[notes] More Is Better: Recent Progress in Multi-Omics Data Integration Methods

For essays that shed light on the background and present research, it is relatively easy to make the structure apparent to the audience. However, it is not quite so for making it less brusque. Here are the opening paragraphs that I found as a case study to improve my writing.

A new era of personalized medicine has arrived, which proposes an individualized health care model with tailored medical target treatment and management for each patient (Chin et al., 2011). Under this regime, not only clinical profiles of patients but also their molecular profiles are personally managed to drive for advanced treatment. Cancer studies that are focused on one-dimensional omics data have only provided limited information regarding the etiology of oncogenesis and tumor progression. To overcome this, tremendous efforts have been made to obtain multi-platform based genomic data from biospecimen.

Background introduction with specific cases to depict the application scenarios is way more reader-friendly than solely emphasizing the importance. (Question: how do you find or imagine the actual application? Especially when the topic came from a minor issue, though it’s indeed helping for something, it’s hard to make it both specific and not too niche.)

Multi-platform-based molecular profiles look like social networks and user portraits. Perhaps we can transfer some techniques between them.

However, human genomes are complex and regulated at multiple levels, which can be manifested by various genomic assays mentioned above. While each of these assays offers a peek at the complex system, these events are rather interdependent (or interactive). Thus, when combining several different omics data to discover the coherent biological signatures, it is challenging to incorporate different biological layers of information to predict phenotypic outcomes (tumor/normal, early/late stage, survival, etc.). It is herein our goal to address the pressing and challenging issues for developing novel algorithms and theoretical methods for multi-omics data integration, in the hope to extract biologically meaningful information of clinical relevance.

Again, the structure is pretty straightforward: the problem, the present works are good but limited, our attempt, our challenge, and our goal. It’s also helpful to use higher vocabulary to make it sound more professional, like coherent biological signatures, incorporate(instead of combine), herein(instead of thus), and novel(against new).