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Virtual genetic diagnosis for familial hypercholesterolemia powered by machine learning published in European Journal of Preventive Cardiology

Neural networks consist of a series of connected layers of neurons, whose connection weights are adjusted to learn how to determine the diagnosis from the input data.

Virtual genetic diagnosis for familial hypercholesterolemia powered by machine learning
Anna Pina, Saga Helgadottir, Rosellina Margherita Mancina, Chiara Pavanello, Carlo Pirazzi, Tiziana Montalcini, Roberto Henriques, Laura Calabresi, Olov Wiklund, M Paula Macedo, Luca Valenti, Giovanni Volpe, Stefano Romeo
European Journal of Preventive Cardiology (2020)
doi: https://doi.org/10.1177/2047487319898951

Aims

Familial hypercholesterolemia (FH) is the most common genetic disorder of lipid metabolism. The gold standard for FH diagnosis is genetic testing, available, however, only in selected university hospitals. Clinical scores – for example, the Dutch Lipid Score – are often employed as alternative, more accessible, albeit less accurate FH diagnostic tools. The aim of this study is to obtain a more reliable approach to FH diagnosis by a “virtual” genetic test using machine-learning approaches.

Methods and results

We used three machine-learning algorithms (a classification tree (CT), a gradient boosting machine (GBM), a neural network (NN)) to predict the presence of FH-causative genetic mutations in two independent FH cohorts: the FH Gothenburg cohort (split into training data (N = 174) and internal test (N = 74)) and the FH-CEGP Milan cohort (external test, N = 364). By evaluating their area under the receiver operating characteristic (AUROC) curves, we found that the three machine-learning algorithms performed better (AUROC 0.79 (CT), 0.83 (GBM), and 0.83 (NN) on the Gothenburg cohort, and 0.70 (CT), 0.78 (GBM), and 0.76 (NN) on the Milan cohort) than the clinical Dutch Lipid Score (AUROC 0.68 and 0.64 on the Gothenburg and Milan cohorts, respectively) in predicting carriers of FH-causative mutations.

Conclusion

In the diagnosis of FH-causative genetic mutations, all three machine-learning approaches we have tested outperform the Dutch Lipid Score, which is the clinical standard. We expect these machine-learning algorithms to provide the tools to implement a virtual genetic test of FH. These tools might prove particularly important for lipid clinics without access to genetic testing.

Seminar on polymers under local active forces: a simplified stochastic model for motor-induced translocations by Laura Natali from Università di Roma “La Sapienza”, Nexus, 22 January 2020

Polymers under local active forces: a simplified stochastic model for motor-induced translocations.
Seminar by Laura Natali from the University of Rome “La Sapienza”, Rome, Italy.

Molecular motors are a wide class of enzymes that can transport even large macromolecules by converting chemical into mechanical energy, through the process of ATP-hydrolysis. Among those, active nanopore translocation is a common mechanism to carry proteins across biological membranes. The phospholipid bilayer is impenetrable for the folded protein, that requires to be unfolded and pulled across channels such as alpha-hemolysin [1]. Exploiting the translocation process, we can acquire information about the target proteins’ structure, such as the intermediate configurations between the folded and denatured structures [2]. Here follows the interest in modeling the motor proteins complex in a simple simulation setup.
First of all, we characterized the active polymer’s features in free space and subsequently, we confined it inside a nanopore model. The study in free space aims to investigate how activity affects both the global and the local polymer properties. The chosen model for the active force is an Active Ornstein-Uhlenbeck Particle, a model closely related to Active Brownian Particles [3]. The presence of the catalytic head affects the end-to-end distance of the polymer, which describes its degree of compactness. The active chain can be studied through the Rouse mode decomposition [4], which allowed us to analyze the dependence of the second moment of the end-to-end distance as a function of the persistence time of the activity. We considered also the distance between consecutive monomers, which provides an insight into the local effects of the active force and how it is transmitted along the chain.
In this work, we were inspired by an experiment employing the ClpXP complex [5], a protein-degradation machine responsible for unfolding and digesting malfunctioning proteins through unidirectional transport across the nanopore. In the model we propose, we enclose the polymer chain in a confining potential, simulating the effect of the nanopore. The energy generated by the molecular motor translocates the polymers, which means they cross the pore from side to side. In our setup, the effect of the molecular motor [6] is represented as a potential barrier combined with a region that makes active the monomers that are crossing it. The translocation pathways have a step-wise profile typical of their biological counterparts.

References:

  1. J. Nivala, D. B. Marks and M. Akeson, Nature Biotechnol., 2013, 31, 247
  2. M. Bacci, M. Chinappi, C. M. Casciola and F. Cecconi, J. Phys.Chem. B, 2012, 116, 4255–4262
  3. L. Caprini and U. M. B. Marconi, Soft matter, 2018, 14, 9044–9054.
  4. P. E. Rouse, J. Chem. Phys., 1953, 21, 1272.
  5. T. A. Baker and R. T. Sauer, Biochimica et Biophysica Acta (BBA) – Molecular Cell Research, 2012, 1823, 15–28.
  6. L. Caprini, U. Marini Bettolo Marconi, A. Puglisi and A. Vulpiani, J. Chemi. Phys., 2019, 150, 024902.

Place: Nexus room, Fysik Origo, Fysik
Time: 22 January, 2020, 11:00

Hillevi Wachtmeister joins the Soft Matter Lab

Hillevi Wachtmeister joined the Soft Matter Lab on 21 January 2020.

Hillevi Wachtmeister is a Master student in Physics at Chalmers University of Technology.

During her Master thesis work she will work on characterizing and tracking different particles and micro organisms using deep learning. She is supervised by Daniel Midtvedt.

Harshith Bachimanchi joins the Soft Matter Lab

Harshith Bachimanchi. (Photo by A. Argun)
Harshith Bachimanchi starts his PhD at the Physics Department of the University of Gothenburg on 20th January 2020.

Harshith has a Master degree in physics from the Indian Institute of Science Education and Research, Pune, India, where he submitted a Master thesis in optics, whose results can be found here.

In his PhD, he will focus on microscopy and deep learning.

Start-up “Lucero” Semi-finalist in SPIE Startup Challenge

Our idea Lucero, has reached the semi-final for the SPIE Start-up challenge, where will pitch in front of a jury at Photonics West in San Francisco, CA, USA on the 4th of February 2020.

Lucero will compete, among other 41 semifinalists, for cash prizes and business support.

In addition, Lucero was awarded one of the three Early Stage Entrepreneurship Travel Grants to attend the semi-final.

The start-up is aiming to make cutting-edge laser technology easy to use and available to anyone by combining it with commercial microscope. The product and software combo utilizes optical tweezers in a brand-new way – and bridges the gap between physics and other scientific fields that would greatly benefit from easier access to this tool.

In December, Lucero was ranked among the best 5 business ideas in West Sweden.

Team components: Christopher Jacklin, Rich Zapata Rosas, Felix Mossberg, Falko Schmidt, Alejandro Diaz Tormo and Martin Mojica-Benavides.

Links: Lucero Homepage

Start-up “Lucero Bio” among the best 5 business ideas in West Sweden

Falko Schmidt and other researchers at the University of Gothenburg, in collaboration with Business students at the Chalmers School of Entrepreneurship, have received early acclaimfor their Start-up idea “Lucero Bio”.

Lucero Bio was ranked among one of the top 5 business ideas in West Sweden by Venture Cup Sweden. Out of the 376 ideas that were submitted to the competition, nearly half came from the western region of Sweden.

The start-up is aiming to make cutting-edge laser technology easy to use and available to anyone by combining it with commercial microscope. The product and software combo utilizes optical tweezers in a brand-new way – and bridges the gap between physics and other scientific fields that would greatly benefit from easier access to this tool.

Team components: Christopher Jacklin, Rich Zapata Rosas, Felix Mossberg, Falko Schmidt, Alejandro Diaz Tormo and Martin Mojica-Benavides.

More information:
Press release, in Swedish.
Top 20 list of the 2019 winners, in Swedish.

Poster presentation by F. Schmidt at the Light at the Nanoscale conference, 5 December 2019

Light-induced phase separation power novel micro machines
Falko Schmidt, and Giovanni Volpe
Light at the Nanoscale Conference, Chalmers University, Gothenburg, Sweden
5 December 2019, 16:30-18:30

Focused laser light is used to trap a micronsized absorbing particle around its beam center where it performs constant orbital rotation. The light-induced local phase separations create a concentration gradient on which the particle moves along. Large patches of absorbing material on the particle’s surface give rise to a torque required for steady rotation1

Phase separation is a phenomena that commonly exists in nature, from the freezing of ice to the intrinsic mechanism of the cell to order matter. We are exploiting phase separations to produce new types of miniaturised machines, in particular micron and nano sized engines1as well as to form self-assembled colloidal molecules2. We control their behaviour using only light and varying its ambient temperature making this a simple tool to study complex matter3. This will enhance the development of future medicine where nano robots deliver drugs specifically to the local infection side.

References:1. F. Schmidt et al. Microscopic engine powered by critical demixing, Phys Rev Lett 120, 068004, 2018

2. F. Schmidt et al. Light-controlled assembly of active colloidal molecules, J Chem Phys150, 094905, 2019

3. S. Bo et al. Measurement of anomalous diffusion using recurrent neural networks, Phys Rev E 100, 010102(R), 2019

DeepTrack selected by Optics & Photonics News as one of the most exciting optics discoveries in 2019

Optics & Photonics News has picked Saga Helgadóttir and Aykut Argun’s work on deep learning for particle tracking (DeepTrack) as a top break-through of the year.

“This has been a really good year for me, research-wise. My publication, presenting a new AI method, garnered a lot of attention,” says Saga Helgadóttir, PhD at the Department of Physics.

The research article in question, which is now included in Optics & Photonics News’ best-of-2019 list, identifies a new way of implementing neural networks and machine learning in order to track particle motion and study surrounding microenvironments.

After the publication in mid-April, Saga Helgadóttir was contacted by both national and international press to talk about her discoveries. She has also been invited to visit research groups abroad and was a speaker at the AI in Health and Health in AI conference held in Gothenburg in August.

Currently, Saga Helgadottir is collaborating with a group of scientists at Sahlgrenska’s Wallenberg Laboratory. They are working on new ways of using deep learning in the medical field.

“I started my PhD research studying bio-hybrid microswimmers, but ended up more within the area of artificial intelligence and optics. I like this work a lot, and the positive response to my publication earlier this year has allowed me to establish myself in the AI-field.”

Text: Carolina Svensson

List of highlighted research from 2019: Optics in 2019

Saga Helgadottir’s featured summary: Deep Learning for Particle Tracking

Original press release about the research: She has discovered a new method of using AI