A common scenario we come across is that we have a main manuscript document and a supplementary information document, each of which have their own sections, tables and figures. The question then becomes – how do we effectively cross-reference between the documents without having to tediously count all the numbers ourselves every time we make a change and recompile the documents?
The answer: cross referencing!
Continue readingAs a first year PhD student, it is not an uncommon thing to be asked to write a review paper on your subject area. It is both a great way to get acquainted with your research field and to get the background portion of your thesis completed early. However, it can seem like a daunting task to go from knowing almost nothing about your research field to producing something of interest for experts who have spent years studying your subject matter.
In my first year, I was exactly in this position and I found very little online to help guide this process. Thus, here is my reflective look at writing a review paper that will hopefully help someone else in the future.
Continue readingYou might have created the most esthetic figures for your last presentation with a beautiful colour scheme, but have you considered how these might look to someone with colourblindness? Around 5% of the gerneral population suffer from some kind of color vision deficiency, so making your figures more accessible is actually quite important! There are a range of online tools that can help you create figures that look great to everyone.
Continue readingIn an attempt to ease the transition from Word to LaTeX for some of my colleagues (*cough* Alex *cough*) this blog post covers some LaTeX tricks I use most frequently when preparing manuscripts. It’s pitched at someone who is already familiar with the basic syntax of paragraphs, figures and tables.
Continue readingAnalysing, comparing and communicating the predictive performance of machine learning models is a crucial component of any empirical research effort. Pandas, a staple in the Python data analysis stack, not only helps with the data wrangling itself, but also provides efficient solutions for data presentation. Two of its lesser-known yet incredibly useful features are df.to_markdown() and df.to_latex(), which allow for a seamless transition from DataFrames to publication-ready tables. Here’s how you can use them!
We’ve been investigating deep learning-based protein-ligand docking methods which often claim to be able to generate ligand binding modes within 2Å RMSD of the experimental one. We found, however, this simple criterion can conceal a multitude of chemical and structural sins…
DeepDock attempted to generate the ligand binding mode from PDB ID 1t9b
(light blue carbons, left), but gave pretzeled rings instead (white carbons, right).
OPIG’s growing immunoinformatics team continues to develop and openly distribute a wide variety of databases and software packages for antibody/nanobody/T-cell receptor analysis. Below is a summary of all the latest updates (follows on from v1.0 and v2.0).
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If you are like me, you can happily write code for hours and hours on end but as soon as you need to write a paper you end up staring at a blank page. Luckily, I have come up with a fool proof way to trick myself into thinking I am coding when in reality I am finalling getting around to writing up the work my supervisor has been wanting for the last month. Introducing Latex and git- this was my approach to draft a review paper recently and in this blopig post I will go through some of the ups and downs I had using these tools.
Continue readingWhen starting a new project, conducting a literature review of the field can be one of the most daunting prospects. Not only do you need to get through a mountain of research papers, you also need to work out which mountain of papers to get through. You don’t want to start a project only to realise a few weeks (or months!) in that you missed a key paper which would have completely changed the course of your research. Luckily, there are now several handy tools which can help speed up this process.
Continue readingA blog post about reviewing papers and preparing papers for publication.
We start with the following premise: all papers have limitations. There is not a single paper without limitations. A method may not be generally applicable, a result may not be completely justified by the data or a theory may make restrictive assumptions. To cover all limitations would make a paper infinitely long, so we must stop somewhere.
A lot of limitations fall into the following scenario. The results or methods are presented but they could have extended them in some way. Suppose, we obtain results on a particular cell type using an immortalized cell-line. Are the results still true, if we performed the experiments on primary or patient-derived cells? If the signal from the original cells was sufficiently robust then we would hope so. However, we can not be one hundred percent sure. A similar example is a method that can be applied to a certain type of data. It may be possible to extend the method to be applied to other data types. However, this may require some new methodology. I call this flavor of limitations vertical limitations. They are vertical in the sense that they build upon an already developed result in the manuscript. For certain journals, they will require that you tackle vertical limitations by adapting the original idea or method to demonstrate broad appeal or that idea could permeate multiple fields. Most of the time, however, the premise of an approach is not to keep extending it. It works. Leave it alone. Do not ask for more. An idea done well does not need more.
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