scholarly journals Candidate Explorer: a tool for discovery, evaluation, and display of mutations causing significant immune phenotypes

2020 ◽  
Author(s):  
Darui Xu ◽  
Stephen Lyon ◽  
Chun Hui Bu ◽  
Sara Hildebrand ◽  
Jin Huk Choi ◽  
...  
Keyword(s):  
Machine Learning ◽  
Software Tool ◽  
Linkage Data ◽  
Genetic Studies ◽  
Flow Cytometric ◽  
Linkage Information ◽  
Meiotic Mapping ◽  
Immune Phenotypes ◽  
Learning Software ◽  
Selection Of

AbstractWhen applied to immunity, forward genetic studies use meiotic mapping to provide strong statistical evidence that a particular mutation is causative of a particular immune phenotype. Notwithstanding this, co-segregation of multiple mutations, occasional unawareness of mutations, and paucity of homozygotes may lead to erroneous declarations of cause and effect. We sought to improve the selection of authentic causative mutations using a machine learning software tool, Candidate Explorer (CE), which integrates 65 data features into a single numeric score, mathematically convertible to the likelihood of verification of any putative mutation-phenotype association. CE has identified most genes within which mutations can be causative of flow cytometric phenovariation in Mus musculus. The majority of these genes were not previously known to support immune function or homeostasis. Mouse geneticists will find CE data informative in identifying causative mutations within quantitative trait loci, while clinical geneticists may use CE to help connect causative variants with rare heritable diseases of immunity, even in the absence of linkage information. CE displays integrated mutation, phenotype, and linkage data, and is freely available for query online.

scholarly journals Thousands of induced germline mutations affecting immune cells identified by automated meiotic mapping coupled with machine learning

2021 ◽  
Vol 118 (28) ◽  
pp. e2106786118
Author(s):  
Darui Xu ◽  
Stephen Lyon ◽  
Chun Hui Bu ◽  
Sara Hildebrand ◽  
Jin Huk Choi ◽  
...  
Keyword(s):  
Machine Learning ◽  
Immune Cells ◽  
Linkage Data ◽  
Mapping Data ◽  
Genetic Studies ◽  
Flow Cytometric ◽  
Linkage Information ◽  
Meiotic Mapping ◽  
Immune Phenotypes ◽  
Learning Software

Forward genetic studies use meiotic mapping to adduce evidence that a particular mutation, normally induced by a germline mutagen, is causative of a particular phenotype. Particularly in small pedigrees, cosegregation of multiple mutations, occasional unawareness of mutations, and paucity of homozygotes may lead to erroneous declarations of cause and effect. We sought to improve the identification of mutations causing immune phenotypes in mice by creating Candidate Explorer (CE), a machine-learning software program that integrates 67 features of genetic mapping data into a single numeric score, mathematically convertible to the probability of verification of any putative mutation–phenotype association. At this time, CE has evaluated putative mutation–phenotype associations arising from screening damaging mutations in ∼55% of mouse genes for effects on flow cytometry measurements of immune cells in the blood. CE has therefore identified more than half of genes within which mutations can be causative of flow cytometric phenovariation in Mus musculus. The majority of these genes were not previously known to support immune function or homeostasis. Mouse geneticists will find CE data informative in identifying causative mutations within quantitative trait loci, while clinical geneticists may use CE to help connect causative variants with rare heritable diseases of immunity, even in the absence of linkage information. CE displays integrated mutation, phenotype, and linkage data, and is freely available for query online.

A Prolegomenon to Partial Deduction

1993 ◽  
Vol 18 (1) ◽  
pp. 41-64
Author(s):  
Jan Komorowski
Keyword(s):  
Machine Learning ◽  
Logic Programming ◽  
Deductive Databases ◽  
Software Synthesis ◽  
Open Problems ◽  
Partial Deduction ◽  
The Law ◽  
Learning Software ◽  
Selection Of

Partial deduction is a specialization principle related to the law of syllogism. It has several computational applications in logic programming but it has been recently also used in deductive databases, machine learning, software synthesis and other areas of computing. This article is a systematic introduction to partial deduction, its applications and open problems. Starting from an informal and intuitive presentation, the fundamental notions such as correctness and completeness are discussed. A selection of applications is presented to illustrate partial deduction in different contexts.

Deep Learning in Disease Diagnosis: Models and Datasets

2020 ◽  
Vol 15 ◽  
Author(s):  
Deeksha Saxena ◽  
Mohammed Haris Siddiqui ◽  
Rajnish Kumar
Keyword(s):  
Biological Sciences ◽  
Machine Learning ◽  
Deep Learning ◽  
Disease Diagnosis ◽  
Learning Models ◽  
Data Types ◽  
Related Data ◽  
Abstract Level ◽  
Experimental Validations ◽  
Selection Of

Background: Deep learning (DL) is an Artificial neural network-driven framework with multiple levels of representation for which non-linear modules combined in such a way that the levels of representation can be enhanced from lower to a much abstract level. Though DL is used widely in almost every field, it has largely brought a breakthrough in biological sciences as it is used in disease diagnosis and clinical trials. DL can be clubbed with machine learning, but at times both are used individually as well. DL seems to be a better platform than machine learning as the former does not require an intermediate feature extraction and works well with larger datasets. DL is one of the most discussed fields among the scientists and researchers these days for diagnosing and solving various biological problems. However, deep learning models need some improvisation and experimental validations to be more productive. Objective: To review the available DL models and datasets that are used in disease diagnosis. Methods: Available DL models and their applications in disease diagnosis were reviewed discussed and tabulated. Types of datasets and some of the popular disease related data sources for DL were highlighted. Results: We have analyzed the frequently used DL methods, data types and discussed some of the recent deep learning models used for solving different biological problems. Conclusion: The review presents useful insights about DL methods, data types, selection of DL models for the disease diagnosis.

Color Trend Analysis using Machine Learning with Fashion Collection Images

2021 ◽  
pp. 0887302X2199594
Author(s):  
Ahyoung Han ◽  
Jihoon Kim ◽  
Jaehong Ahn
Keyword(s):  
Machine Learning ◽  
Trend Analysis ◽  
Image Data ◽  
Fashion Industry ◽  
Color Palette ◽  
Web Scraping ◽  
Design Variables ◽  
Sales Organizations ◽  
Fashion Designers ◽  
Selection Of

Fashion color trends are an essential marketing element that directly affect brand sales. Organizations such as Pantone have global authority over professional color standards by annually forecasting color palettes. However, the question remains whether fashion designers apply these colors in fashion shows that guide seasonal fashion trends. This study analyzed image data from fashion collections through machine learning to obtain measurable results by web-scraping catwalk images, separating body and clothing elements via machine learning, defining a selection of color chips using k-means algorithms, and analyzing the similarity between the Pantone color palette (16 colors) and the analysis color chips. The gap between the Pantone trends and the colors used in fashion collections were quantitatively analyzed and found to be significant. This study indicates the potential of machine learning within the fashion industry to guide production and suggests further research expand on other design variables.

Machine learning approaches to understand and predict rate constants for organic processes in mixtures containing ionic liquids

2021 ◽  
Vol 23 (4) ◽  
pp. 2742-2752
Author(s):  
Tamar L. Greaves ◽  
Karin S. Schaffarczyk McHale ◽  
Raphael F. Burkart-Radke ◽  
Jason B. Harper ◽  
Tu C. Le
Keyword(s):  
Machine Learning ◽  
Ionic Liquids ◽  
Rate Constants ◽  
Learning Approaches ◽  
Learning Models ◽  
Organic Reaction ◽  
Machine Learning Models ◽  
Selection Of

Machine learning models were developed for an organic reaction in ionic liquids and validated on a selection of ionic liquids.

Accelerating the optimization of enzyme-catalyzed synthesis conditions via machine learning and reactivity descriptors

2021 ◽  
Author(s):  
Zhongyu Wan ◽  
Quan-De Wang ◽  
Dongchang Liu ◽  
Jinhu Liang
Keyword(s):  
Machine Learning ◽  
Optimal Synthesis ◽  
Human Knowledge ◽  
Crucial Importance ◽  
Reactivity Descriptors ◽  
Synthesis Conditions ◽  
Wide Range ◽  
Synthesis Reactions ◽  
Selection Of

Enzyme-catalyzed synthesis reactions are of crucial importance for a wide range of applications. An accurate and rapid selection of optimal synthesis conditions is crucial and challenging for both human knowledge...

scholarly journals Selection of Suitable Machine Learning Algorithms for Classification Tasks in Reverse Logistics

Procedia CIRP ◽  
2021 ◽  
Vol 96 ◽  
pp. 272-277
Author(s):  
Hannah Lickert ◽  
Aleksandra Wewer ◽  
Sören Dittmann ◽  
Pinar Bilge ◽  
Franz Dietrich
Keyword(s):  
Machine Learning ◽  
Reverse Logistics ◽  
Learning Algorithms ◽  
Machine Learning Algorithms ◽  
Classification Tasks ◽  
Selection Of

scholarly journals Machine learning, transcriptome, and genotyping chip analyses provide insights into SNP markers identifying flower color in Platycodon grandiflorus

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Go-Eun Yu ◽  
Younhee Shin ◽  
Sathiyamoorthy Subramaniyam ◽  
Sang-Ho Kang ◽  
Si-Myung Lee ◽  
...  
Keyword(s):  
Machine Learning ◽  
Random Forest ◽  
Feature Selection Method ◽  
Flower Color ◽  
Classification Performance ◽  
Snp Markers ◽  
Rna Seq ◽  
Color Classification ◽  
Dna Pcr ◽  
Selection Of

AbstractBellflower is an edible ornamental gardening plant in Asia. For predicting the flower color in bellflower plants, a transcriptome-wide approach based on machine learning, transcriptome, and genotyping chip analyses was used to identify SNP markers. Six machine learning methods were deployed to explore the classification potential of the selected SNPs as features in two datasets, namely training (60 RNA-Seq samples) and validation (480 Fluidigm chip samples). SNP selection was performed in sequential order. Firstly, 96 SNPs were selected from the transcriptome-wide SNPs using the principal compound analysis (PCA). Then, 9 among 96 SNPs were later identified using the Random forest based feature selection method from the Fluidigm chip dataset. Among six machines, the random forest (RF) model produced higher classification performance than the other models. The 9 SNP marker candidates selected for classifying the flower color classification were verified using the genomic DNA PCR with Sanger sequencing. Our results suggest that this methodology could be used for future selection of breeding traits even though the plant accessions are highly heterogeneous.

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