Tag: Data Science

Data Science Products
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Data Science Products: Top 3 Things You Must Know

Introduction

Ever wondered why Clive Humby famously coined the 'Data is new oil' phrase?  Well, this blog article tells you exactly what he meant. The latest advancements in data analytics, cloud infrastructure, and increased emphasis on making data-driven decisions have opened up several avenues for developing Data Science Products.  People can build amazing data-based products that can generate revenues. In other words, the data is the new money-making machine. In this article, we will discuss the top 3 things that you must know about data science products.

# 1: What is a Data Science Product?

Data Science Product is a new era money-making machine that is fueled by data and built using machine learning techniques. It takes data as input and gives out valuable business insights as an output.

#2: Examples of Data-based Products 

Classic examples of data products include Google search and Amazon product recommendations, both these products improve as more users engage. But the opportunity for building data-based products extends far beyond the tech giants. These days companies of all range of sizes and across almost all sectors are investing in their own data-powered products.  Some inspirational examples of data science products that are developed by non-tech giants are as below:

HealthWorks

It mimics consumer choice in Medicare Advantage. The product compares and contrasts more than 5000+ variables across plan costs, plan benefits, market factors, regulatory changes, and many more. It helps Health Plans identify the top attributes that lead to plan competitiveness, predict enrolments, design better products and create winning plans. 

Cognitive Claims Assistant

Damage assessment in vehicles is an important step for insurance claims and auto finance industry. Currently, these processes involve manual interventions requiring a long turnaround time. Cognitive Claims Assistant (CCA) by Genpact automates this process. The data product not only reduces cost and time in the process but also accurately estimates the cost of repairs.

#3: How to Build Data Powered Products?

Steps in making data science product

Do you want to build a data science product too? Here are the five steps that will help you to build a good data science product:

Step 1: Ideation and Design of Data Product

Ideation

The first step of building a data science product is Conceptualizing the product. Conceptualization starts with identifying potential opportunities. A good data science product is the one that solves a critical business need. An unsolved business need that can be solved using data is an opportunity for building data products.

Design

Design the data structure that you will need to solve the business need. This often involves brainstorming on various data inputs and their corresponding valuable outputs that will solve the business need.

Step 2: Get the Raw Data

The second step in building data products is getting the data. If you already own the data, you are already covered for this. All you have to do is move on to the next step. If you don’t have the data then you need to generate or gather it.

Step 3: Refine the Data

As they rightly say, data is the new oil but it is of no use until it is refined like an oil. Understand the structure of your data. Refine, clean, and pre-process it if it is unstructured. Always remember the golden rule-‘Garbage in is Garbage out!’ Knowing the data helps you clearly define the inputs and outputs from your data science product.

Step 4: Data Based Product Development

This is the most tricky part in data science product development and needs a strong knowledge of the business process, business needs, statistics, mathematics, and coding. This knowledge forms the backbone of the data product. In the majority of the cases, this step involves building a machine learning model using domain knowledge. In some cases, it could also involve simple graphical outputs for exploratory analysis of the data. No matter what is the output the codes developed for executing the desired process need to be tested and validated for real-life use of the data product.

Step 5: Release!

This is the last step in data product development. In this step, tried, tested, and validated data science product is deployed on a cloud. The data product buyers can simply log in from anywhere in the world and use the product.

Conclusion

Anybody who owns the treasure trove of the data should develop a ‘Data Science Product’ or a ‘Data Product’. Now the question arises, is it possible to build data products without coding knowledge? And the answer is, absolutely yes! You can use our data analytics platforms that are specially built for non-coders. All you have to do is arrange your data meaningfully and just make few clicks to build your base model described in Step 4 of How to build data products as described above. When you deploy the model on the cloud your money-making machine becomes a reality. If you don’t like the idea of doing it all yourself, then you always have an option to outsource.

Like any other product, the success of the data product is dependent on its usability. Half the battle is won with a strong business case. The remaining battle can be won with mathematics, statistics, and computer science. This is exactly where we can contribute. Our aim to accelerate the data product development process. Let's unite your domain knowledge and data with our data modeling capabilities. Let's build amazing data science products!

Reach out to us Now!

Alphafold
Blog

AlphaFold – AI breakthrough unfolding a new path for Protein Research

‘Structure is Function’ – is a principle on which biology is based. 

Introduction

Proteins are essentially the crux of life – be it in a single cell (enzymes, hormones, etc) or outside of our bodies in our surroundings (food, medicines, etc). The function of all these proteins is dependent solely on its 3D structure and any change in this structure, alters its performance.

Nobel Laureate Christian Anfinsen believed that ‘the folding pattern and 3D structure of any protein can be determined from its amino acid sequence’.  Further to his experiments and based on his theories, numerous research groups have been working relentlessly on the ‘Protein-Folding Problem’ to figure out how and why the protein attains only a specific conformation out of the multitude of possible folding patterns. 

Predicting Quaternary Structure of Protein from Primary Structure

Conventionally, scientists have been using techniques like X-ray crystallography, Nuclear Magnetic Resonance, Cryo-electron microscopy etc to try and elucidate the 3D protein structure. These experiments involve years of laborious work and require investment worth millions of dollars.

CASP Challenge 2020

Recently, on the 30th of November, 2020, a new milestone in Protein Research was achieved when the results of the 14th biennial Critical Assessment of Protein Structure Prediction (CASP) challenge were announced. AlphaFold – a program developed by DeepMind, an Artificial Intelligence research lab affiliated with Google and its parent company was successful in predicting protein structure from its amino acid sequence comparable with the experimental results with a GDT score of 92.4. 

The CASP challenge held by the Protein Structure Prediction Centre at the University of California, was started by Prof John Moult and his co-founders in 1994 with an aim of boosting computational research in protein biology. The assessment was based on Global Distance Test (GDT) on a 0-100 scale, depending on the similarity of the predicted structure with the experimental results. 

Figure: (Source- deepmind.com/blog), Improvements in the median accuracy of predictions in the free modelling category for the best team in each CASP, measured as best-of-5 GDT

As can be seen from the graph above, the GDT scores over the years were very low implying poor resemblance between the experimental and computational outcomes.

AlphaFold

DeepMind entered the competition for the first time in 2018 with its AlphaFold program and outwit all the participants with a GDT score of more than 60. Though not close enough to being accurate, it was definitely a hope for a successful model. AlphaFold in 2020 could predict even the most challenging protein structure with a GDT score of 87 (25 GDT points higher than its competitor)

AlphaFold: The key to the protein folding problem

AlphaFold in 2018 predicted the distance between pairs of amino acids in a protein based on structural and genetic data using deep learning, a subset of AI. However, this approach could not lead them any further. John Jumper, the project lead for AlphaFold. The team then resorted to some different thinking strategies. Jumper mentions that they started developing the program based on the principles of biology, physics, machine learning and years of experience and work of the experts in the field of protein folding over the past five decades.

How did they develop it?

This time, they also included additional information about the physical and geometrical constraints that play a role in determining the 3D conformation of a protein. The program was developed to fulfill the tough task of predicting the final protein structure of the target protein.

Jumper explained that if we consider a folded protein to be a ‘spatial graph’, then the amino acid residues can be said to be nodes and the residues in close proximity can be considered to be connected by edges.

The latest version of AlphaFold used at CASP14, was based on a neural network system and trained with the publicly available protein data bank which consists of around 170000 protein structures. In addition to this, the program was also trained with other large databases having protein sequences whose structures were not yet deduced. AlphaFold can predict the protein structure with the repetitive application of a system which involves the use of  evolutionarily related sequences, multiple sequence alignment (MSA), and a representation of amino acid residue pairs to refine this graph. This whole prediction process requires only a few days as opposed to the years of experimental research.

The impact

Professor Andrei Lupas (Director of the Max Planck Institute for Developmental Biology, Germany and a judge for the CASP challenge) said that his lab was working on a bacterial protein for almost a decade but could not really put together the information obtained from the X-ray diffraction data. But, with the insights on the protein shape from AlphaFold, they could deduce the structure in half an hour. Professor Lupas complimented the DeepMind team on the accuracy of the model and claimed that this new milestone will help them in their quest to understand how signals are transmitted across cell membranes.

So what role does this new landmark play in the common world?

AlphaFold cannot and will not definitely replace the experimental research, but as Professor Lupas commented, “It’s going to require more thinking and less pipetting.”

This quicker method for the prediction of protein structure, can assist the scientists in faster experiments and lesser investment in times of time and infrastructure. The spared time and money can be better utilized to study the function of proteins, effect of structure on function, cause of changes in protein structure, and the implications such alterations have for example in various diseases such as Parkinsons, Alzheimers, etc. This AI miracle, AlphaFold can serve as a boon to researchers and aid in drug discovery, designing protein drugs, developing enzymes for various applications, and much more. It can revolutionize research, solving a number of unsolved issues while creating several new unexplored avenues, unthinkable at the moment. 

A very recent application of AlphaFold is the prediction of COVID-19 proteins Orf3a and Orf8, the knowledge of which will certainly have a significant place in understanding the various aspects of the viral infection as well as in the development of vaccines.

Conclusion

To total it all, a statement by Professor Lupas can be quoted: “It’s a game changer. This will change medicine. It will change research. It will change bioengineering. It will change everything”

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