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According to a new study, Drug Ranking Using Machine Learning (DRUML) can accurately rank cancer therapies by efficacy across a range of cancer types.

Researchers from Queen Mary University of London, UK, have developed a machine learning algorithm that ranks drugs based on their efficacy in reducing cancer cell growth. According to the developers of Drug Ranking Using Machine Learning (DRUML), in the future the approach could advance personalised therapies by enabling oncologists select the best drugs to treat individual cancer patients.

One of the problems in cancer treatment, is that different people respond differently to the same treatments. This is because, despite tumours being classified as the same type, they exhibit a huge amount of variation in their genetic makeup and characteristics between patients. The field of personalised medicine is attempting to address this issue by combining genetic insights with other clinical and diagnostic information to identify patterns that can allow clinicians to predict patient responses to therapies and select the most effective interventions.

The application of artificial intelligence and machine learning to biomedicine, as was done in the study by the Queen Mary University, is one method being used to promote the development and adoption of personalised medicine and transform how cancers are diagnosed and treated in the future.

DRUML was trained using datasets derived from proteomics and phosphoproteomics analyses of 48 leukaemia, oesophagus and liver cancer cell lines responding to over 400 drugs. Based on these results it produces ordered lists predicting which drug will be most effective at reducing cancer cell growth. The team verified the predictive accuracy of DRUML using data obtained from 12 other laboratories and a clinical dataset of 36 primary acute myeloid leukaemia samples.

According to the developers, one of the most important features of the method is that, as new drug are developed, it could be retrained to include them in its predictions as well.

Speaking of the new method, Professor Pedro Cutillas from Queen Mary University of London, who led the study, remarked: DRUML predicted drug efficacy in several cancer models and from data obtained from different laboratories and in a clinical dataset. These are exciting results because previous machine learning methods have failed to accurately predict drug responses in verification datasets and they demonstrate the robustness and wide applicability of our method.

The research was funded by The Alan Turing Institute, Medical Research Council, Barts Charity and Cancer Research UK.

The method was published in Nature Communications.

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Machine learning used to predict most effective cancer drugs - European Pharmaceutical Review

This article was originally published here

Biomed Eng Online. 2021 Mar 25;20(1):31. doi: 10.1186/s12938-021-00865-9.

ABSTRACT

INTRODUCTION: The use of machine learning (ML) methods would improve the diagnosis of respiratory changes in systemic sclerosis (SSc). This paper evaluates the performance of several ML algorithms associated with the respiratory oscillometry analysis to aid in the diagnostic of respiratory changes in SSc. We also find out the best configuration for this task.

METHODS: Oscillometric and spirometric exams were performed in 82 individuals, including controls (n = 30) and patients with systemic sclerosis with normal (n = 22) and abnormal (n = 30) spirometry. Multiple instance classifiers and different supervised machine learning techniques were investigated, including k-Nearest Neighbors (KNN), Random Forests (RF), AdaBoost with decision trees (ADAB), and Extreme Gradient Boosting (XGB).

RESULTS AND DISCUSSION: The first experiment of this study showed that the best oscillometric parameter (BOP) was dynamic compliance, which provided moderate accuracy (AUC = 0.77) in the scenario control group versus patients with sclerosis and normal spirometry (CGvsPSNS). In the scenario control group versus patients with sclerosis and altered spirometry (CGvsPSAS), the BOP obtained high accuracy (AUC = 0.94). In the second experiment, the ML techniques were used. In CGvsPSNS, KNN achieved the best result (AUC = 0.90), significantly improving the accuracy in comparison with the BOP (p < 0.01), while in CGvsPSAS, RF obtained the best results (AUC = 0.97), also significantly improving the diagnostic accuracy (p < 0.05). In the third, fourth, fifth, and sixth experiments, different feature selection techniques allowed us to spot the best oscillometric parameters. They resulted in a small increase in diagnostic accuracy in CGvsPSNS (respectively, 0.87, 0.86, 0.82, and 0.84), while in the CGvsPSAS, the best classifiers performance remained the same (AUC = 0.97).

CONCLUSIONS: Oscillometric principles combined with machine learning algorithms provide a new method for diagnosing respiratory changes in patients with systemic sclerosis. The present studys findings provide evidence that this combination may help in the early diagnosis of respiratory changes in these patients.

PMID:33766046 | DOI:10.1186/s12938-021-00865-9

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Machine learning associated with respiratory oscillometry: a computer-aided diagnosis system for the detection of respiratory abnormalities in...

CHAMPAIGN, Ill. Monitoring the progression of multiple sclerosis-related gait issues can be challenging in adults over 50 years old, requiring a clinician to differentiate between problems related to MS and other age-related issues. To address this problem, researchers are integrating gait data and machine learning to advance the tools used to monitor and predict disease progression.

A new study of this approach led by University of Illinois Urbana Champaign graduate student Rachneet Kaur, kinesiology and community health professor Manuel Hernandez and industrial and enterprise engineering and mathematics professor Richard Sowers is published in the journal Institute of Electrical and Electronics Engineers Transactions on Biomedical Engineering.

Multiple sclerosis can present itself in many ways in the approximately 2 million people that it affects globally, and walking problems are a common symptom. About half of the patients need walking assistance within 15 years of onset, the study reports.

We wanted to get a sense of the interactions between aging and concurrent MS disease-related changes, and whether we can also differentiate between the two in older adults with MS, Hernandez said. Machine-learning techniques seem to work particularly well at spotting complex hidden changes in performance. We hypothesized that these analysis techniques might also be useful in predicting sudden gait changes in persons with MS.

Using an instrumented treadmill, the team collected gait data normalized for body size and demographics from 20 adults with MS and 20 age-, weight-, height- and gender-matched older adults without MS. The participants walked at a comfortable pace for up to 75 seconds while specialized software captured gait events, corresponding ground reaction forces and center-of-pressure positions during each walk. The team extracted each participants characteristic spatial, temporal and kinetic features in their strides to examine variations in gait during each trial.

Changes in various gait features, including a data feature called the butterfly diagram, helped the team detect differences in gait patterns between participants. The diagram gains its name from the butterfly-shaped curve created from the repeated center-of-pressure trajectory for multiple continuous strides during a subjects walk and is associated with critical neurological functions, the study reports.

We study the effectiveness of a gait dynamics-based machine-learning framework to classify strides of older persons with MS from healthy controls to generalize across different walking tasks and over new subjects, Kaur said. This proposed methodology is an advancement toward developing an assessment marker for medical professionals to predict older people with MS who are likely to have a worsening of symptoms in the near term.

Future studies can provide more thorough examinations to manage the studys small cohort size, Sowers said.

Biomechanical systems, such as walking, are poorly modeled systems, making it difficult to spot problems in a clinical setting, Sowers said. In this study, we are trying to extract conclusions from data sets that include many measurements of each individual, but a small number of individuals. The results of this study make significant headway in the area of clinical machine learning-based disease-prediction strategies.

Hernandez also is affiliated with the Beckman Institute of Advanced Science and Technology and the theCarle Illinois College of Medicine.

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Machine learning helps spot gait problems in individuals with multiple sclerosis - University of Illinois News

Scope: Global Machine Learning MarketThe global Machine Learning market report includes the analysis of all the important aspects associated with the Machine Learning market. The detailed study on the CAGR at which the market is anticipated to expand in the future is provided in the study. The detailed information regarding market valuation at different times is included in the report. The market study also covers the study of varying dynamics of the Machine Learning industry.

Vendor Landscape and Profiling:Microsoft Corporation (Washington, US), IBM Corporation (New York, US), SAP SE (Walldorf, Germany), SAS Institute Inc. (North Carolina, US), Google, Inc. (California, US), Amazon Web Services Inc. (Washington, US), Baidu, Inc. (Beijing, China), BigML, Inc. (Oregon, US), Fair Isaac Corporation (FICO) (California, US), Hewlett Packard Enterprise Development LP (HPE) (California, US), Intel Corporation (California, US), KNIME.com AG (Zurich, Switzerland), RapidMiner, Inc. (Massachusetts, US), Angoss Software Corporation (Toronto, Canada), H2O.ai (California, US), Alpine Data (California, US), Domino Data Lab, Inc. (California, US), Dataiku (Paris, France), Luminoso Technologies, Inc. (Massachusetts, US), TrademarkVision (Pennsylvania, US), Fractal Analytics Inc. (New Jersey, US), TIBCO Software Inc. (California, US), Teradata (Ohio, US), Dell Inc. (Texas, US), and Oracle Corporation (California, US)

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Machine Learning Market Predominant Trends and Growth Opportunities by 2028: Microsoft Corporation (Washington, US), IBM Corporation (New York, US),...

This article was originally published here

IEEE Rev Biomed Eng. 2021 Mar 26;PP. doi: 10.1109/RBME.2021.3069213. Online ahead of print.

ABSTRACT

COVID-19 is a life threatening disease which has a enormous global impact. As the cause of the disease is a novel coronavirus whose gene information is unknown, drugs and vaccines are yet to be found. For the present situation, disease spread analysis and prediction with the help of mathematical and data driven model will be of great help to initiate prevention and control action, namely lockdown and qurantine. There are various mathematical and machine-learning models proposed for analyzing the spread and prediction. Each model has its own limitations and advantages for a particluar scenario. This article reviews the state-of-the art mathematical models for COVID-19, including compartment models, statistical models and machine learning models to provide more insight, so that an appropriate model can be well adopted for the disease spread analysis. Furthermore, accurate diagnose of COVID-19 is another essential process to identify the infected person and control further spreading. As the spreading is fast, there is a need for quick auotomated diagnosis mechanism to handle large population. Deep-learning and machine-learning based diagnostic mechanism will be more appropriate for this purpose. In this aspect, a comprehensive review on the deep learning models for the diagnosis of the disease is also provided in this article.

PMID:33769936 | DOI:10.1109/RBME.2021.3069213

Read more from the original source:
A Survey on Mathematical, Machine Learning and Deep Learning Models for COVID-19 Transmission and Diagnosis - DocWire News