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IBM con modelos de regresión para análisis crediticios

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IBM con modelos de regresión para análisis crediticios

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Se utiliza modelo de aprendizaje por refuerzo para el análisis de riesgo de potenciales clientes afectos a créditos.

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https://www.ibm.com/blogs/research/2019/07/ai-tools-for-cancer-research/

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Cancer is the second leading cause of death worldwide[i], with an estimated 18.1 million new cases and 9.6 million deaths attributed to it in 2018[ii]. The search for more effective anti-cancer drugs is a global effort involving academia and industry. In our Computational Systems Biology group at the IBM Research lab in Zurich, we are building machine learning approaches that can potentially help to accelerate our understanding of the leading drivers and molecular mechanisms of these complex diseases, as well as the differences in tumor composition occurring across various cancer types. Our goal is to deepen our understanding of cancer to equip industries and academia with the knowledge that could potentially one day help fuel new treatments and therapies.

At the 18 th European Conference on Computational Biology (ECCB) and the 27 th Conference on Intelligent Systems for Molecular Biology (ISMB) to be held from July 21 -25 in Basel, Switzerland, IBM will present significant, novel research that led to the implementation of three machine learning solutions aimed at accelerating and guiding cancer research. Below is a brief discussion of each of those three advancements.

PaccMann: How deep learning can help predict and explain the efficacy of drugs

Developing and getting even one single anticancer drug approved can cost hundreds of millions of US dollars[iii], which speaks to the potential for greater efficiency along the development pipeline. One way to potentially cut costs in the search for new and better drugs would involve identifying at an early stage which candidate compounds will most-likely prove effective at fighting the targeted disease. For this purpose, we developed PaccMann (Prediction of anticancer compound sensitivity with Multi-modal attention-based neural networks), a multimodal deep learning solution that uses data from disparate sources to help predict how cells in diseased tissue will respond to a given drug. In our research, we applied PaccMann to predict the sensitivity of cancer cell lines to known drugs, achieving a superior predictive power compared to existing algorithms[iv].

PaccMann exploits data on gene expression in the cell line under study, as well as information on the molecular structure of an anticancer compound. Additionally, PaccMann takes into account prior knowledge on protein interactions that are relevant to understanding how a cell line responds to a drug. This previous knowledge is independent on the cell line-drug pair under consideration. In the underlying studywe used PaccMann to predict drug sensitivity on a large number of drug-cell line pairs (more than 200,000 pairs) contained in the GDSC database. On these data, PaccMann not only predicted sensitivity for the drug-cell line pairs more accurately than alternative tools, it also offered explainability, highlighting which specific genes and which portions of the compound's molecular structure it paid the most attention to while performing the predictions. This information can be used by researchers as a guide to potentially help them improve or repurpose existing drugs, as well as to develop new ones.

Herzlichen Glückwunsch an unsere Gewinner des Scientific Excellence Awards: @JannisBorn von @ETH & @UZH_ch und des #Startup Awards: Brian Della Valle von #GLXanalytics #FXH2019 #Roche ? pic.twitter.com/qTGzCPpiDU

- Roche Deutschland (@Roche_de) November 14, 2019

INtERAcT: A tool to automate knowledge extraction from scientific publications

INtERAcT (Interaction Network infErence from vectoR representATions of words) is a novel approach to extract information on protein-protein interactions from scientific papers in a completely unsupervised way.

Normally, the way proteins interact is well-regulated. In diseases like cancer, however, disturbed biological processes are reflected in - and possibly caused by - altered protein-protein interactions. A huge amount of knowledge on these crucial protein interactions is buried as unstructured text, in images and charts contained in scientific publications. While a comprehensive knowledge on protein interactions is fundamental in biomedical research, it would be overwhelming for any scientist to try to access that information by actually reading through all existing publications. According to a recent bibliometric study, around 17,000 scientific articles are published on average every year in the field of cancer research alone - and the output keeps growing exponentially.

A particular strength of INtERAcT is its capability to infer interactions in the context of a specific disease. The comparison with the normal interactions in healthy tissue may potentially help to obtain insight into the disease mechanisms.

Towards interpretable phenotype prediction using pathway information

Predicting disease progression based on molecular data obtained from diseased tissue samples and stratifying, or classifying, patients accordingly is a crucial step to help clinicians better personalize and design effective treatments. Although a number of machine algorithms have been proposed to solve this task, many of them have failed in providing interpretable predictions, a key aspect for adoption of such methodologies in the health care industry.

IBM has created PIMKL (pathway-induced multiple kernel learning), a new machine learning algorithm that can produce highly predictive performance and interpretability in predicting phenotypes based on molecular data. PIMKL enables this by exploiting prior knowledge on molecular interactions. Specifically, we feed PIMKL information on molecular networks and pathways describing well-defined biomolecular process. Using a machine learning technique known as multiple kernel learning, PIMKL identifies molecular pathways that are important for the classification of patient groups. The insights on differences between patient groups provided, thanks to the interpretability of the model, could therefore lead to better understanding of cancer progression.

In our study, we specifically addressed the task of predicting whether a breast cancer patient will suffer a relapse within five years after first treatment. In order to benchmark PIMKL, we compared it to 14 other similar algorithms that have been previously applied to six breast cancer cohorts. PIMKL consistently outperformed its counterparts or ranked among the top-performing algorithms on each single cohort.[vii]

The molecular signatures identified by PIMKL turned out to also be highly relevant when we applied the algorithm on unseen data from a different cohort to that used for training. This is an indication that the knowledge obtained on disease progression can be transferred across datasets and cohorts. A particular strength of PIMKL showed up when we trained it with noisy data from different omic layers, i.e. genetic, epigenetic or protein data. Even then, PIMKL proved able to discard the noise and select the most informative data without reducing its performance.

The three presented algorithms demonstrate how machine learning approaches can be exploited to advance biomedical research on complex diseases such as cancer. Our work also shows that it is possible to incorporate explainabilty into the algorithms, thereby reinforcing trust while also guiding the search for the underlying disease mechanisms. We continue working to further refine these solutions. By making them publicly available, we hope to maximize their positive impact in the scientific community.
References:
[iv] Matteo Manica, Ali Oskooei, Jannis Born, Vigneshwari Subramanian, Julio Sáez- Rodríguez, María Rodríguez Martínez, " Towards Explainable Anticancer Compound Sensitivity Prediction via Multimodal Attention-based Convolutional Encoders ", Workshop on Computational Biology ICML, 2019.

[v] Mikolov, T., Sutskever, I., Chen, K., Corrado, G. & Dean, J. Distributed representations of words and phrases and their compositionality. In Proc. 26th International Conference on Neural Information Processing Systems, NIPS'13 3111-3119

[vi] Matteo Manica, Roland Mathis, Joris Cadow, María Rodríguez Martínez, " Context-specific interaction networks from vector representation of words", Nature Machine Intelligence 1(4), 181-190, 2019.

[vii] Matteo Manica, Joris Cadow, Roland Mathis, María Rodríguez Martínez,
" PIMKL: Pathway-Induced Multiple Kernel Learning",
npj Systems Biology and Applications 5(1), 8, 2019.

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Novel AI tools to accelerate cancer research | IBM Research Blog

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Novel AI tools to accelerate cancer research

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UTILIZA APRENDIZAJE SUPERVISADO A TRAVÉS DE LA RECOPILACION HISTORICA DE TRANSACCIONES
https://www.paypal.com/us/brc/article/enterprise-solutions-paypal-machine-learning-stop-fraud

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Data is the currency of our modern, technology-powered world. It flows from customers to businesses and back again, enriching us all. But in the wrong hands, it can also line the pockets of fraudsters. This is why we need machine learning. Using high-quality data, we can train these complex, intelligent models to help spot suspicious online behavior that human eyes may miss.

Our access to data on more than 350 million consumers and merchants in over 200 markets is therefore a large part of our success in helping merchants detect and prevent fraud. But what kind of data do we need, and how do we use it?

Why machine learning?

Fraud prevention is not only about machine learning. Rules-based systems can also be helpful, by detecting scenarios that occur frequently and enabling us to set up simple "if X then Y" commands. For example: "decline transactions if they come from a specific country." However, rules deal in absolutes, and can't handle the complexity of scenarios that characterize today's user behavior, and the fraud patterns that seek to imitate it.

That's where machine learning comes in. This subset of artificial intelligence enables us to create algorithms that process huge datasets with multiple variables to find correlations at lightning-fast speeds. By training these models with thousands of good and bad transactions, they can be taught to help identity future bad buying behavior independently. This is faster than setting up new rules, is easy to retrain with the latest data, and involves less manual work, reducing operational costs. Ultimately, it's a more adaptive, flexible, and effective approach to fraud prevention.

What data do we need?

However, exactly how effective these tools are depends to a great extent on the quality and type of data fed into machine learning models. Let's take a look at some of the main types of fraud and the kind of data needed.

Signup Fraud is among the fastest-growing types of fraud. It occurs when scammers use stolen identities or create synthetic ones to open a new financial account - typically a bank or credit card. They will usually try to max-out credit limits before disappearing, leaving the victim to pick up the pieces. Signup fraud is difficult to spot as account creation is the first time a company will see that customer, so there's no historical data for comparison, and interaction with them is minimal.

PayPal feeds its algorithms device data, third-party information like email address checks and identity scores, session analysis, and data collected during enrolment to help spot scammers. We build hundreds of signals on top of the data to detect, for example, differences between a user's real and stated location, or difficulty in typing familiar information like first and second names.

Login Fraud is another fast-growing fraud type and involves the hijacking of existing customer accounts, usually through stolen log-ins. Depending on the type of account, a fraudster could siphon off funds, carry out fraudulent transactions or steal and sell personal information. The impact on the victim could range from increased chargebacks, potentially leading to customer churn and brand damage.

PayPal uses machine learning to help assess in real-time if an individual is a legitimate customer or not. Device, email, IP, phone, transaction, and behavioral user information are key here, as are checks for proxies to hide the true location of an individual. The signals we build check for high-risk activity like a large number of log-in attempts, or account changes unlike any seen in the past.
​
Payment fraud involves the use of card details without the actual cardholder's knowledge. Previous transaction data can be used for comparison to check for fraud here, alongside device, email, IP, phone, user, and address information. Typical indicators of malicious activity include a shipping-billing address mismatch, unusually large orders, and the use of multiple different cards for purchases shipped to the same address. A combination of these signals can indicate a higher chance of fraud.

The power of PayPal

There are no hard and fast rules for how much data machine learning models need to function effectively. But a good rule of thumb is "as much as possible." The further back in time data goes the more useful it can be to this kind of modeling, just as the more complex the algorithm, the more data you need. Understanding good and bad events more accurately during training will also require a comprehensive dataset, including edge cases.

Fortunately, this is where the PayPal 2-Sided Network really comes into its own. The scale of insight we have into transaction data-from both a customer and merchant perspective-is the fuel for our machine learning algorithms. This data ultimately helps to reduce consumer friction, lower fraud losses, and enhance customer trust. More importantly, it's a system which will continue to adapt even as the fraudsters constantly evolve their tools and tactics, to help keep us one step ahead.

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fraud detection with deep learning at paypal

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transaction fraud detection machine learning

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artificial intelligence to detect fraudulent account activity

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Leveraging Machine Learning for Fraud Prevention| PayPal US

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Data is the currency of our modern, technology-powered world. It flows from customers to businesses and back again, enriching us all. But in the wrong hands, it can also line the pockets of fraudsters. This is why we need machine learning.

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Machine Learning utilizando clasificación de imágenes

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Machine Learning utilizando clasificación de imágenes

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Esta es una plataforma en donde los usuarios pueden dejar sus comentarios de evaluación sobre lugares donde han ido, por ejemplo, restaurantes, tiendas.
Yelp recurre al Machine Learning supervisado utilizando tecnología de clasificación de imagenes. Los algoritmos de ML de Yelp ayudan al personal humano de la compañía a compilar, categorizar y etiquetar las imágenes.

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Predecir qué quieren los clientes, en qué cantidad y cuándo lo querrán resulta fundamental para el éxito de cualquier organización
Es posible generar predicciones relacionadas con el comportamiento de la demanda de un producto y la necesidad de recursos. Amazon Forecast ha cambiado esta situación, ya que facilita la generación de predicciones rápidas y precisas mediante la combinación de datos de series temporales y otras variables que son relevantes para las necesidades únicas de cada cliente.

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Qué es el Machine learning y cómo funciona en la Industria 4.0

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Qué es el Machine learning y cómo funciona en la Industria 4.0

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El machine learning, o aprendizaje automático, es un concepto muy presente en las tecnologías introducidas en la industria por la Revolución 4.0. Esta naturaleza altamente tecnológica ya es una indicación de la complejidad del tema, que involucra elementos de diversas áreas del conocimiento.

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Machine learning: ventajas y aplicaciones en la industria

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Sabes qué es el Machine Learning? su concepto, muy utilizado por la Industria 4.0 posee diversas aplicaciones prácticas.