Data Science for Science

  Credit: Acropolis Museum/Socratis Mavrommatis Ancient civilizations have held fascination for historians and anthropologists ever since humans became curious about the past. Deciphering life in civilizations past meant that excavated tools, vessels, assorted items, inscriptions etc. had to be unearthed and examined. The most informative of these would be the inscriptions on scrolls, papyri, stone, metal, or pottery. A lot of these inscriptions are incomplete due to damage and large chunks of the inscribed text are often illegible. Determining where the texts originated from can be a challenge since they may have been moved multiple times. Dating these texts is another challenge as radiocarbon dating and similar methods can't be used since they can damage the priceless artifacts. Enter AI. Researchers from DeepMind collaborated with researchers from the University of Oxford to develop Pythia , an ancient-text restoration system based on Deep Learning. The database used was of the 

The Coma Cluster contains more than 1,000 galaxies. Scientists have long been frustrated by large uncertainties in its mass. Credit: PSC  A galaxy, as you may know, is a huge collection of stars that has a certain overall shape and which moves as a unit across the cosmos. Even larger groupings such as galaxy clusters  exist. The individual galaxies in a cluster move about relative to each other and revolve around their common center of mass. Their velocities relative to our observation point can be gleaned from measurements of their red-shifts or blue-shifts.  Estimation of the mass of galaxy clusters is still fraught with uncertainties. It is not yet clear how to determine the three-dimensional structure of galaxy clusters which reduces further the confidence of estimating the mass.  But why estimate the mass? It is now known that stars in a galaxy revolve around the center of mass of the galaxy. But their velocities exceed the calculated velocity based on the detectable mass of the

  Hello! Today we have an entry in the field of medicine. What is skin cancer recurrence? An early-stage skin cancer type called  melanoma  is treatable if diagnosed early enough. Most melanoma deaths in the US occur because of recurrence of the disease which was early stage at the time of diagnosis. However, recurrence is not detected until the cancer starts to spread; called symptomatic metastatic progression. This is where prognostic tools can be helpful for regular surveillance and quick action to stave off mortality and ensure health. Also, identifying high-risk patients can help in determining who should receive special therapies and treatments. Enter Machine Learning. A team led by investigators at Massachusetts General Hospital performed two types Machine Learning prediction with nine different models: Melanoma Recurrence Classification: Where the prediction would be a probability that melanoma recurrence would occur. Time-To-Event Melanoma Recurrence Risk Prediction: Where the

      (Credit: Retinal scan courtesy UK Biobank) Artificial Intelligence can now identify patients with a high risk of heart attack just by an eye scan that can be taken at an optician or eye clinic. Sounds too good to be true? The size and pumping efficiency of the left ventricle (one of the four chambers) of the heart is linked to risk of heart attack. An oversized left-ventricle leads to higher risk of heart disease. In turn, the retinal scans are linked to the size and pumping efficiency of the left ventricle. Currently, risk of heart disease can be determined through expensive diagnostic tests using MRI and ECG which are available only in a hospital setting. These tests are also unavailable in less well-resourced healthcare systems in developing countries and unnecessarily increase healthcare costs in developed countries.  Retinal scans on the other hand are comparatively cheap and available in many optician practices. Patients who are found to be at high risk of heart disease c

    (Photo credit: BBC) Are We Alone?  It is a question that humans have had ever since we realized there are other worlds, other planets out there in the cosmos. The implications of the answer to this question are staggering no matter what that answer is. In pursuit of an answer scientists have been searching for planets that could potentially harbor life. Planets that are outside of our solar system are termed Exoplanets. The exploratory pursuit is to find "earth-like" exoplanets because these would have the greatest chance of harboring life as we know it. Determining the habitability of exoplanets is not an easy task. Existing ML methods such as Metric-based quantification (using metrics like surface temperature) or Supervised Learning (based mainly on estimated surface temperature) are not reliable enough.  Astronomers from the Indian Institute of Astrophysics along with astronomers from BITS Pilani and Goa campus designed an anomaly detection method to identify potential

  Drug development for the treatment of various diseases is the cornerstone of medicine for human health. Drug development is an expensive and time-consuming process. Most drugs developed up to the human-trials stage end up having no effect at all or too many side-effects. These drugs are thus discarded resulting in wasted time, money and effort. The cost of these drugs end up on the price tags of drugs that do become successful. Before drug development can even take place the drug discovery process must occur. Briefly, drug discovery involves a process of finding promising drug-like molecules that can bind or "dock" properly onto certain protein targets. After successfully docking to the protein, the binding drug, also known as the ligand, can stop a protein from functioning. If this happens to an essential protein of a bacterium, it can kill the bacterium, conferring protection to the human body. Drug discovery, the process of discovering new candidate medications, involves

Hubble Deep Field. Courtesy: Encyclopedia Britannica Space is vast. The number of objects in the Universe is literally astronomical. The first step toward exploring these objects is to identify them aka classify them. Objects could be stars, galaxies, quasars, supernovae etc. Due to the sheer number and complexity of objects the task is very challenging to do manually. The further away a source of light is, the harder it is to distinguish its features and thus classify it. Astronomers have now sought the aid of AI in this task. A machine learning pipeline called SHEEP has been developed. SHEEP first extracts the photometric redshift of celestial objects and then uses this as one of the data features fed into an ML model for training. SHEEP combines several ML algorithms: XGBoost , LightGBM , and Catboost to obtain better classification performance. SHEEP contains two distinct classification methodologies: (i) Multi-class and (ii) one versus all with correction by a meta-learner.