scholarly journals Recent Advances in Stereochemistry Reveal Classification Shortcomings

2020 ◽  
Author(s):  
Peter J. Canfield ◽  
Linda J. Govenlock ◽  
Jeffrey Reimers ◽  
Maxwell J. Crossley
Keyword(s):  
Machine Learning ◽  
Fundamental Form ◽  
Learning Approaches ◽  
Software Packages ◽  
Recent Advances ◽  
Chemical Design ◽  
Polytope Model

We contend that the Polytope model utilized by IUPAC to specify stereoisomerism for species ML<i><sub>n</sub></i> with <i>n</i> > 3 should be universally applied. Such application recently led to the synthesis of isolable compounds displaying a new fundamental form of isomerism, akamptisomerism, pertinent to ML<sub>2</sub> stereocenters. We review 443807 molecules that could be classified as akamptisomers. Some akamptisomers are described as being “wrong” by existing IUPAC rules, hindering molecular conception. For many classes of medicinal and technology-related molecules, software packages like ChemDraw mostly do not handle akamptisomers correctly, databases such as CAS provide 2D representations inconsistent with those presented in the original publications, and often the akamptisomeric identity of compounds remains unknown. These features hinder both human and machine-learning approaches to chemical design. Further, the existence of previously unrecognized isomeric forms has broad implications for patents and pharmaceutical-registration requirements. Hence, the immediate re-examination of stereochemistry is demanded.

scholarly journals Recent Advances in Stereochemistry Reveal Classification Shortcomings

2020 ◽  
Author(s):  
Peter J. Canfield ◽  
Linda J. Govenlock ◽  
Jeffrey Reimers ◽  
Maxwell J. Crossley
Keyword(s):  
Machine Learning ◽  
Fundamental Form ◽  
Learning Approaches ◽  
Software Packages ◽  
Recent Advances ◽  
Chemical Design ◽  
Polytope Model

We contend that the Polytope model utilized by IUPAC to specify stereoisomerism for species ML<i><sub>n</sub></i> with <i>n</i> > 3 should be universally applied. Such application recently led to the synthesis of isolable compounds displaying a new fundamental form of isomerism, akamptisomerism, pertinent to ML<sub>2</sub> stereocenters. We review 443807 molecules that could be classified as akamptisomers. Some akamptisomers are described as being “wrong” by existing IUPAC rules, hindering molecular conception. For many classes of medicinal and technology-related molecules, software packages like ChemDraw mostly do not handle akamptisomers correctly, databases such as CAS provide 2D representations inconsistent with those presented in the original publications, and often the akamptisomeric identity of compounds remains unknown. These features hinder both human and machine-learning approaches to chemical design. Further, the existence of previously unrecognized isomeric forms has broad implications for patents and pharmaceutical-registration requirements. Hence, the immediate re-examination of stereochemistry is demanded.

A Review of Recent Advances in Machine Learning Approaches for Cyber Defense

2021 ◽  
Author(s):  
Ricardo Buettner ◽  
Daniel Sauter ◽  
Jonas Klopfer ◽  
Johannes Breitenbach ◽  
Hermann Baumgartl
Keyword(s):  
Machine Learning ◽  
Learning Approaches ◽  
Cyber Defense ◽  
Recent Advances

scholarly journals Machine Learning and Deep Learning Approaches for Brain Disease Diagnosis: Principles and Recent Advances

IEEE Access ◽  
2021 ◽  
Vol 9 ◽  
pp. 37622-37655
Author(s):  
Protima Khan ◽  
Md. Fazlul Kader ◽  
S. M. Riazul Islam ◽  
Aisha B. Rahman ◽  
Md. Shahriar Kamal ◽  
...  
Keyword(s):  
Machine Learning ◽  
Deep Learning ◽  
Disease Diagnosis ◽  
Brain Disease ◽  
Learning Approaches ◽  
Recent Advances

scholarly journals Grey matter biomarker identification in Schizophrenia: detecting regional alterations and their underlying substrates

2018 ◽  
Author(s):  
V. Chatzi ◽  
R.P. Teixeira ◽  
J. Shawe-Taylor ◽  
A. Altmann ◽  
O. O’Daly ◽  
...  
Keyword(s):  
Machine Learning ◽  
Grey Matter ◽  
Structural Mri ◽  
Brain Regions ◽  
List Type ◽  
Healthy Controls ◽  
Learning Approaches ◽  
Learning Framework ◽  
Software Packages ◽  
Biological Substrates

AbstractState-of-the-art approaches in Schizophrenia research investigate neuroanatomical biomarkers using structural Magnetic Resonance Imaging. However, current models are 1) voxel-wise, 2) difficult to interpret in biologically meaningful ways, and 3) difficult to replicate across studies. Here, we propose a machine learning framework that enables the identification of sparse, region-wise grey matter neuroanatomical biomarkers and their underlying biological substrates by integrating well-established statistical and machine learning approaches. We address the computational issues associated with application of machine learning on structural MRI data in Schizophrenia, as discussed in recent reviews, while promoting transparent science using widely available data and software. In this work, a cohort of patients with Schizophrenia and healthy controls was used. It was found that the cortical thickness in left pars orbitalis seems to be the most reliable measure for distinguishing patients with Schizophrenia from healthy controls.HighlightsWe present a sparse machine learning framework to identify biologically meaningful neuroanatomical biomarkers for SchizophreniaOur framework addresses methodological pitfalls associated with application of machine learning on structural MRI data in Schizophrenia raised by several recent reviewsOur pipeline is easy to replicate using widely available software packagesThe presented framework is geared towards identification of specific changes in brain regions that relate directly to the pathology rather than classification per se

scholarly journals EXPLAIN IT TO ME – FACING REMOTE SENSING CHALLENGES IN THE BIO- AND GEOSCIENCES WITH EXPLAINABLE MACHINE LEARNING

2020 ◽  
Vol V-3-2020 ◽  
pp. 817-824
Author(s):  
R. Roscher ◽  
B. Bohn ◽  
M. F. Duarte ◽  
J. Garcke
Keyword(s):  
Machine Learning ◽  
Remote Sensing ◽  
Neural Networks ◽  
Simulated Data ◽  
High Accuracy ◽  
Learning Approaches ◽  
Learning Methods ◽  
Machine Learning Methods ◽  
Recent Advances ◽  
Post Hoc

Abstract. For some time now, machine learning methods have been indispensable in many application areas. Especially with the recent development of efficient neural networks, these methods are increasingly used in the sciences to obtain scientific outcomes from observational or simulated data. Besides a high accuracy, a desired goal is to learn explainable models. In order to reach this goal and obtain explanation, knowledge from the respective domain is necessary, which can be integrated into the model or applied post-hoc. We discuss explainable machine learning approaches which are used to tackle common challenges in the bio- and geosciences, such as limited amount of labeled data or the provision of reliable and scientific consistent results. We show that recent advances in machine learning to enhance transparency, interpretability, and explainability are helpful in overcoming these challenges.

scholarly journals Calcium imaging analysis – how far have we come?

F1000Research ◽  
2021 ◽  
Vol 10 ◽  
pp. 258
Author(s):  
Miranda Robbins ◽  
Charles N. Christensen ◽  
Clemens F. Kaminski ◽  
Marta Zlatic
Keyword(s):  
Machine Learning ◽  
Image Analysis ◽  
Future Development ◽  
Calcium Imaging ◽  
Cellular Activity ◽  
Imaging Analysis ◽  
Signal To Noise ◽  
The Past ◽  
Software Packages ◽  
Recent Advances

Techniques for calcium imaging were first achieved in the mid-1970s, whilst tools to analyse these markers of cellular activity are still being developed and improved. For image analysis, custom tools were developed within labs and until relatively recently, software packages were not widely available between researchers. We will discuss some of the most popular, alongside our preferred, methods for calcium imaging analysis that are now widely available and describe why these protocols are so effective. We will also describe some of the newest innovations in the field that are likely to benefit researchers, particularly as calcium imaging is often an inherently low signal-to-noise method. Although calcium imaging analysis has seen recent advances, particularly following the rise of machine learning, we will end by highlighting the outstanding requirements and questions that hinder further progress, and pose the question of how far we have come in the past sixty years and what can be expected for future development in the field.

scholarly journals Calcium imaging analysis – how far have we come?

F1000Research ◽  
2021 ◽  
Vol 10 ◽  
pp. 258
Author(s):  
Miranda Robbins ◽  
Charles N. Christensen ◽  
Clemens F. Kaminski ◽  
Marta Zlatic
Keyword(s):  
Machine Learning ◽  
Image Analysis ◽  
Future Development ◽  
Calcium Imaging ◽  
Cellular Activity ◽  
Imaging Analysis ◽  
Signal To Noise ◽  
The Past ◽  
Software Packages ◽  
Recent Advances

Techniques for calcium imaging were first demonstrated in the mid-1970s, whilst tools to analyse these markers of cellular activity are still being developed and improved today. For image analysis, custom tools were developed within labs and until relatively recently, software packages were not widely available between researchers. We will discuss some of the most popular methods for calcium imaging analysis that are now widely available and describe why these protocols are so effective. We will also describe some of the newest innovations in the field that are likely to benefit researchers, particularly as calcium imaging is often an inherently low signal-to-noise method. Although calcium imaging analysis has seen recent advances, particularly following the rise of machine learning, we will end by highlighting the outstanding requirements and questions that hinder further progress and pose the question of how far we have come in the past sixty years and what can be expected for future development in the field.

Multiple vehicles detection and tracking for intelligent transport systems using machine learning approaches

2019 ◽  
Vol 70 (3) ◽  
pp. 214-224
Author(s):  
Bui Ngoc Dung ◽  
Manh Dzung Lai ◽  
Tran Vu Hieu ◽  
Nguyen Binh T. H.
Keyword(s):  
Machine Learning ◽  
Gaussian Mixture ◽  
Research Field ◽  
Transport Systems ◽  
Learning Approaches ◽  
Subtraction Method ◽  
Intelligent Transport Systems ◽  
Intelligent Transport ◽  
Detection And Tracking ◽  
Multiple Vehicles

Video surveillance is emerging research field of intelligent transport systems. This paper presents some techniques which use machine learning and computer vision in vehicles detection and tracking. Firstly the machine learning approaches using Haar-like features and Ada-Boost algorithm for vehicle detection are presented. Secondly approaches to detect vehicles using the background subtraction method based on Gaussian Mixture Model and to track vehicles using optical flow and multiple Kalman filters were given. The method takes advantages of distinguish and tracking multiple vehicles individually. The experimental results demonstrate high accurately of the method.

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