{"id":6071,"date":"2022-12-07T16:20:56","date_gmt":"2022-12-07T15:20:56","guid":{"rendered":"https:\/\/www.fgw-brandenburg.de\/medizinische-bioinformatik\/"},"modified":"2026-03-26T12:24:14","modified_gmt":"2026-03-26T11:24:14","slug":"medical-bioinformatics","status":"publish","type":"page","link":"https:\/\/www.fgw-brandenburg.de\/en\/members\/professorial-chairs\/medical-bioinformatics\/","title":{"rendered":"Medical Bioinformatics with a Focus on Near-patient Data Acquisition"},"content":{"rendered":"\n<div class=\"wp-block-columns\">\n<div class=\"wp-block-column\" style=\"flex-basis:55%\">\n<div style=\"height:120px\" aria-hidden=\"true\" class=\"wp-block-spacer\"><\/div>\n\n\n\n<p>Prof. Dr. Alexander Schliep is the new FGW Professor for Medical Bioinformatics with a focus on patient-oriented data acquisition at the Brandenburg University of Technology Cottbus-Senftenberg (BTU) since October 01, 2022.<\/p>\n\n\n\n<div style=\"height:50px\" aria-hidden=\"true\" class=\"wp-block-spacer\"><\/div>\n\n\n\n<p>Large amounts of data are collected in many disciplines, often with highly diverse formats that need to be processed bioinformatically. For this purpose, it is usually necessary to develop new tools that provide the best possible answer for the data set and the specific question.<\/p>\n\n\n\n<div style=\"height:50px\" aria-hidden=\"true\" class=\"wp-block-spacer\"><\/div>\n\n\n\n<p>Furthermore, one focus of this professorship will be the development of new techniques such as mobile diagnostics or networked care of chronically ill people as well as the bias-free evaluation of these new techniques.<\/p>\n\n\n\n<div style=\"height:30px\" aria-hidden=\"true\" class=\"wp-block-spacer\"><\/div>\n<\/div>\n\n\n\n<div class=\"wp-block-column\" style=\"flex-basis:5%\"><\/div>\n\n\n\n<div class=\"wp-block-column\" style=\"flex-basis:40%\">\n<div style=\"height:25px\" aria-hidden=\"true\" class=\"wp-block-spacer\"><\/div>\n\n\n<div class=\"about_person\">\n  <div class=\"about_person__img\"><img decoding=\"async\" src=\"https:\/\/www.fgw-brandenburg.de\/wp-content\/uploads\/2024\/01\/Alexander_Homepage-scaled-e1707905933155.jpg\" alt=\"Prof. Dr. Alexander Schliep\" \/><a href=\"https:\/\/www.fgw-brandenburg.de\/en\/person\/prof-dr-alexander-schliep\/\"><\/a><\/div>\n  <div class=\"about_person-content\">\n    <div class=\"about_person__name\">Prof. Dr. Alexander Schliep<\/div>\n    <div class=\"about_person__position\">Head of the Professorship for Medical Bioinformatics with a Focus on near-patient Data Acquisition<\/div>\n    <div class=\"about_peersont__address\">\n    <\/div>\n<a href=\"tel:+493555818721\" class=\"about_person__phone\">phone: +49 (0)355 5818 721<\/a><a href=\"mailto:schliep@b-tu.de\" class=\"about_person__email\">schliep[at]b-tu.de<\/a>  <\/div>\n<\/div>\n\n\n\n<p><\/p>\n\n\n\n<p><\/p>\n<\/div>\n<\/div>\n\n\n\n<div style=\"height:60px\" aria-hidden=\"true\" class=\"wp-block-spacer\"><\/div>\n\n\n\n<div class=\"wp-block-getwid-tabs\" data-active-tab=\"0\"><ul class=\"wp-block-getwid-tabs__nav-links\"><\/ul>\n<div class=\"wp-block-getwid-tabs__nav-link\"><span class=\"wp-block-getwid-tabs__title-wrapper\"><a href=\"#\"><span class=\"wp-block-getwid-tabs__title\">Research<\/span><\/a><\/span><\/div><div class=\"wp-block-getwid-tabs__tab-content-wrapper\"><div class=\"wp-block-getwid-tabs__tab-content\">\n<p>Das SchliepLAB ist Teil der Fakult\u00e4t f\u00fcr Gesundheitswissenschaften Brandenburg und befindet sich an der Brandenburgischen Technischen Universit\u00e4t Cottbus-Senftenberg. Ein Teil der Gruppe ist an der Fakult\u00e4t f\u00fcr Informatik und Ingenieurwissenschaften angesiedelt, die eine gemeinsame Fakult\u00e4t der Universit\u00e4t G\u00f6teborg und der Chalmers University of Technology ist.<\/p>\n\n\n\n<p>Eine Liste der laufenden und abgeschlossenen Forschungsprojekte finden Sie unter&nbsp;<a href=\"https:\/\/schlieplab.org\/Research\/\" target=\"_blank\" rel=\"noreferrer noopener\">https:\/\/schlieplab.org\/Research\/<\/a>, eine Liste der Ver\u00f6ffentlichungen unter&nbsp;<a href=\"https:\/\/schlieplab.org\/Publications\/\" target=\"_blank\" rel=\"noreferrer noopener\">https:\/\/schlieplab.org\/Publications\/<\/a>&nbsp;und eine Liste der Softwarepakete unter&nbsp;<a href=\"https:\/\/schlieplab.org\/Software\/\" target=\"_blank\" rel=\"noreferrer noopener\">https:\/\/schlieplab.org\/Software\/<\/a>.<\/p>\n\n\n\n<div style=\"height:20px\" aria-hidden=\"true\" class=\"wp-block-spacer\"><\/div>\n\n\n\n<h2 class=\"wp-block-heading\">Forschungsschwerpunkte<\/h2>\n\n\n\n<h3 class=\"wp-block-heading\">AI f\u00fcr das Design von Oligonukleotid-Therapeutika<\/h3>\n\n\n\n<p>Insbesondere f\u00fcr Antisense-Oligonukleotide (ASO), die durch RNAse H1-mediated Knockdown wirken, sind Bindungsenergien und Kinetik von ASO-mRNA-Duplexen entscheidend f\u00fcr die Vorhersage von Wirksamkeit und Sicherheit. Wir sagen Bindungsenergien anhand von Sequenzen voraus, untersuchen die Kinetik der ASO-Reaktionen, um den Entwurfsprozess von ASO-Medikamenten vorhersehbarer zu machen, und kombinieren in Kooperationsprojekten Molekulardynamik und k\u00fcnstliche Intelligenz, um Vorhersagemodelle f\u00fcr eine breitere Palette von Nukleotidmodifikationen zu erweitern. Unsere federated, privacy preserving Lernmethoden erm\u00f6glichen auch ohne Austausch von Daten ML-Modelle f\u00fcr die Vorhersage von Bindungsenergien zu lernen.<\/p>\n\n\n\n<div style=\"height:20px\" aria-hidden=\"true\" class=\"wp-block-spacer\"><\/div>\n\n\n\n<h3 class=\"wp-block-heading\">ML f\u00fcr pan-genomische Graphen<\/h3>\n\n\n\n<p>Pan-Genom-Graphen bieten einen prinzipiellen Ansatz f\u00fcr den Umgang mit strukturellen Variationen und, allgemein, dem hohen Grad an Variationen zwischen individuellen Genomen. ML auf Pan-Genom-Graphen wird es erm\u00f6glichen, Vorhersage- und Regressionsaufgaben f\u00fcr verschiedene Populationen zu bew\u00e4ltigen, einschlie\u00dflich Gr\u00f6\u00dfen, die f\u00fcr Oligonukleotid-Therapeutika relevant sind, wie Transkriptionsgeschwindigkeit oder DNA-Accessibility, sowie klinisch relevante Variablen.<\/p>\n\n\n\n<div style=\"height:20px\" aria-hidden=\"true\" class=\"wp-block-spacer\"><\/div>\n\n\n\n<h3 class=\"wp-block-heading\">ML und Algorithmik f\u00fcr Sequenzierdaten<\/h3>\n\n\n\n<p>Die von experimentellen Hochdurchsatzplattformen wie dem High-Throughput Sequencing (HTS) erzeugten Daten stellen eine rechnerische Herausforderung dar, insbesondere wenn fortschrittliche statistische Ans\u00e4tze wie Bayes&#8217;sche Methoden zur Analyse verwendet werden. In der Vergangenheit haben wir einen komprimierenden Genomik-Ansatz entwickelt, der vom Big Data to Knowledge Program (BD2K) der NIH finanziert wurde, Wavelet-Kompression in Bayes&#8217;schen HMMs zur Erkennung von Kopienzahlvarianten eingesetzt und den Nutzen statistischer ML-Modelle, die Genome repr\u00e4sentieren &#8211; Markov-Ketten variabler L\u00e4nge &#8211; durch schnellere Lernalgorithmen erheblich verbessert. Dies erm\u00f6glicht z. B. alignementfreie Genomvergleiche aus Rohdaten.<\/p>\n\n\n\n<div style=\"height:20px\" aria-hidden=\"true\" class=\"wp-block-spacer\"><\/div>\n\n\n\n<h2 class=\"wp-block-heading\">Lehre<\/h2>\n\n\n\n<p>Computational Thinking ist heute eine Grundvoraussetzung f\u00fcr fast alle Disziplinen. Die Vermittlung von algorithmischen Ideen kann von Software-Tools stark profitieren. Wir entwickeln Animationssysteme f\u00fcr Graphenalgorithmen, die auf dem Desktop, als Web-App und bald auch als iOS-App verf\u00fcgbar sind; CATBox ist ein Springer-Lehrbuch, das Gato verwendet. Mit unserer Hidden Markov Model-Bibliothek k\u00f6nnen sich die Lernenden auf die L\u00f6sung spannender bioinformatischer Probleme konzentrieren.<\/p>\n<\/div><\/div>\n\n\n\n<div class=\"wp-block-getwid-tabs__nav-link\"><span class=\"wp-block-getwid-tabs__title-wrapper\"><a href=\"#\"><span class=\"wp-block-getwid-tabs__title\">Teaching<\/span><\/a><\/span><\/div><div class=\"wp-block-getwid-tabs__tab-content-wrapper\"><div class=\"wp-block-getwid-tabs__tab-content\">\n<h2 class=\"wp-block-heading\">Wintersemester 2025\/26<\/h2>\n\n\n\n<h3 class=\"wp-block-heading has-medium-font-size\">Computing at Scale in Machine Learning: Distributed computing and algorithmic&nbsp;approaches (Modul: 14038)<\/h3>\n\n\n\n<p><strong>Learning Outcomes<\/strong><br>Students will obtain an overview on how to solve large-scale computational problems in&nbsp;data science and machine learning using a) parallel approaches from multi-threaded&nbsp;computation on individual machines to implicit parallelism frameworks on compute clusters&nbsp;and b) algorithms and data structures supporting efficient exact or approximate&nbsp;computation with massive data sets in and out of core. In particular they will learn how to&nbsp;analyze relevant probabilistic data structures and algorithms and select and implement&nbsp;appropriate computational approaches for large-scale problems.<\/p>\n\n\n\n<p><strong>Contents<\/strong><br>The focus will be on four fundamental problem areas:<\/p>\n\n\n\n<ul class=\"wp-block-list\">\n<li>A review of memory-compute co-location and its impact on big data computations.<\/li>\n\n\n\n<li>Solving Machine Learning (ML) work loads using explicit parallelism, specifically&nbsp;multi-threaded computation on an individual machine.<\/li>\n\n\n\n<li>Introduction of implicit parallelism programming models as implemented for&nbsp;example in MapReduce, Spark and Ray and their application in ML.<\/li>\n\n\n\n<li>Probabilistic algorithms such as sketching algorithms (incl. CountMinSketch, HyperLogLog)&nbsp;or Bloom filters.<\/li>\n\n\n\n<li>Implementing ML methods using index data structures such as suffix or kd-trees.<\/li>\n<\/ul>\n\n\n\n<p><strong>Recommended Prerequisites<\/strong><\/p>\n\n\n\n<ul class=\"wp-block-list\">\n<li>Introduction to machine learning at Master\u2019s level. Advanced&nbsp;knowledge of programming in Python and the Linux command line.&nbsp;<\/li>\n<\/ul>\n\n\n\n<figure class=\"wp-block-table\">\n<table>\n<thead>\n<tr>\n<th>Lecture<\/th>\n<th>Exercise<\/th>\n<\/tr>\n<\/thead>\n<tbody>\n<tr>\n<td>Thursdays<\/td>\n<td>Thursdays<\/td>\n<\/tr>\n<tr>\n<td>15:30 &#8211; 17:00<\/td>\n<td>17:30 &#8211; 19:00<\/td>\n<\/tr>\n<tr>\n<td><a href=\"https:\/\/www.b-tu.de\/campusplan\/zentralcampus-cottbus#toggle_building_39\"><br \/>ZC, LG 1A, HS 1<\/a><\/td>\n<td><a href=\"https:\/\/www.b-tu.de\/campusplan\/zentralcampus-cottbus#toggle_building_31\">ZC, ZHG, SR 1<br \/>ZC, ZHG, SR 2<\/a><\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n<\/figure>\n\n\n\n<div style=\"height:20px\" aria-hidden=\"true\" class=\"wp-block-spacer\"><\/div>\n\n\n\n<p><strong>The examination will take place on Mar 27, 2026, 11:00 &#8211; 13:00, Zentralcampus, ZHG, AM 2. <\/strong><\/p>\n\n\n\n<div style=\"height:20px\" aria-hidden=\"true\" class=\"wp-block-spacer\"><\/div>\n\n\n\n<p>Detailed information for participants is available at&nbsp;<a href=\"https:\/\/www.b-tu.de\/elearning\/btu\/course\/view.php?id=15473\" target=\"_blank\" rel=\"noreferrer noopener\">https:\/\/www.b-tu.de\/elearning\/btu\/course\/view.php?id=15473<\/a>.<br><\/p>\n\n\n\n<div style=\"height:45px\" aria-hidden=\"true\" class=\"wp-block-spacer\"><\/div>\n\n\n\n<hr class=\"wp-block-separator has-alpha-channel-opacity is-style-wide\"\/>\n\n\n\n<div style=\"height:45px\" aria-hidden=\"true\" class=\"wp-block-spacer\"><\/div>\n\n\n\n<h3 class=\"wp-block-heading has-medium-font-size\">Introduction to Bioinformatics (Modul: 14336)<\/h3>\n\n\n\n<p><strong>Learning Outcomes<\/strong><br>After successfully completing the module, students will have acquired an overview of the&nbsp;fundamentals of bioinformatics. This includes an introduction to relevant molecular processes, scientific instruments to investigate these processes, and the data generated by&nbsp;them. For central computational problems, students will be able to discuss advantages and&nbsp;disadvantages of statistical and basic algorithmic approaches, respectively adapt them to&nbsp;specific biological questions. Students will be able to analyze specific biological data using&nbsp;appropriate software libraries for Python.<\/p>\n\n\n\n<p><strong>Contents<\/strong><br>The focus will be on the basics of the following areas:<\/p>\n\n\n\n<ul class=\"wp-block-list\">\n<li>An introduction to molecular biology including relevant scientific instruments and&nbsp;the Omics-data generated by them.<\/li>\n\n\n\n<li>Pair-wise and multiple sequence alignments, seed-and-extend approaches, and&nbsp;genome indexes<\/li>\n\n\n\n<li>Evolutionary models and phylogenetic trees<\/li>\n\n\n\n<li>Signals in sequences: identification of motifs<\/li>\n\n\n\n<li>Assembly of genomes and transcriptomes<\/li>\n\n\n\n<li>Gene expression analysis<\/li>\n<\/ul>\n\n\n\n<p><strong>Recommended Prerequisites<\/strong><br>Good knowledge of discrete probability, algorithms and data&nbsp;structures at the undergraduate level. Advanced knowledge of programming in Python and&nbsp;the Linux command line.&nbsp;<\/p>\n\n\n\n<div class=\"wp-block-columns\">\n<div class=\"wp-block-column\" style=\"flex-basis:100%\">\n<figure class=\"wp-block-table\"><table><thead><tr><th>Exercise<\/th><th>Lecture<\/th><\/tr><\/thead><tbody><tr><td>Fridays<\/td><td>Fridays<\/td><\/tr><tr><td>9:15 &#8211; 10:45<\/td><td>11:30 &#8211; 13:00<\/td><\/tr><tr><td><a href=\"https:\/\/www.b-tu.de\/special-pages\/campusplan\/campus-cottbus-sachsendorf#toggle_building_26\" target=\"_blank\" rel=\"noreferrer noopener\">Campus Sachsendorf, building 9, room 9.219<\/a><\/td><td><a href=\"https:\/\/www.b-tu.de\/special-pages\/campusplan\/campus-cottbus-sachsendorf#toggle_building_26\" target=\"_blank\" rel=\"noreferrer noopener\">Campus Sachsendorf, building 9, room 9.219<\/a><\/td><\/tr><\/tbody><\/table><\/figure>\n<\/div>\n<\/div>\n\n\n\n<div style=\"height:20px\" aria-hidden=\"true\" class=\"wp-block-spacer\"><\/div>\n\n\n\n<p><strong>The examination will take part on Mar 24, 2026, 11:00 &#8211; 13:00, <a href=\"https:\/\/www.b-tu.de\/special-pages\/campusplan\/campus-cottbus-sachsendorf#toggle_building_30\">Campus Sachsendorf, building 15, 15V110<\/a>.<\/strong><\/p>\n\n\n\n<div style=\"height:20px\" aria-hidden=\"true\" class=\"wp-block-spacer\"><\/div>\n\n\n\n<p>Detailed information available for participants at&nbsp;<a href=\"https:\/\/www.b-tu.de\/elearning\/btu\/course\/view.php?id=15472\">https:\/\/www.b-tu.de\/elearning\/btu\/course\/view.php?id=15472<\/a>.<\/p>\n\n\n\n<div style=\"height:45px\" aria-hidden=\"true\" class=\"wp-block-spacer\"><\/div>\n\n\n\n<hr class=\"wp-block-separator has-alpha-channel-opacity is-style-wide\"\/>\n\n\n\n<div style=\"height:45px\" aria-hidden=\"true\" class=\"wp-block-spacer\"><\/div>\n\n\n\n<h3 class=\"wp-block-heading has-medium-font-size\">Bioinformatics (Modul: 14810)<\/h3>\n\n\n\n<figure class=\"wp-block-table\"><table><thead><tr><th>Seminar<\/th><\/tr><\/thead><tbody><tr><td>Fridays<\/td><\/tr><tr><td>13:45 &#8211; 15:15<\/td><\/tr><tr><td><a href=\"https:\/\/www.b-tu.de\/special-pages\/campusplan\/campus-cottbus-sachsendorf#toggle_building_26\">Sachsendorf Campus, LG 9, HS 9.219<\/a><\/td><\/tr><\/tbody><\/table><\/figure>\n\n\n\n<div style=\"height:20px\" aria-hidden=\"true\" class=\"wp-block-spacer\"><\/div>\n\n\n\n<p>Detailed information for participants is available at&nbsp;<a href=\"https:\/\/www.b-tu.de\/elearning\/btu\/course\/view.php?id=15474\" target=\"_blank\" rel=\"noreferrer noopener\">https:\/\/www.b-tu.de\/elearning\/btu\/course\/view.php?id=15474<\/a>.<\/p>\n\n\n\n<div style=\"height:45px\" aria-hidden=\"true\" class=\"wp-block-spacer\"><\/div>\n\n\n\n<hr class=\"wp-block-separator has-alpha-channel-opacity is-style-wide\"\/>\n\n\n\n<div style=\"height:45px\" aria-hidden=\"true\" class=\"wp-block-spacer\"><\/div>\n\n\n\n<h3 class=\"wp-block-heading\"><strong><strong>Oberseminar Medizinische Bioinformatik (Modul: 13600)<\/strong><\/strong> <\/h3>\n\n\n\n<p>Meetings will take part on Tuesdays, 13:45 &#8211; 15:15, Campus Sachsendorf, building 9, room 9.317.<\/p>\n\n\n\n<p>Detailed information for participants is available at <a href=\"https:\/\/www.b-tu.de\/elearning\/btu\/course\/view.php?id=15470\">https:\/\/www.b-tu.de\/elearning\/btu\/course\/view.php?id=15470<\/a>.<\/p>\n\n\n\n<div style=\"height:45px\" aria-hidden=\"true\" class=\"wp-block-spacer\"><\/div>\n\n\n\n<hr class=\"wp-block-separator has-alpha-channel-opacity\"\/>\n\n\n\n<div style=\"height:45px\" aria-hidden=\"true\" class=\"wp-block-spacer\"><\/div>\n\n\n\n<h3 class=\"wp-block-heading\"><strong>Research Module in Artificial Intelligence (Modul: 14060)<\/strong><\/h3>\n\n\n\n<p>Meetings will take part on Tuesdays, 16:00 &#8211; 17:30, Campus Sachsendorf, building 9, room 9.317.<\/p>\n\n\n\n<p>Detailed information for participants is available at <a href=\"https:\/\/www.b-tu.de\/elearning\/btu\/course\/view.php?id=15475\" target=\"_blank\" rel=\"noreferrer noopener\">https:\/\/www.b-tu.de\/elearning\/btu\/course\/view.php?id=15475<\/a>.<\/p>\n\n\n\n<div style=\"height:45px\" aria-hidden=\"true\" class=\"wp-block-spacer\"><\/div>\n\n\n\n<hr class=\"wp-block-separator has-alpha-channel-opacity\"\/>\n\n\n\n<div style=\"height:45px\" aria-hidden=\"true\" class=\"wp-block-spacer\"><\/div>\n\n\n\n<p>A list of current and prior offered courses and seminars can be found at&nbsp;<a href=\"https:\/\/schlieplab.org\/Teaching\/\" target=\"_blank\" rel=\"noreferrer noopener\">https:\/\/schlieplab.org\/Teaching\/<\/a><\/p>\n<\/div><\/div>\n\n\n\n<div class=\"wp-block-getwid-tabs__nav-link\"><span class=\"wp-block-getwid-tabs__title-wrapper\"><a href=\"#\"><span class=\"wp-block-getwid-tabs__title\">Team<\/span><\/a><\/span><\/div><div class=\"wp-block-getwid-tabs__tab-content-wrapper\"><div class=\"wp-block-getwid-tabs__tab-content\"><div class=\"about_person\">\n  <div class=\"about_person__img\"><img decoding=\"async\" src=\"https:\/\/www.fgw-brandenburg.de\/wp-content\/uploads\/2026\/01\/Katharina-scaled.jpeg\" alt=\"Katharina Mansfeld\" \/><a href=\"https:\/\/www.fgw-brandenburg.de\/en\/person\/katharina-mansfeld\/\"><\/a><\/div>\n  <div class=\"about_person-content\">\n    <div class=\"about_person__name\">Katharina Mansfeld<\/div>\n    <div class=\"about_person__position\">Assistant<\/div>\n    <div class=\"about_peersont__address\">\n    <\/div>\n<a href=\"tel:+493555818720\" class=\"about_person__phone\">phone: +49 (0)355 5818 720<\/a><a href=\"mailto: mansfeld@b-tu.de\" class=\"about_person__email\"> mansfeld[at]b-tu.de<\/a>  <\/div>\n<\/div>\n\n\n<div class=\"about_person\">\n  <div class=\"about_person__img\"><img decoding=\"async\" src=\"https:\/\/www.fgw-brandenburg.de\/wp-content\/uploads\/2024\/01\/Nathalie_Homepage-scaled-e1705495422131.jpg\" alt=\"Nathalie Gocht, M. Sc.\" \/><a href=\"https:\/\/www.fgw-brandenburg.de\/en\/person\/nathalie-gocht\/\"><\/a><\/div>\n  <div class=\"about_person-content\">\n    <div class=\"about_person__name\">Nathalie Gocht, M. Sc.<\/div>\n    <div class=\"about_person__position\">Research Associate<\/div>\n    <div class=\"about_peersont__address\">\n    <\/div>\n<a href=\"tel:+493555818373\" class=\"about_person__phone\">phone: +49 (0)355 5818 373<\/a><a href=\"mailto:gocht@b-tu.de\" class=\"about_person__email\"> gocht[at]b-tu.de<\/a>  <\/div>\n<\/div>\n\n\n<div class=\"about_person\">\n  <div class=\"about_person__img\"><img decoding=\"async\" src=\"https:\/\/www.fgw-brandenburg.de\/wp-content\/uploads\/2024\/01\/Aleksandra-scaled.jpg\" alt=\"Aleksandra Khatova, M. Sc.\" \/><a href=\"https:\/\/www.fgw-brandenburg.de\/en\/person\/aleksandra-khatova-m-sc\/\"><\/a><\/div>\n  <div class=\"about_person-content\">\n    <div class=\"about_person__name\">Aleksandra Khatova, M. Sc.<\/div>\n    <div class=\"about_person__position\">Research Associate<\/div>\n    <div class=\"about_peersont__address\">\n    <\/div>\n<a href=\"tel:+493555818723\" class=\"about_person__phone\">phone:  +49 (0)355 5818 723<\/a><a href=\"mailto: khatova@b-tu.de\" class=\"about_person__email\"> khatova[at]b-tu.de<\/a>  <\/div>\n<\/div>\n<\/div><\/div>\n<\/div>\n\n\n\n<div style=\"height:30px\" aria-hidden=\"true\" class=\"wp-block-spacer\"><\/div>\n\n\n<div class=\"about_person\">\n  <div class=\"about_person__img\"><img decoding=\"async\" src=\"https:\/\/www.fgw-brandenburg.de\/wp-content\/uploads\/2026\/01\/Seyedarash-scaled.jpeg\" alt=\"Seyedarash Ayatollahi, M.Sc.\" \/><a href=\"https:\/\/www.fgw-brandenburg.de\/en\/person\/seyedarash-ayatollahi-m-sc\/\"><\/a><\/div>\n  <div class=\"about_person-content\">\n    <div class=\"about_person__name\">Seyedarash Ayatollahi, M.Sc.<\/div>\n    <div class=\"about_person__position\">Research Associate<\/div>\n    <div class=\"about_peersont__address\">\n    <\/div>\n<a href=\"tel:+493555818373\" class=\"about_person__phone\">phone: +49 355 5818 373<\/a><a href=\"mailto:ayatollahi@b-tu.de\" class=\"about_person__email\">ayatollahi@b-tu.de<\/a>  <\/div>\n<\/div>\n","protected":false},"excerpt":{"rendered":"<p>Prof. Dr. Alexander Schliep is the new FGW Professor for Medical Bioinformatics with a focus on patient-oriented data acquisition at the Brandenburg University of Technology Cottbus-Senftenberg (BTU) since October 01, 2022. Large amounts of data are collected in many disciplines, often with highly diverse formats that need to be processed bioinformatically. For this purpose, it &#8230;<\/p>\n","protected":false},"author":11,"featured_media":0,"parent":2481,"menu_order":7,"comment_status":"closed","ping_status":"closed","template":"","meta":{"_acf_changed":false,"inline_featured_image":false,"footnotes":""},"class_list":["post-6071","page","type-page","status-publish","hentry"],"acf":[],"yoast_head":"<!-- This site is optimized with the Yoast SEO plugin v27.7 - https:\/\/yoast.com\/product\/yoast-seo-wordpress\/ -->\n<title>Medical Bioinformatics | Faculty of Health Sciences<\/title>\n<meta name=\"description\" content=\"Prof. Dr. Alexander Schliep is FGW Professor of Medical Bioinformatics with a focus on patient-oriented data acquisition at BTU.\" \/>\n<meta name=\"robots\" content=\"index, follow, max-snippet:-1, max-image-preview:large, max-video-preview:-1\" \/>\n<link rel=\"canonical\" href=\"https:\/\/www.fgw-brandenburg.de\/en\/members\/professorial-chairs\/medical-bioinformatics\/\" \/>\n<meta property=\"og:locale\" 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