables. The difference between this research and the previous research are the use of constructs and the measurement in the unit of analysis being used.

MetaADEDB 2.0: a comprehensive database on adverse drug events

2020 ◽  
Author(s):  
Zhuohang Yu ◽  
Zengrui Wu ◽  
Weihua Li ◽  
Guixia Liu ◽  
Yun Tang
Keyword(s):  
Safety Assessment ◽  
Adverse Drug Events ◽  
Adverse Event Reporting System ◽  
Adverse Event Reporting ◽  
Supplementary Information ◽  
Online Database ◽  
Web Interface ◽  
Drug Discovery And Development ◽  
Comprehensive Information ◽  
User Friendly

Abstract Summary MetaADEDB is an online database we developed to integrate comprehensive information on adverse drug events (ADEs). The first version of MetaADEDB was released in 2013 and has been widely used by researchers. However, it has not been updated for more than seven years. Here, we reported its second version by collecting more and newer data from the U.S. FDA Adverse Event Reporting System (FAERS) and Canada Vigilance Adverse Reaction Online Database, in addition to the original three sources. The new version consists of 744 709 drug–ADE associations between 8498 drugs and 13 193 ADEs, which has an over 40% increase in drug–ADE associations compared to the previous version. Meanwhile, we developed a new and user-friendly web interface for data search and analysis. We hope that MetaADEDB 2.0 could provide a useful tool for drug safety assessment and related studies in drug discovery and development. Availability and implementation The database is freely available at: http://lmmd.ecust.edu.cn/metaadedb/. Supplementary information Supplementary data are available at Bioinformatics online.

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