{"id":2986,"date":"2019-03-15T14:59:00","date_gmt":"2019-03-15T14:59:00","guid":{"rendered":"https:\/\/blog.kolabtree.com\/?p=2986"},"modified":"2023-02-15T11:47:02","modified_gmt":"2023-02-15T11:47:02","slug":"applications-of-machine-learning-in-biology","status":"publish","type":"post","link":"https:\/\/www.kolabtree.com\/blog\/applications-of-machine-learning-in-biology\/","title":{"rendered":"The Applications of Machine Learning in Biology"},"content":{"rendered":"<div id=\"ez-toc-container\" class=\"ez-toc-v2_0_45_1 counter-flat ez-toc-counter ez-toc-grey ez-toc-container-direction\">\n<div class=\"ez-toc-title-container\">\n<p class=\"ez-toc-title\">Table of Contents<\/p>\n<span class=\"ez-toc-title-toggle\"><a href=\"#\" class=\"ez-toc-pull-right ez-toc-btn ez-toc-btn-xs ez-toc-btn-default ez-toc-toggle\" area-label=\"ez-toc-toggle-icon-1\"><label for=\"item-69f1d96e713fa\" aria-label=\"Table of Content\"><span style=\"display: flex;align-items: center;width: 35px;height: 30px;justify-content: center;direction:ltr;\"><svg style=\"fill: #999;color:#999\" xmlns=\"http:\/\/www.w3.org\/2000\/svg\" class=\"list-377408\" width=\"20px\" height=\"20px\" viewBox=\"0 0 24 24\" fill=\"none\"><path d=\"M6 6H4v2h2V6zm14 0H8v2h12V6zM4 11h2v2H4v-2zm16 0H8v2h12v-2zM4 16h2v2H4v-2zm16 0H8v2h12v-2z\" fill=\"currentColor\"><\/path><\/svg><svg style=\"fill: #999;color:#999\" class=\"arrow-unsorted-368013\" xmlns=\"http:\/\/www.w3.org\/2000\/svg\" width=\"10px\" height=\"10px\" viewBox=\"0 0 24 24\" version=\"1.2\" baseProfile=\"tiny\"><path d=\"M18.2 9.3l-6.2-6.3-6.2 6.3c-.2.2-.3.4-.3.7s.1.5.3.7c.2.2.4.3.7.3h11c.3 0 .5-.1.7-.3.2-.2.3-.5.3-.7s-.1-.5-.3-.7zM5.8 14.7l6.2 6.3 6.2-6.3c.2-.2.3-.5.3-.7s-.1-.5-.3-.7c-.2-.2-.4-.3-.7-.3h-11c-.3 0-.5.1-.7.3-.2.2-.3.5-.3.7s.1.5.3.7z\"\/><\/svg><\/span><\/label><input  type=\"checkbox\" id=\"item-69f1d96e713fa\"><\/a><\/span><\/div>\n<nav><ul class='ez-toc-list ez-toc-list-level-1 ' ><li class='ez-toc-page-1'><a class=\"ez-toc-link ez-toc-heading-1\" href=\"https:\/\/www.kolabtree.com\/blog\/applications-of-machine-learning-in-biology\/#What_is_machine_learning\" title=\"What is machine learning?\">What is machine learning?<\/a><\/li><li class='ez-toc-page-1'><a class=\"ez-toc-link ez-toc-heading-2\" href=\"https:\/\/www.kolabtree.com\/blog\/applications-of-machine-learning-in-biology\/#Applications_of_Machine_Learning_in_Biology\" title=\"Applications of Machine Learning in Biology\">Applications of Machine Learning in Biology<\/a><\/li><li class='ez-toc-page-1'><a class=\"ez-toc-link ez-toc-heading-3\" href=\"https:\/\/www.kolabtree.com\/blog\/applications-of-machine-learning-in-biology\/#Machine_Learning_Tools_used_in_Biology\" title=\"Machine Learning Tools used in Biology\">Machine Learning Tools used in Biology<\/a><\/li><\/ul><\/nav><\/div>\n<p><strong>Machine learning has several applications in diverse fields, ranging from healthcare to natural language processing. Dr. Ragothanam Yennamalli, a computational biologist and Kolabtree freelancer, examines the applications of AI and <a href=\"https:\/\/www.kolabtree.com\/find-an-expert\/subject\/machine-learning?utm_source=Blog&amp;utm_medium=Post&amp;campaign=MLBiology\">machine learning<\/a> in biology.\u00a0<\/strong><\/p>\n<p>Machine Learning and <a href=\"https:\/\/www.kolabtree.com\/blog\/ensuring-reproducibility-in-ai-driven-research-how-freelance-experts-can-help-in-biotech-and-healthcare\/\">Artificial Intelligence<\/a> &#8212; these technologies have stormed the world and have changed the way we work and live. Advances in these areas have led to many either praising it or decrying it. However, for a computational person like me, they are not new words. AI and ML, as they&#8217;re popularly called, have several applications and benefits across a wide range of industries. Most notably, they are revolutionizing the way biological research is performed, leading to new innovations across <a href=\"https:\/\/blog.kolabtree.com\/5-real-world-examples-of-ai-in-healthcare\/\">healthcare<\/a> and biotechnology.<\/p>\n<h2><span class=\"ez-toc-section\" id=\"What_is_machine_learning\"><\/span>What is machine learning?<span class=\"ez-toc-section-end\"><\/span><\/h2>\n<p><a href=\"https:\/\/blog.kolabtree.com\/understanding-scope-of-machine-learning-and-its-applications\/\">Machine learning<\/a> and statistics are closely knit. The reason is that the methods used in most machine learning approaches have origins from statistics such as regression analysis. <strong>While there are many applications for machine learning methods, their applications to biological data since the last 30 years or so have been in gene prediction, functional annotation, systems biology, microarray data analysis, pathway analysis, etc.<\/strong><\/p>\n<p>Patterns is what a machine tries to identify in a given data, using which it tries to identify a similar pattern in another set of data. The processes of machine learning are quite similar to predictive modelling and data mining. They search data to identify patterns and alter the action of program, accordingly.<\/p>\n<p><img decoding=\"async\" loading=\"lazy\" class=\"alignnone wp-image-5409\" src=\"https:\/\/blog.kolabtree.com\/wp-content\/uploads\/2019\/03\/online-2900303_640.jpg\" alt=\"\" width=\"350\" height=\"233\" srcset=\"https:\/\/www.kolabtree.com\/blog\/wp-content\/uploads\/2019\/03\/online-2900303_640.jpg 640w, https:\/\/www.kolabtree.com\/blog\/wp-content\/uploads\/2019\/03\/online-2900303_640-300x200.jpg 300w, https:\/\/www.kolabtree.com\/blog\/wp-content\/uploads\/2019\/03\/online-2900303_640-300x200@2x.jpg 600w\" sizes=\"(max-width: 350px) 100vw, 350px\" \/><\/p>\n<p>We are aware about\u00a0 machine learning and AI through online shopping tools, since some recommendations are suggested related to our purchase. This happens because the recommendation engines work on machine learning. Machine learning also has other applications such as spam filtering, security threat detection, fraud detection, and personalizing news feeds.<\/p>\n<p>Machine learning is majorly categorized into three types: supervised learning, unsupervised learning, and reinforcement learning.<\/p>\n<p><strong>Supervised learning: <\/strong>Supervised machine learning algorithms require external assistance. The external assistance is usually through a human expert who provides curated input for the desired output to predict accuracy in algorithm training. The expert or data scientist determines the features or patterns that the model would use. Once the training is completed, then it can be applied to test another data for the prediction and classification. It is supervised because the algorithm learns from the training data set akin to a teacher supervising the learning process of a student.<\/p>\n<p>Further, supervised learning is divided into two categories, classification and regression. In classification, the output variable is categorized into classes such as \u2018red\u2019 or \u2018green\u2019 or \u2018disease\u2019 or \u2018non-disease\u2019. In regression, the output variable is a real value such as \u2018dollars\u2019 or \u2018weight\u2019.<\/p>\n<p>So, in supervised classifiers a training set is provided to train the machine and it is evaluated with a test set. Most important in these classifiers is how one goes about building a training set. In most cases, having a high quality training set makes or breaks the machine learning. One should also consider the negative data that is provided as part of the training set. Sometimes, it becomes difficult to identify a good negative data set.<\/p>\n<p><em>For example, if I would want to develop\/train a machine to predict if two proteins interact (Protein-Protein interactions or PPI) or not; I would require a positive set of protein sequences\/structures that have been proven to interact physically (such as X-ray crystallography, NMR data) and I would require a negative set of protein sequences\/structures that \u00a0are known to work without interacting with. a partner. In this case, the negative set is relatively large in comparison to the positive set, since the data of known PPI is significantly less as compared to the proteome of an organism. Thus, critically analyzed data is needed and this takes time.<\/em><\/p>\n<p><strong>Unsupervised learning:<\/strong> In unsupervised learning algorithms no external assistance is required. The computer program automatically searches the feature or pattern form the data and groups them into clusters. When we introduce new data for the prediction, then it uses previously learned features to classify the data. This method is very useful in the era of big data because it requires huge amount of training data. It is called unsupervised learning because there is no teacher or supervision involved.<\/p>\n<p>The unsupervised learning is further classified in three classes such as clustering, hierarchical clustering, and Gaussian mixture model. In clustering method, one finds out the relation among similar kind of data and group into clusters. In hierarchical clustering, the data is grouped on the basis of small clusters by some similarity measurement. Then, based on some similar parameter sub-clusters are grouped again. In the Gaussian mixture model, each mixture component presents a unique cluster.<\/p>\n<p><strong>Reinforcement learning:\u00a0<\/strong>In reinforcement learning the decision is made on the basis of taken action that that give more positive outcome. The learner has no knowledge which action to take, it can decide by performing actions and seeing results. So, this learning is depend upon the trial and error [5].<\/p>\n<p>The most promising implementation of machine learning and artificial intelligence is in personalized medicine and in precision medicine. In recent years, many startups have focused on this and have developed pipelines. It is worth waiting to see if these translate into commodities that benefit the common man in the long run.<\/p>\n<h2><span class=\"ez-toc-section\" id=\"Applications_of_Machine_Learning_in_Biology\"><\/span>Applications of Machine Learning in Biology<span class=\"ez-toc-section-end\"><\/span><\/h2>\n<p><strong>Identifying gene coding regions<br \/>\n<\/strong>In the area of genomics, next-generation sequencing has rapidly advanced the field by sequencing a genome in a short time. Thus, an active area machine learning is applied to identifying gene coding regions in a genome. Such gene prediction tools that involve machine learning would be more sensitive than typical homolog based sequence searches.<\/p>\n<p><strong>Structure prediction<\/strong><br \/>\nIn proteomics, we touched upon PPI earlier. But, the use of machine learning in structure prediction has pushed the accuracy from 70% to more than 80%. The use of machine learning in text-mining is quite promising with using training sets to identify new or novel drug targets from multiple journal articles and searching secondary databases.<\/p>\n<p><strong>Neural networks<\/strong><br \/>\nDeep learning is a more recent subfield of machine learning that is the extension of neural network. In deep learning \u201cdeep\u201d refers to the number of layers through which data is transformed. So, deep learning is similar to neural network with multi-layers. These multi-layers nodes try to mimic how the human brain thinks to solve the problems. Neural networks are already used by machine learning. Neural network-based machine learning algorithms needs refined or significant data from raw data sets to perform analysis. But increasing data of genome sequencing made it difficult to process meaningful information and then perform the analysis. Multi layers in neural network filter the information and communicate to each layer and permit to refine the output.<\/p>\n<p>Deep learning algorithms extract features from large data sets like a group of images or genomes and develop a model on the basis of extracted features. Once the model is developed, then algorithms can use the developed model to perform analysis of other data set. T<strong>oday, scientists use deep learning algorithms to perform classification of cellular images, genome analysis, drug discovery and also find out how image data and genome data are link with electronic medical records.<\/strong> Now day\u2019s deep learning is an active field in computational biology. Deep learning applied on high-throughput biological data that help to make better understating about high-dimension data set. In computational biology, deep learning is used in regulatory genomics for the identification of regulatory variants, effect of mutation using DNA sequence, analyzing whole cells, population of cells and tissues [11].<\/p>\n<p><strong>AI in healthcare<\/strong><br \/>\nMachine learning and AI are being used extensively by hospitals and health service providers to improve patient satisfaction, deliver personalized treatments, make accurate predictions and enhance the quality of life. It is also being used to make clinical trials more efficient and help speed up the process of drug discovery and delivery.<\/p>\n<p>To quote the work by Google employing\u00a0<a href=\"https:\/\/blog.kolabtree.com\/the-future-of-artificial-intelligence-in-healthcare\/\">AI in healthcare data<\/a> [17, 18]\n<blockquote><p>Doctors are already inundated with alerts and demands on their attention \u2014 could models help physicians with tedious, administrative tasks so they can better focus on the patient in front of them or ones that need extra attention? Can we help patients get high-quality care no matter where they seek it?<\/p><\/blockquote>\n<p>And from the patient&#8217;s point of view<\/p>\n<blockquote><p>When will I be able to go home? Will I get better? Will I have to come back to the hospital?<\/p><\/blockquote>\n<h2><span class=\"ez-toc-section\" id=\"Machine_Learning_Tools_used_in_Biology\"><\/span>Machine Learning Tools used in Biology<span class=\"ez-toc-section-end\"><\/span><\/h2>\n<p><strong>Cell Profiler<\/strong>: Few years ago, software for biological image analysis only measured single parameter from group of images. As, in 2005, a computational biologist, Anne Carpenter from MIT and Harvard released a software called <a href=\"https:\/\/cellprofiler.org\/\">CellProfiler<\/a> for the measurement of quantitatively individual features like fluorescent cell number in microscopy field. But, currently CellProfiler can produce thousands of features by implementing deep learning techniques.<\/p>\n<p><strong>DeepVariant<\/strong>: Application of deep learning is extensively used in tools for mining genome data. <a href=\"https:\/\/verily.com\">Verily life science<\/a> and Google developed a tool based on deep learning called <a href=\"https:\/\/github.com\/google\/deepvariant\">DeepVariant<\/a> that predicts a common type of genetic variation more accurately in comparison to conventional tools.<\/p>\n<p><strong>Atomwise<\/strong>: Another field is drug discovery in which deep learning contributing significantly. A San Francisco based biotech company called <a href=\"https:\/\/www.atomwise.com\/\">Atomwise<\/a> has developed a algorithm that help to convert molecules into 3D pixels. This representation helps to account the 3D structure of proteins and small molecules with atomic precision. Then by using these features algorithm can predict small molecules that possibly interact with given protein [12].<\/p>\n<p>Different types of deep learning methods exist such as deep neural network (DNN), recurrent neural network (RNN), convolution neural network (CNN), deep autoencoder (DA), deep Boltzman machine (DBM), deep belief network (DBN) and deep residual network (DRN) etc. In the field of biology some methods like, DNN, RNN, CNN, DA and DBM are most commonly used methods [13].\u00a0Translation of biological data to perform validation of biomarkers that reveal disease state is a key task in biomedicine. DNN plays significant role in the identification of potential biomarkers from genome and proteome data. Deep learning also play important role in drug discovery [14].<\/p>\n<p>CNN has been used recently developed computational tool DeepCpG to predict DNA methylation states in single cells. In the DNA methylation, methyl groups associated with DNA molecule and alter the functions of DNA molecule with causing any changes in sequence. DeepCpG also used for the prediction of known motifs that are responsible for methylation variability. DeepCpG predicted more accurate result in comparison to other methods when evaluation using five different types of methylation data. DNA methylation is a most widely studied epigenetic marker [15].<\/p>\n<p><strong>TensorFlow<\/strong> is a deep learning framework developed by Google researchers. TensorFlow is a recently developed software that accelerates DNN design and training. It is implemented in several improvements like graphical visualization and time complication. Main improvement of TensorFlow is that, it available with supporting tools called TensorBoard used for visualization of model training progress. It can provide visualization of a complex model [16].<\/p>\n<p>In conclusion, AI and machine learning are changing the way biologists carry out research, interpret it, and apply it to solve problems. As science grows increasingly interdisciplinary it is only inevitable that biology will continue to borrow from machine learning, or better still, machine learning will lead the way.<\/p>\n<p><strong>Need to hire a <a href=\"https:\/\/www.kolabtree.com\/find-an-expert\/subject\/machine-learning?utm_source=Blog&amp;utm_medium=Post&amp;campaign=MLBiology\">machine learning consultant<\/a> for a project? Consult from freelance experts on Kolabtree. It&#8217;s free to post your project and get quotes!<\/strong><\/p>\n<p><em>Acknowledgement:<\/em> The author would like to thank Mr. Arvind Yadav for assisting in this blog post.<\/p>\n<p><em>References and Further Reading:<\/em><\/p>\n<ol>\n<li>http:\/\/www.bbc.com\/news\/technology-43127533<\/li>\n<li>https:\/\/www.wired.com\/story\/why-artificial-intelligence-researchers-should-be-more-paranoid\/<\/li>\n<li>https:\/\/www.theverge.com\/2018\/2\/20\/17032228\/ai-artificial-intelligence-threat-report-malicious-uses<\/li>\n<li>http:\/\/www.thehindu.com\/opinion\/lead\/the-politics-of-ai\/article22809400.ece?homepage=true<\/li>\n<li>https:\/\/www.economist.com\/news\/science-and-technology\/21713828-silicon-valley-has-squidgy-worlds-biology-and-disease-its-sights-will<\/li>\n<li>Raina, C. K. (2016). A review on machine learning techniques.\u00a0<em>International Journal on Recent and Innovation Trends in Computing and Communication<\/em>,\u00a0<em>4<\/em>(3), 395-399.<\/li>\n<li>Jordan, M. I., &amp; Mitchell, T. M. (2015). Machine learning: Trends, perspectives, and prospects.\u00a0<em>Science<\/em>,\u00a0<em>349<\/em>(6245), 255-260.<\/li>\n<li>Praveena, M., &amp; Jaiganesh, V. (2017). A literature review on supervised machine learning algorithms and boosting process.\u00a0<em>International Journal of Computer Applications<\/em>,\u00a0<em>169<\/em>(8), 32-35.<\/li>\n<li>Forsberg, F., &amp; Alvarez Gonzalez, P. (2018). Unsupervised Machine Learning: An Investigation of Clustering Algorithms on a Small Dataset.<\/li>\n<li>Gosavi, A. (2009). Reinforcement learning: A tutorial survey and recent advances.\u00a0<em>INFORMS Journal on Computing<\/em>,\u00a0<em>21<\/em>(2), 178-192.<\/li>\n<li>Angermueller, C., P\u00e4rnamaa, T., Parts, L., &amp; Stegle, O. (2016). Deep learning for computational biology.\u00a0<em>Molecular systems biology<\/em>,\u00a0<em>12<\/em>(7), 878.<\/li>\n<li>Webb, S. (2018). Deep learning for biology. Nature.\u00a02018 554(7693):555-557.<\/li>\n<li>Mahmud, M., Kaiser, M. S., Hussain, A., &amp; Vassanelli, S. (2018). Applications of deep learning and reinforcement learning to biological data.\u00a0<em>IEEE transactions on neural networks and learning systems<\/em>,\u00a0<em>29<\/em>(6), 2063-2079.<\/li>\n<li>Mamoshina, P., Vieira, A., Putin, E., &amp; Zhavoronkov, A. (2016). Applications of deep learning in biomedicine.\u00a0<em>Molecular pharmaceutics<\/em>,\u00a0<em>13<\/em>(5), 1445-1454.<\/li>\n<li>Angermueller, C., Lee, H. J., Reik, W., &amp; Stegle, O. (2017). DeepCpG: accurate prediction of single-cell DNA methylation states using deep learning.\u00a0<em>Genome biology<\/em>,\u00a0<em>18<\/em>(1), 67.<\/li>\n<li>Rampasek, L., &amp; Goldenberg, A. (2016). Tensorflow: Biology\u2019s gateway to deep learning?.\u00a0<em>Cell systems<\/em>,\u00a0<em>2<\/em>(1), 12-14.<\/li>\n<li>https:\/\/ai.googleblog.com\/2018\/05\/deep-learning-for-electronic-health.html<\/li>\n<li>Rajkomar et al., (2018) &#8220;Scalable and accurate deep learning with electronic health records<em>&#8220;,\u00a0npj Digital Medicine<\/em>, 1(1)<\/li>\n<\/ol>\n","protected":false},"excerpt":{"rendered":"<p>Machine learning has several applications in diverse fields, ranging from healthcare to natural language processing. Dr. Ragothanam Yennamalli, a computational biologist and Kolabtree freelancer, examines the applications of AI and machine learning in biology.\u00a0 Machine Learning and Artificial Intelligence &#8212; these technologies have stormed the world and have changed the way we work and live.<\/p>\n<div class=\"read-more\"><a href=\"https:\/\/www.kolabtree.com\/blog\/applications-of-machine-learning-in-biology\/\" title=\"Read More\">Read More<\/a><\/div>\n","protected":false},"author":26,"featured_media":5410,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":[],"categories":[434,398,443,435],"tags":[],"acf":[],"yoast_head":"<!-- This site is optimized with the Yoast SEO Premium plugin v20.1 (Yoast SEO v20.1) - https:\/\/yoast.com\/wordpress\/plugins\/seo\/ -->\n<title>The Applications of Machine Learning in Biology - The Kolabtree Blog<\/title>\n<meta name=\"description\" content=\"Machine learning in biology has several applications that help scientists conduct and interpret research and apply their learnings to solving problems.\" \/>\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.kolabtree.com\/blog\/applications-of-machine-learning-in-biology\/\" \/>\n<meta property=\"og:locale\" content=\"en_US\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"The Applications of Machine Learning in Biology\" \/>\n<meta property=\"og:description\" content=\"Machine learning in biology has several applications that help scientists conduct and interpret research and apply their learnings to solving problems.\" \/>\n<meta property=\"og:url\" content=\"https:\/\/www.kolabtree.com\/blog\/applications-of-machine-learning-in-biology\/\" \/>\n<meta property=\"og:site_name\" content=\"The Kolabtree Blog\" \/>\n<meta property=\"article:publisher\" content=\"https:\/\/www.facebook.com\/kolabtree\" \/>\n<meta property=\"article:published_time\" content=\"2019-03-15T14:59:00+00:00\" \/>\n<meta property=\"article:modified_time\" content=\"2023-02-15T11:47:02+00:00\" \/>\n<meta property=\"og:image\" content=\"https:\/\/www.kolabtree.com\/blog\/wp-content\/uploads\/2019\/03\/Untitled-design-11.png\" \/>\n\t<meta property=\"og:image:width\" content=\"810\" \/>\n\t<meta property=\"og:image:height\" content=\"450\" \/>\n\t<meta property=\"og:image:type\" content=\"image\/png\" \/>\n<meta name=\"author\" content=\"Ragothaman Yennamalli\" \/>\n<meta name=\"twitter:card\" content=\"summary_large_image\" \/>\n<meta name=\"twitter:creator\" content=\"@kolabtree\" \/>\n<meta name=\"twitter:site\" content=\"@kolabtree\" \/>\n<meta name=\"twitter:label1\" content=\"Written by\" \/>\n\t<meta name=\"twitter:data1\" content=\"Ragothaman Yennamalli\" \/>\n\t<meta name=\"twitter:label2\" content=\"Est. reading time\" \/>\n\t<meta name=\"twitter:data2\" content=\"10 minutes\" \/>\n<!-- \/ Yoast SEO Premium plugin. -->","yoast_head_json":{"title":"The Applications of Machine Learning in Biology - The Kolabtree Blog","description":"Machine learning in biology has several applications that help scientists conduct and interpret research and apply their learnings to solving problems.","robots":{"index":"index","follow":"follow","max-snippet":"max-snippet:-1","max-image-preview":"max-image-preview:large","max-video-preview":"max-video-preview:-1"},"canonical":"https:\/\/www.kolabtree.com\/blog\/applications-of-machine-learning-in-biology\/","og_locale":"en_US","og_type":"article","og_title":"The Applications of Machine Learning in Biology","og_description":"Machine learning in biology has several applications that help scientists conduct and interpret research and apply their learnings to solving problems.","og_url":"https:\/\/www.kolabtree.com\/blog\/applications-of-machine-learning-in-biology\/","og_site_name":"The Kolabtree Blog","article_publisher":"https:\/\/www.facebook.com\/kolabtree","article_published_time":"2019-03-15T14:59:00+00:00","article_modified_time":"2023-02-15T11:47:02+00:00","og_image":[{"width":810,"height":450,"url":"https:\/\/www.kolabtree.com\/blog\/wp-content\/uploads\/2019\/03\/Untitled-design-11.png","type":"image\/png"}],"author":"Ragothaman Yennamalli","twitter_card":"summary_large_image","twitter_creator":"@kolabtree","twitter_site":"@kolabtree","twitter_misc":{"Written by":"Ragothaman Yennamalli","Est. reading time":"10 minutes"},"schema":{"@context":"https:\/\/schema.org","@graph":[{"@type":"Article","@id":"https:\/\/www.kolabtree.com\/blog\/applications-of-machine-learning-in-biology\/#article","isPartOf":{"@id":"https:\/\/www.kolabtree.com\/blog\/applications-of-machine-learning-in-biology\/"},"author":{"name":"Ragothaman Yennamalli","@id":"https:\/\/www.kolabtree.com\/blog\/#\/schema\/person\/61d4584c2ca630dcee91e7a79c417693"},"headline":"The Applications of Machine Learning in Biology","datePublished":"2019-03-15T14:59:00+00:00","dateModified":"2023-02-15T11:47:02+00:00","mainEntityOfPage":{"@id":"https:\/\/www.kolabtree.com\/blog\/applications-of-machine-learning-in-biology\/"},"wordCount":2170,"commentCount":0,"publisher":{"@id":"https:\/\/www.kolabtree.com\/blog\/#organization"},"articleSection":["Artificial Intelligence","Data Science","Healthcare","Research"],"inLanguage":"en-US","potentialAction":[{"@type":"CommentAction","name":"Comment","target":["https:\/\/www.kolabtree.com\/blog\/applications-of-machine-learning-in-biology\/#respond"]}]},{"@type":"WebPage","@id":"https:\/\/www.kolabtree.com\/blog\/applications-of-machine-learning-in-biology\/","url":"https:\/\/www.kolabtree.com\/blog\/applications-of-machine-learning-in-biology\/","name":"The Applications of Machine Learning in Biology - The Kolabtree Blog","isPartOf":{"@id":"https:\/\/www.kolabtree.com\/blog\/#website"},"datePublished":"2019-03-15T14:59:00+00:00","dateModified":"2023-02-15T11:47:02+00:00","description":"Machine learning in biology has several applications that help scientists conduct and interpret research and apply their learnings to solving problems.","breadcrumb":{"@id":"https:\/\/www.kolabtree.com\/blog\/applications-of-machine-learning-in-biology\/#breadcrumb"},"inLanguage":"en-US","potentialAction":[{"@type":"ReadAction","target":["https:\/\/www.kolabtree.com\/blog\/applications-of-machine-learning-in-biology\/"]}]},{"@type":"BreadcrumbList","@id":"https:\/\/www.kolabtree.com\/blog\/applications-of-machine-learning-in-biology\/#breadcrumb","itemListElement":[{"@type":"ListItem","position":1,"name":"Home","item":"https:\/\/www.kolabtree.com\/blog\/"},{"@type":"ListItem","position":2,"name":"The Applications of Machine Learning in Biology"}]},{"@type":"WebSite","@id":"https:\/\/www.kolabtree.com\/blog\/#website","url":"https:\/\/www.kolabtree.com\/blog\/","name":"The Kolabtree Blog","description":"Expert Views on Science, Innovation and Product Development","publisher":{"@id":"https:\/\/www.kolabtree.com\/blog\/#organization"},"potentialAction":[{"@type":"SearchAction","target":{"@type":"EntryPoint","urlTemplate":"https:\/\/www.kolabtree.com\/blog\/?s={search_term_string}"},"query-input":"required name=search_term_string"}],"inLanguage":"en-US"},{"@type":"Organization","@id":"https:\/\/www.kolabtree.com\/blog\/#organization","name":"Kolabtree","url":"https:\/\/www.kolabtree.com\/blog\/","logo":{"@type":"ImageObject","inLanguage":"en-US","@id":"https:\/\/www.kolabtree.com\/blog\/#\/schema\/logo\/image\/","url":"","contentUrl":"","caption":"Kolabtree"},"image":{"@id":"https:\/\/www.kolabtree.com\/blog\/#\/schema\/logo\/image\/"},"sameAs":["https:\/\/www.facebook.com\/kolabtree","https:\/\/twitter.com\/kolabtree","https:\/\/instagram.com\/kolabtree","https:\/\/www.linkedin.com\/company\/kolabtree","https:\/\/en.m.wikipedia.org\/wiki\/Kolabtree"]},{"@type":"Person","@id":"https:\/\/www.kolabtree.com\/blog\/#\/schema\/person\/61d4584c2ca630dcee91e7a79c417693","name":"Ragothaman Yennamalli","image":{"@type":"ImageObject","inLanguage":"en-US","@id":"https:\/\/www.kolabtree.com\/blog\/#\/schema\/person\/image\/","url":"https:\/\/www.kolabtree.com\/blog\/wp-content\/uploads\/2017\/06\/raghu_bt-96x96.jpg","contentUrl":"https:\/\/www.kolabtree.com\/blog\/wp-content\/uploads\/2017\/06\/raghu_bt-96x96.jpg","caption":"Ragothaman Yennamalli"},"description":"Dr. Ragothaman Yennamalli completed his PhD in Computational Biology and Bioinformatics in 2008 from Jawaharlal Nehru University, New Delhi. He conducted postdoctoral research at Iowa State University (2009-2011), University of Wisconsin-Madison (2011-2012), and Rice University (2012-2014). Currently he is an Assistant Professor at Jaypee University of Information Technology, Waknaghat, Himachal Pradesh, India.","url":"https:\/\/www.kolabtree.com\/blog\/author\/ragothaman\/"}]}},"amp_enabled":true,"_links":{"self":[{"href":"https:\/\/www.kolabtree.com\/blog\/wp-json\/wp\/v2\/posts\/2986"}],"collection":[{"href":"https:\/\/www.kolabtree.com\/blog\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/www.kolabtree.com\/blog\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/www.kolabtree.com\/blog\/wp-json\/wp\/v2\/users\/26"}],"replies":[{"embeddable":true,"href":"https:\/\/www.kolabtree.com\/blog\/wp-json\/wp\/v2\/comments?post=2986"}],"version-history":[{"count":25,"href":"https:\/\/www.kolabtree.com\/blog\/wp-json\/wp\/v2\/posts\/2986\/revisions"}],"predecessor-version":[{"id":10463,"href":"https:\/\/www.kolabtree.com\/blog\/wp-json\/wp\/v2\/posts\/2986\/revisions\/10463"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/www.kolabtree.com\/blog\/wp-json\/wp\/v2\/media\/5410"}],"wp:attachment":[{"href":"https:\/\/www.kolabtree.com\/blog\/wp-json\/wp\/v2\/media?parent=2986"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/www.kolabtree.com\/blog\/wp-json\/wp\/v2\/categories?post=2986"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/www.kolabtree.com\/blog\/wp-json\/wp\/v2\/tags?post=2986"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}