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Researchers from NVIDIA and Harvard University have introduced a machine learning-driven toolkit calledAtacWorksthat has the potential to bring about remarkable advancements in genome sequencing.

What is genome sequencing?

Genome sequencing was introduced by British biochemist Frederick Sanger and his team in 1977. The world was fascinated by how this new technology could uncover human similarity and genetic diversity in new ways.

A genome is a map of all the nucleotides in our body, andgenome sequencingis a technique used to generate this nucleotide map to decode our DNA. The human genome consists of over 3 billion nucleotides.

Genome sequencing has helped scientists figure out the location of various genes and how they work together to ensure the growth and maintenance of organisms. It has served as an essential tool in the study of hereditary diseases and genetic abnormalities.

Current challenges and limitations of genome sequencing techniques

The traditional technique,ATAC-seq, measures the intensity of signals across the genome and plots the data in a graph. However, ATAC-seq can perform DNA sequencing efficiently only if it has access to many cells. We need a large number of cells (in the order of thousands) to carry out reasonably efficient genome sequencing. The fewer cells available, the noisier the data, and the more challenging it is to analyze rare cell types.

In addition, the traditional process is time-consuming. This poses a significant challenge to studying genetic mutations in organisms, like viruses, that rapidly mutate.

Introducing AtacWorks: the latest game-changer in genome sequencing

A machine learning driven toolkit called,AtacWorks,was created by researchers from NVIDIA and Harvard University to help address some of the challenges we face in genome sequencing.

AtacWorks is a Pytorch basedConvolutional Neural Network (CNN)trained to differentiate between data and noise and pick out peaks in a noisy data set. AtacWorks can be combined with ATAC-seq data to obtain the same quality data using lesser data points (lesser number of cells in this case). Researchers have found that AtacWorks can produce the same quality data from 1 million data points as was earlier done using 50 million data points.

In addition, AtacWorks helps speed up analysis by usingtensor core GPUs. This makes it possible to complete the full genome analysis in just 30 minutes a radical difference compared to the traditional 15-hour time frame.

In theresearch paper published in Nature Communications, Harvard researchers applied AtacWorks to a dataset of stem cells that produce red and white blood cells. Stem cells are rare cell types and are often found in very small numbers in the human body at a time.

Using a sample of just 50 stem cells, the researchers were able to identify distinct regions of the DNA of a stem cell that causes it to evolve into a red blood cell or a white blood cell. They were also able to isolate DNA sequences that correspond to red blood cells.

This remarkable breakthrough made by machine learning in genome sequencing has the potential to lead to the discovery of new drugs and explore evolution through the study of new mutations.

Source: https://ngc.nvidia.com/catalog/resources/nvidia:atacworks

Paper: https://www.nature.com/articles/s41467-021-21765-5

Github: https://github.com/clara-parabricks/AtacWorks

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NVIDIA and Harvard University Researchers Introduce AtacWorks: A Machine Learning Toolkit to Revolutionize Genome Sequencing - MarkTechPost

This article was originally published here

Immun Inflamm Dis. 2021 Mar 13. doi: 10.1002/iid3.421. Online ahead of print.

ABSTRACT

BACKGROUND: Identifying patients who may develop severe coronavirus disease 2019 (COVID-19) will facilitate personalized treatment and optimize the distribution of medical resources.

METHODS: In this study, 590 COVID-19 patients during hospitalization were enrolled (Training set: n = 285; Internal validation set: n = 127; Prospective set: n = 178). After filtered by two machine learning methods in the training set, 5 out of 31 clinical features were selected into the model building to predict the risk of developing severe COVID-19 disease. Multivariate logistic regression was applied to build the prediction nomogram and validated in two different sets. Receiver operating characteristic (ROC) analysis and decision curve analysis (DCA) were used to evaluate its performance.

RESULTS: From 31 potential predictors in the training set, 5 independent predictive factors were identified and included in the risk score: C-reactive protein (CRP), lactate dehydrogenase (LDH), Age, Charlson/Deyo comorbidity score (CDCS), and erythrocyte sedimentation rate (ESR). Subsequently, we generated the nomogram based on the above features for predicting severe COVID-19. In the training cohort, the area under curves (AUCs) were 0.822 (95% CI, 0.765-0.875) and the internal validation cohort was 0.762 (95% CI, 0.768-0.844). Further, we validated it in a prospective cohort with the AUCs of 0.705 (95% CI, 0.627-0.778). The internally bootstrapped calibration curve showed favorable consistency between prediction by nomogram and the actual situation. And DCA analysis also conferred high clinical net benefit.

CONCLUSION: In this study, our predicting model based on five clinical characteristics of COVID-19 patients will enable clinicians to predict the potential risk of developing critical illness and thus optimize medical management.

PMID:33713584 | DOI:10.1002/iid3.421

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Construction and validation of a machine learning-based nomogram: A tool to predict the risk of getting severe coronavirus disease 2019 (COVID-19) -...

While more companies are warming up to AI, AI platforms are being taught to screen for specific cue words to detect bias or abuse.

While the focus on checking human biases from getting coded into artificial intelligence (AI) is desirable, there is a need for the developing AI that is intelligent about biases and contexts, too. The Indian Express reports that the reason behind YouTube AI banning Agadmator, a popular chess channel on the platform last year, could be the use of white, black and attackwhich mean different things in chess and in race-relations.

While more companies are warming up to AI, AI platforms are being taught to screen for specific cue words to detect bias or abuse. So, in this case, with the use of the particular words, YouTube AI read racism where there was none. How poorly human understanding is being translated for machines is evident from not just this case, but also from that of Microsofts Tay-bot, that all too quickly picked up anti-Semitic and hateful content from the internet when it should have been designed to filter this out contextually.

While the need will be to continuously go back to the AI drawing board, human control of AIs learning and other machine-learning will be important to set the context for the machines.

AI ethics is surely a minefieldbusiness interests, as various analyses of the recent episode at Google involving the termination of two senior ethics experts at the company suggest, could sometimes come into conflict with the larger good. But, as research translates human understanding for machines more effectively, chances are both Tay-bot and Youtubes reported AI gaffe, at the other extreme, will become rarer.

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Machine Learning: Long way to go for AI bias-correction; some hurl abuses, others see abuse where theres none - The Financial Express

Bloomberg

(Bloomberg) -- Alarm bells are starting to ring across emerging markets as countries brace for a new era of rising interest rates.After an unprecedented period of rate cuts to prop up economies shattered by Covid-19, Brazil is expected to raise rates this week and Nigeria and South Africa could follow soon, according to Bloomberg Economics. Russia already stopped easing earlier than expected and Indonesia may do the same.Behind the shift: Renewed optimism in the outlook for the world economy amid greater U.S. stimulus. Thats pushing up commodity-price inflation and global bond yields, while weighing on the currencies of developing nations as capital heads elsewhere.The turn in policy is likely to inflict the greatest pain on those economies that are still struggling to recover or whose debt burdens swelled during the pandemic. Moreover, the gains in consumer prices, including food costs, that will prompt the higher rates may exact the greatest toll on the worlds poorest.The food-price story and the inflation story are important on the issue of inequality, in terms of a shock that has very unequal effects, said Carmen Reinhart, the chief economist at the World Bank, said in an interview, citing Turkey and Nigeria as countries at risk. What you may see are a series of rate hikes in emerging markets trying to deal with the effects of the currency slide and trying to limit the upside on inflation.Investors are on guard. The MSCI Emerging Markets Index of currencies has dropped 0.5% in 2021 after climbing 3.3% last year. The Bloomberg Commodity Index has jumped 10%, with crude oil rebounding to its highest levels in almost two years.Rate increases are an issue for emerging markets because of a surge in pandemic-related borrowing. Total outstanding debt across the developing world rose to 250% of the countries combined gross domestic product last year as governments, companies and households globally raised $24 trillion to offset the fallout from the pandemic. The biggest increases were in China, Turkey, South Korea and the United Arab Emirates.What Bloomberg Economics Says...The tide is turning for emerging-market central banks. Its timing is unfortunate -- most emerging markets have yet to fully recover from the pandemic recession.-- Ziad Daoud, chief emerging markets economistClick here for the full reportAnd theres little chance of borrowing loads easing any time soon. The Organisation for Economic Co-operation and Development and the International Monetary Fund are among those that have warned governments not to remove stimulus too soon. Moodys Investors Service says its a dynamic thats here to stay.While asset prices and debt issuers market access have largely recovered from the shock, leverage metrics have shifted more permanently, Colin Ellis, chief credit officer at the ratings company in London, and Anne Van Praagh, fixed-income managing director in New York, wrote in a report last week. This is particularly evident for sovereigns, some of which have spent unprecedented sums to fight the pandemic and shore up economic activity.Further complicating the outlook for emerging markets is they have typically been slower to roll out vaccines. Citigroup Inc. reckons such economies wont form herd immunity until some point between the end of the third quarter of this year and the first half of 2022. Developed economies are seen doing so by the end of 2021.The first to change course will likely be Brazil. Policy makers are forecast to lift the benchmark rate by 50 basis to 2.5% when they meet Wednesday. Turkeys central bank, which has already embarked on rate increases to shore up the lira and tame inflation, convenes the following day, with a 100 basis-point move in the cards. On Friday, Russia could signal tightening is imminent.Nigeria and Argentina could then raise their rates as soon as the second quarter, according to Bloomberg Economics. Market metrics show expectations are also building for policy tightening in India, South Korea, Malaysia and Thailand.Given higher global rates and what is likely to be firming core inflation next year, we pull forward our forecasts for monetary policy normalization for most central banks to 2022, from late 2022 or 2023 earlier, Goldman Sachs Group Inc. analysts wrote in a report Monday. For RBI, the liquidity tightening this year could morph into a hiking cycle next year given the faster recovery path and high and sticky core inflation.Some countries may still be in a better position to weather the storm than during the taper tantrum of 2013 when bets on cuts in U.S. stimulus triggered capital outflows and sudden gyrations in foreign-exchange markets. In emerging Asia, central banks have built up critical buffers, partly by adding $468 billion to their foreign reserves last year, the most in eight years.Yet higher rates will expose countries, such as Brazil and South Africa, that are ill-positioned to stabilize the debt theyve run up in the past year, Sergi Lanau and Jonathan Fortun, economists at the Washington-based Institute of International Finance, said in a report last week.Relative to developed markets, the room low rates afford emerging markets is more limited, they wrote. Higher interest rates would reduce fiscal space significantly. Only high-growth Asian emerging markets would be able to run primary deficits and still stabilize debt.Among those most at risk are markets still heavily dependent on foreign-currency debt, such as Turkey, Kenya and Tunisia, William Jackson, chief emerging markets economist at Capital Economics in London, said in a report. Yet local-currency sovereign bond yields also have risen, hurting Latin American economies most, he said.Other emerging markets could be forced to put off their own fiscal measures following the passage of the $1.9 trillion U.S. stimulus plan, a danger underlined by Nomura Holdings Inc. more than a month ago.Governments may be tempted to follow Janet Yellens clarion call to act big this year on fiscal policy, to continue to run large or even larger fiscal deficits, Rob Subbaraman, head of global markets research at Nomura in Singapore, wrote in a recent report. However, this would be a dangerous strategy.The net interest burden of emerging-market governments is more than three times that of their developed-market counterparts, while emerging markets are both more inflation-prone and dependent on external financing, he said.In addition to South Africa, Nomura highlighted Egypt, Pakistan and India as markets where net interest payments on government debt surged from 2011 to 2020 as a share of output.(Updates with analyst comment in paragraph after Read More box, updates yield data in chart.)For more articles like this, please visit us at bloomberg.comSubscribe now to stay ahead with the most trusted business news source.2021 Bloomberg L.P.

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When managing patients with asthma, a major goal is to reduce hospital visits resulting from the disease. Some healthcare centers are now using machine learning predictive models to determine which patients with asthma are highly likely to experience poor outcomes in the future. Machine learning is a state-of-the-art method for gaining high prediction accuracy, explains Gang Luo, PhD. While it has great potential to improve healthcare, most machine learning models are black boxes and dont explain their predictions, creating a barrier for use in clinical practice. This has been a well-known problem associated with machine learning for many years.

Predicting & Explaining Asthma Hospitalization Risk

Recently, Dr. Luo and colleagues built an extreme gradient boosting (XGBoost) machine learning model to predict asthma hospital visits in the subsequent year for patients with asthma. This XGBoost model was found to be more accurate than previous models, but like most machine learning models, it did not offer explanations as to why patients were at risk for poor outcomes. To overcome this barrier, Dr. Luo and colleagues conducted a studypublished in JMIR Medical Informaticsin which they developed a method to automatically explain the models prediction results and suggest tailored interventions without lowering any of the models performance measures.

The automatic explanation function was able to explain prediction results for 89.7% of patients with asthma who were correctly predicted to incur asthma hospital visits in the subsequent year. This percentage is high enough to support routine clinical use of this method. Of note, the researchers also presented several sample rule-based explanations provided by the function to illustrate how the function worked (Table).

Suggesting Tailored Asthma Interventions

For the first time, our study showed that we can automatically provide rule-based explanations and suggest tailored interventions for predictions from any black-box machine learning predictive model built on tabular data without degrading any of the models performance measures, says Dr. Luo. This occurs regardless of whether the outcome of interest has a skewed distribution. Clinicians were able to understand the rule-based explanations. Among all automatic explanation methods for machine learning predictions, our method is the only one that can automatically suggest interventions.

According to Dr. Luo, clinicians previously needed to manually review long patient records and think of interventions on their own. This consumes a lot of time, is labor intensive, and may lead to missing important information and interventions, he says. Our method can serve as a reminder system to help prevent clinicians from missing these opportunities. It also greatly speeds up processes, because the summary information is presented directly to clinicians and doesnt require sifting through long patient records to make an informed decision.

The study team notes that the automatic explanation function should be viewed as a reminder for decision support rather than a replacement for clinical judgment. It is still the clinicians responsibility to use their own judgment to decide whether to use the models prediction results and apply suggested interventions to their patients. If there are any doubts, clinicians are recommended to check their patients records before making final decisions on any recommendations.

Impacting Clinician Use of Machine Learning for Patients With Asthma

After further improvement of model accuracy, using the asthma outcome prediction model together with the automatic explanation function could help with decision support to guide the allocation of limited asthma care management resources. This could help boost asthma outcomes and reduce resource use and costs.

Predicting hospital visits for patients with asthma is an urgent need for asthma care management, which is widely used to improve outcomes, Dr. Luo says. Researchers have been working on this problem for at least two decades but have repeatedly encountered problems with low prediction accuracy. Our model significantly improved prediction accuracy. In addition, we can now automatically explain the prediction results. These are important factors that impact the willingness of clinicians to use our model in clinical practice. In future research, we plan to test our automatic explanation method on more predictive modeling problems, such as in different prediction targets and diseases.

Luo G, Johnson MD, Nkoy FL, He S, Stone BL. Automatically explaining machine learning prediction results on asthma hospital visits in patients with asthma: secondary analysis. JMIR Med Inform.2020;8(12):e21965. Available at: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7808890/.

Luo G, He S, Stone BL, Nkoy FL, Johnson MD. Developing a model to predict hospital encounters for asthma in asthmatic patients: secondary analysis. JMIR Med Inform. 2020;8(1):e16080.

Luo G. Automatically explaining machine learning prediction results: a demonstration on type 2 diabetes risk prediction. Health Inf Sci Syst. 2016;4:2.

Luo G, Stone BL, Sakaguchi F, Sheng X, Murtaugh MA. Using computational approaches to improve risk-stratified patient management: rationale and methods. JMIR Res Protoc. 2015;4(4):e128.

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Enhancing Machine Learning Prediction to Improve Asthma Care Management - Physician's Weekly