scholarly journals Constructing Unrooted Phylogenetic Trees with Reinforcement Learning

2021 ◽  
Vol 66 (1) ◽  
pp. 37
Author(s):  
P. Liptak ◽  
A. Kiss
Keyword(s):  
Machine Learning ◽  
Reinforcement Learning ◽  
Phylogenetic Trees ◽  
New Technologies ◽  
Sequence Data ◽  
Evolutionary Relationship ◽  
Distance Matrix ◽  
Sequencing Technologies ◽  
Reinforced Learning ◽  
The Relationship

With the development of sequencing technologies, more and more amounts of sequence data are available. This poses additional challenges, such as processing them is usually a complex and time-consuming computational task. During the construction of phylogenetic trees, the relationship between the sequences is examined, and an attempt is made to represent the evolutionary relationship. There are several algorithms for this problem, but with the development of computer science, the question arises as to whether new technologies can be exploited in these areas of computational biology. In the following publication, we investigate whether the reinforced learning model of machine learning can generate accurate phylogenetic trees based on the distance matrix.

Molecular characterisation of some species of Mylonchulus (Nematoda: Mononchida) from Japan and comments on the status of Paramylonchulus and Pakmylonchulus

Nematology ◽  
2009 ◽  
Vol 11 (3) ◽  
pp. 337-342 ◽  
Author(s):  
Masaaki Araki ◽  
Wasim Ahmad ◽  
Majid Olia ◽  
Nobuhiro Minaka
Keyword(s):  
Phylogenetic Trees ◽  
Sequence Data ◽  
Preliminary Investigation ◽  
Sister Group ◽  
Small Subunit ◽  
Ssu Rdna ◽  
Rdna Sequences ◽  
The Status ◽  
Molecular Relationships ◽  
The Relationship

AbstractComparative analyses of different regions of ribosomal DNA have become a popular tool in understanding the relationship among different species and genera and nematodes are no exception to this. In this study, molecular relationships were inferred from a nearly complete small subunit (SSU) of total 16 OTUs for five species of Mylonchulus, Paramylonchulus and Pakmylonchulus collected from various parts of Japan with two out-group taxa (Mononchus aquaticus and Clarkus papillatus) to examine the relationship among these species. Out of 1685 bp SSU rDNA sequences, phylogenetic trees using distance (NJ), parsimony and likelihood algorithms were performed. Obtained tree topologies were stable across algorithms and sequence data show that populations of the same species clustered together and four out of five species (M. brachyuris, M. hawaiiensis, M. oceanicus, M. sigmaturus) formed a monophyletic assemblage while M. mulveyi formed a sister group. Populations of species lacking subventral teeth but with a double gonad (M. oceanicus) stand with other Mylonchulus species, thereby confirming the synonymy of Pakmylonchulus, while populations with a narrow buccal cavity with few rows of denticles, no subventral teeth and a single prodelphic gonad (M. mulveyi = Paramylonchulus mulveyi) support to some extent the validity of the genus Paramylonchulus. Though a preliminary investigation, it is the first report on molecular relationships in Mylonchulus, probably a paraphyletic genus. Our results suggest that SSU rDNA sequence data are useful in understanding the relationship between genera and species.

scholarly journals Changing ideas about eukaryotic origins

2015 ◽  
Vol 370 (1678) ◽  
pp. 20140318 ◽  
Author(s):  
Tom A. Williams ◽  
T. Martin Embley
Keyword(s):  
Phylogenetic Trees ◽  
Recent Progress ◽  
New Technologies ◽  
Genomic Data ◽  
Eukaryotic Cells ◽  
Theme Issue ◽  
Statistical Approaches ◽  
Relationship Of ◽  
The Relationship ◽  
Improved Methods

The origin of eukaryotic cells is one of the most fascinating challenges in biology, and has inspired decades of controversy and debate. Recent work has led to major upheavals in our understanding of eukaryotic origins and has catalysed new debates about the roles of endosymbiosis and gene flow across the tree of life. Improved methods of phylogenetic analysis support scenarios in which the host cell for the mitochondrial endosymbiont was a member of the Archaea, and new technologies for sampling the genomes of environmental prokaryotes have allowed investigators to home in on closer relatives of founding symbiotic partners. The inference and interpretation of phylogenetic trees from genomic data remains at the centre of many of these debates, and there is increasing recognition that trees built using inadequate methods can prove misleading, whether describing the relationship of eukaryotes to other cells or the root of the universal tree. New statistical approaches show promise for addressing these questions but they come with their own computational challenges. The papers in this theme issue discuss recent progress on the origin of eukaryotic cells and genomes, highlight some of the ongoing debates, and suggest possible routes to future progress.

scholarly journals A target enrichment probe set for resolving the flagellate plant tree of life

2020 ◽  
Author(s):  
Jesse W. Breinholt ◽  
Sarah B. Carey ◽  
George P. Tiley ◽  
E. Christine Davis ◽  
Lorena Endara ◽  
...  
Keyword(s):  
Phylogenetic Relationships ◽  
Phylogenetic Trees ◽  
Large Scale ◽  
Sequence Data ◽  
Low Cost ◽  
Tree Of Life ◽  
Target Enrichment ◽  
Sequencing Technologies ◽  
Flanking Regions ◽  
Probe Set

ABSTRACTPremise of the studyNew sequencing technologies enable the possibility of generating large-scale molecular datasets for constructing the plant tree of life. We describe a new probe set for target enrichment sequencing to generate nuclear sequence data to build phylogenetic trees with any flagellate plants, comprising hornworts, liverworts, mosses, lycophytes, ferns, and gymnosperms.Methods and ResultsWe leveraged existing transcriptome and genome sequence data to design a set of 56,989 probes for target enrichment sequencing of 451 nuclear exons and non-coding flanking regions across flagellate plant lineages. We describe the performance of target enrichment using the probe set across flagellate plants and demonstrate the potential of the data to resolve relationships among both ancient and closely related taxa.ConclusionsA target enrichment approach using the new probe set provides a relatively low-cost solution to obtain large-scale nuclear sequence data for inferring phylogenetic relationships across flagellate plants.

scholarly journals Determining a novel feature-space for SARS-CoV-2 sequence data

2020 ◽  
Author(s):  
Francesco Ballesio ◽  
Ali Haider Bangash ◽  
Didier Barradas-Bautista ◽  
Justin Barton ◽  
Andrea Guarracino ◽  
...  
Keyword(s):  
Machine Learning ◽  
Feature Extraction ◽  
Mhc Class I ◽  
Phylogenetic Trees ◽  
Data Science ◽  
Sequence Data ◽  
Protein Sequences ◽  
Feature Space ◽  
Future Research ◽  
Alignment Free

The pandemicity & the ability of the SARS-COV-2 to reinfect a cured subject, among other damaging characteristics of it, took everybody by surprise. A global collaborative scientific effort was direly required to bring learned people from different niches of medicine & data science together. Such a platform was provided by COVID19 Virtual BioHackathon, organized from the 5th to the 11th of April, 2020, to ponder on the related pressing issues varying in their diversity from text mining to genomics. Under the "Machine learning" track, we determined optimal k-mer length for feature extraction, constructed continuous distributed representations for protein sequences to create phylogenetic trees in an alignment-free manner, and clustered predicted MHC class I and II binding affinity to aid in vaccine design. All the related work in available in a Github repository under an MIT license for future research.

scholarly journals Machine learning prediction of Antibody-Antigen binding: dataset, method and testing

2021 ◽  
Author(s):  
Chao Ye ◽  
Wenxing Hu ◽  
Bruno Gaeta
Keyword(s):  
Machine Learning ◽  
Structure Prediction ◽  
Large Scale ◽  
Nearest Neighbor ◽  
Sequence Data ◽  
Antigen Binding ◽  
Immunoglobulin Gene ◽  
Learning Program ◽  
Prediction Program ◽  
Sequencing Technologies

DNA sequencing technologies are providing new insights into the immune response by allowing the large scale sequencing of rearranged immunoglobulin gene present in an individual, however the applications of this approach are limited by the lack of methods for determining the antigen(s) that an immunoglobulin encoded by a given sequence binds to. Computational methods for predicting antibody-antigen interactions that leverage structure prediction and docking have been proposed, however these methods require knowledge of the 3D structures. As a step towards the development of a machine learning method suitable for predicting antibody-antigen binding affinities from sequence data, a weighted nearest neighbor machine learning approach was applied to the problem. A prediction program was coded in Python and evaluated using cross-validation on a dataset of 600 antibodies interacting with 50 antigens. The classification predicting accuracy was around 76% for this dataset. These results provide a useful frame of reference as well as protocols and considerations for machine learning and dataset creation in this area. Both the dataset (in csv format) and the machine learning program (coded in python) are freely available for download.

Delay Prediction of Aircrafts Based on Health Monitoring Data

2016 ◽  
Vol 4 (1) ◽  
Author(s):  
B. A. Dattaram ◽  
N. Madhusudanan
Keyword(s):  
Machine Learning ◽  
Health Monitoring ◽  
Monitoring Data ◽  
Machine Learning Techniques ◽  
Learning Techniques ◽  
Flight Delay ◽  
Delay Prediction ◽  
The Relationship

Flight delay is a major issue faced by airline companies. Delay in the aircraft take off can lead to penalty and extra payment to airport authorities leading to revenue loss. The causes for delays can be weather, traffic queues or component issues. In this paper, we focus on the problem of delays due to component issues in the aircraft. In particular, this paper explores the analysis of aircraft delays based on health monitoring data from the aircraft. This paper analyzes and establishes the relationship between health monitoring data and the delay of the aircrafts using exploratory analytics, stochastic approaches and machine learning techniques.

AI Narratives

2020 ◽  
Keyword(s):  
Artificial Intelligence ◽  
New Technologies ◽  
Ancient Greece ◽  
Historical Overview ◽  
Intelligent Machines ◽  
Narrative History ◽  
History Of ◽  
Imaginative Thinking ◽  
The Relationship ◽  
Insight Into

This book is the first to examine the history of imaginative thinking about intelligent machines. As real artificial intelligence (AI) begins to touch on all aspects of our lives, this long narrative history shapes how the technology is developed, deployed, and regulated. It is therefore a crucial social and ethical issue. Part I of this book provides a historical overview from ancient Greece to the start of modernity. These chapters explore the revealing prehistory of key concerns of contemporary AI discourse, from the nature of mind and creativity to issues of power and rights, from the tension between fascination and ambivalence to investigations into artificial voices and technophobia. Part II focuses on the twentieth and twenty-first centuries in which a greater density of narratives emerged alongside rapid developments in AI technology. These chapters reveal not only how AI narratives have consistently been entangled with the emergence of real robotics and AI, but also how they offer a rich source of insight into how we might live with these revolutionary machines. Through their close textual engagements, these chapters explore the relationship between imaginative narratives and contemporary debates about AI’s social, ethical, and philosophical consequences, including questions of dehumanization, automation, anthropomorphization, cybernetics, cyberpunk, immortality, slavery, and governance. The contributions, from leading humanities and social science scholars, show that narratives about AI offer a crucial epistemic site for exploring contemporary debates about these powerful new technologies.

Learning and control

2018 ◽  
Author(s):  
Ivan Herreros
Keyword(s):  
Machine Learning ◽  
Reinforcement Learning ◽  
Brain Function ◽  
Control Strategies ◽  
Learning Problems ◽  
Animal Learning ◽  
Feed Forward Control ◽  
Machine Learning Applications ◽  
And Control

This chapter discusses basic concepts from control theory and machine learning to facilitate a formal understanding of animal learning and motor control. It first distinguishes between feedback and feed-forward control strategies, and later introduces the classification of machine learning applications into supervised, unsupervised, and reinforcement learning problems. Next, it links these concepts with their counterparts in the domain of the psychology of animal learning, highlighting the analogies between supervised learning and classical conditioning, reinforcement learning and operant conditioning, and between unsupervised and perceptual learning. Additionally, it interprets innate and acquired actions from the standpoint of feedback vs anticipatory and adaptive control. Finally, it argues how this framework of translating knowledge between formal and biological disciplines can serve us to not only structure and advance our understanding of brain function but also enrich engineering solutions at the level of robot learning and control with insights coming from biology.

scholarly journals Geocomputing, New Technologies and Historical Analysis: Tools for a Changing Planet

Proceedings ◽  
2019 ◽  
Vol 30 (1) ◽  
pp. 9
Author(s):  
Sebastiano Trevisani
Keyword(s):  
Quantitative Analysis ◽  
Philosophy Of Science ◽  
New Technologies ◽  
Historical Analysis ◽  
Holistic Approach ◽  
Research Topic ◽  
The Other ◽  
Historical Records ◽  
The Earth ◽  
The Relationship

Modern Earth Scientists need also to interact with other disciplines, apparently far from the Earth Sciences and Engineering. Disciplines related to history and philosophy of science are emblematic from this perspective. From one side, the quantitative analysis of information extracted from historical records (documents, maps, paintings, etc.) represents an exciting research topic, requiring a truly holistic approach. On the other side, epistemological and philosophy of science considerations on the relationship between geoscience and society in history are of fundamental importance for understanding past, present and future geosphere-anthroposphere interlinked dynamics.

Living with Barren Truths: A Tale of Triumphalism and Tribulations of a Technology

2021 ◽  
pp. 097168582110159
Author(s):  
Sital Mohanty ◽  
Subhasis Sahoo ◽  
Pranay Kumar Swain
Keyword(s):  
Gender Discrimination ◽  
New Technologies ◽  
The Body ◽  
Social Scientists ◽  
Social Fact ◽  
Science Technology ◽  
The Subject ◽  
And Gender ◽  
The Relationship

Science, technology and human values have been the subject of enquiry in the last few years for social scientists and eventually the relationship between science and gender is the subject of an ongoing debate. This is due to the event of globalization which led to the exponential growth of new technologies like assisted reproductive technology (ART). ART, one of the most iconic technological innovations of the twentieth century, has become increasingly a normal social fact of life. Since ART invades multiple human discourses—thereby transforming culture, society and politics—it is important what is sociological about ART as well as what is biological. This article argues in commendation of sociology of technology, which is alert to its democratic potential but does not concurrently conceal the historical and continuing role of technology in legitimizing gender discrimination. The article draws the empirical insights from local articulations (i.e., Odisha state in eastern India) for the understandings of motherhood, freedom and choice, reproductive right and rights over the body to which ART has contributed. Sociologically, the article has been supplemented within the broader perspectives of determinism, compatibilism alongside feminism.

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