[{"innerBlocks":[],"name":"core\/freeform","postId":2817,"blockType":{"name":"core\/freeform","keywords":[],"attributes":{"content":{"type":"string","source":"raw"},"lock":{"type":"object"}},"providesContext":[],"usesContext":[],"selectors":[],"supports":{"className":false,"customClassName":false,"reusable":false},"styles":[],"variations":[],"apiVersion":3,"title":"Classic","description":"Use the classic WordPress editor.","category":"text"},"originalContent":"Researchers at the Georgia Institute of Technology have developed a machine learning algorithm that accurately predicts the efficacy of cancer drugs and are making it freely available to the open source community in the hope of generating further medical breakthroughs.

Leveraging raw genetic data, the software tool was demonstrated to be about 85 percent accurate in assessing the treatment effectiveness of nine drug therapies in 273 cancer patients. Results of the study<\/a>, conducted by Georgia Tech researchers, were published October 26 in the journal PLOS One<\/i>.

\u201cBy making our algorithm \u2018open source,\u2019 we hope to facilitate its testing in a variety of cancer types and contexts leading to community-driven improvements and refinements in subsequent applications,\u201d state the authors of the article. \u201cIt is our hope that through community sharing of this and other open source cancer drug prediction algorithms and associated data formatting\/normalization procedures that the attainment of a major goal of personalized cancer medicine will be facilitated.\u201d

Also See<\/b>: Machine learning automatically identifies brain tumors<\/a>

John McDonald, an author of the article and director of Georgia Tech\u2019s Integrated Cancer Research Center, says models for nine drugs were individually built using gene expression and drug response data from the National Cancer Institute panel of 60 human cancer cell lines.

\u201cWe selected nine drugs that are commonly used in the treatment of ovarian cancer,\u201d he adds. \u201cThe algorithm goes through an optimization step where it looks for what features in the gene expression profiles correlate most highly with response to the drug.\u201d

According to McDonald, the models were ultimately tested against the gene expression datasets of 273 ovarian cancer patient tumors. However, he points out that the models were built using data from nine different types of cancer in an effort to reduce human bias about which data are important for predicting outcomes.

\u201cIntuitively, it would seem if you\u2019re going to apply a model to ovarian cancer, you should build it using ovarian cancer samples exclusively\u2014we did not do that,\u201d McDonald notes. \u201cWe found out later that we were getting better accuracy in predicting ovarian cancer response when the models were built across all of these cancer types.\u201d

When it comes to making predictions through machine learning, he contends that preselecting data for what researchers suspect are most relevant is a mistake. \u201cIt\u2019s much more effective to put in loads of raw data and let the algorithm sort it out\u201d rather than limiting the gene expression data to a specific type of cancer. \u201cThe computer narrows it down and looks for the most informative features,\u201d says McDonald. \u201cThe algorithm can improve because you\u2019re giving it more data to learn from.\u201d

On a molecular level, he believes some ovarian cancers are more similar to some breast cancers than to other ovarian cancers. \u201cIt would explain why we\u2019re getting better accuracy in building the models across a diversity of cancer types,\u201d concludes McDonald. \u201cFor that reason, I think the predictions could apply to more than just ovarian cancer\u2014that\u2019s my guess, but we haven\u2019t tested it yet.\u201d

Georgia Tech\u2019s open-source software can be accessed here<\/a> on GitHub and is available for download.","saveContent":"Researchers at the Georgia Institute of Technology have developed a machine learning algorithm that accurately predicts the efficacy of cancer drugs and are making it freely available to the open source community in the hope of generating further medical breakthroughs.

Leveraging raw genetic data, the software tool was demonstrated to be about 85 percent accurate in assessing the treatment effectiveness of nine drug therapies in 273 cancer patients. Results of the study<\/a>, conducted by Georgia Tech researchers, were published October 26 in the journal PLOS One<\/i>.

\u201cBy making our algorithm \u2018open source,\u2019 we hope to facilitate its testing in a variety of cancer types and contexts leading to community-driven improvements and refinements in subsequent applications,\u201d state the authors of the article. \u201cIt is our hope that through community sharing of this and other open source cancer drug prediction algorithms and associated data formatting\/normalization procedures that the attainment of a major goal of personalized cancer medicine will be facilitated.\u201d

Also See<\/b>: Machine learning automatically identifies brain tumors<\/a>

John McDonald, an author of the article and director of Georgia Tech\u2019s Integrated Cancer Research Center, says models for nine drugs were individually built using gene expression and drug response data from the National Cancer Institute panel of 60 human cancer cell lines.

\u201cWe selected nine drugs that are commonly used in the treatment of ovarian cancer,\u201d he adds. \u201cThe algorithm goes through an optimization step where it looks for what features in the gene expression profiles correlate most highly with response to the drug.\u201d

According to McDonald, the models were ultimately tested against the gene expression datasets of 273 ovarian cancer patient tumors. However, he points out that the models were built using data from nine different types of cancer in an effort to reduce human bias about which data are important for predicting outcomes.

\u201cIntuitively, it would seem if you\u2019re going to apply a model to ovarian cancer, you should build it using ovarian cancer samples exclusively\u2014we did not do that,\u201d McDonald notes. \u201cWe found out later that we were getting better accuracy in predicting ovarian cancer response when the models were built across all of these cancer types.\u201d

When it comes to making predictions through machine learning, he contends that preselecting data for what researchers suspect are most relevant is a mistake. \u201cIt\u2019s much more effective to put in loads of raw data and let the algorithm sort it out\u201d rather than limiting the gene expression data to a specific type of cancer. \u201cThe computer narrows it down and looks for the most informative features,\u201d says McDonald. \u201cThe algorithm can improve because you\u2019re giving it more data to learn from.\u201d

On a molecular level, he believes some ovarian cancers are more similar to some breast cancers than to other ovarian cancers. \u201cIt would explain why we\u2019re getting better accuracy in building the models across a diversity of cancer types,\u201d concludes McDonald. \u201cFor that reason, I think the predictions could apply to more than just ovarian cancer\u2014that\u2019s my guess, but we haven\u2019t tested it yet.\u201d

Georgia Tech\u2019s open-source software can be accessed here<\/a> on GitHub and is available for download.","order":0,"get_parent":{},"attributes":[],"attributesType":{"content":{"type":"string","source":"raw"},"lock":{"type":"object"}},"dynamicContent":""}]

Georgia Tech makes machine learning algorithm available to fight cancer

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