Translational bioinformatics and data science for biomarker discovery in mental health: an analytical review

Abstract

Translational bioinformatics and data science play a crucial role in biomarker discovery as it enables translational research and helps to bridge the gap between the bench research and the bedside clinical applications. Thanks to newer and faster molecular profiling technologies and reducing costs, there are many opportunities for researchers to explore the molecular and physiological mechanisms of diseases. Biomarker discovery enables researchers to better characterize patients, enables early detection and intervention/prevention and predicts treatment responses. Due to increasing prevalence and rising treatment costs, mental health (MH) disorders have become an important venue for biomarker discovery with the goal of improved patient diagnostics, treatment and care. Exploration of underlying biological mechanisms is the key to the understanding of pathogenesis and pathophysiology of MH disorders. In an effort to better understand the underlying mechanisms of MH disorders, we reviewed the major accomplishments in the MH space from a bioinformatics and data science perspective, summarized existing knowledge derived from molecular and cellular data and described challenges and areas of opportunities in this space.

Keywords: biomarker discovery; data science; mental health informatics; neuroscience; translational bioinformatics.

Figure 1

Alzheimer’s disease gene interaction network input to this network: TREM2, PLD3, DLGPAP1, TMEM51, EIF2AK2, APP, PSEN1, PSEN2, APOE. Genes that are known to interact with each other are connected by cyan lines (information obtained from curated databases) or magenta lines (experimentally determined connections). The genes that could be in the same neighborhood are connected by green lines, those that could have gene fusions are linked by red lines and those genes that could co-occur are linked by blue lines.

Figure 2

Top enriched gene ontology biological processes in AD. The x axis shows the negative log base 10 of the adjusted P value. The y axis indicates the various GO terms enriched.

Figure 3

Gene interaction networks in MDD Input gene list: SORCS3, NEGR1, NR3C2, NR3C1, MTRNRL8, SERPINH1, CCL4, SLC1A2, GABRD, HTR1A, HTR1B, HTR2A, HTR2C, PXMP2, EEF2, RPL26L1, RPLP0, PRPF8, LSM3, DHX9, RSRC1, AP2B1 Genes that are known to interact with each other are connected by cyan lines (information obtained from curated databases) or magenta lines (experimentally determined connections). The genes that could be in the same neighborhood are connected by green lines, those that could have gene fusions are linked by red lines and those genes that could co-occur are linked by blue lines.

Figure 4

microRNAs and genes associated with PTSD enriched with common cell processes and diseases. The figure shows an interaction network between the microRNAs and genes associated with PTSD. First connections between the microRNAs and genes were found and then the common cell processes and diseases were added to the network. The microRNAs are represented as red parallelograms, the cell processes are the yellow colored boxes and the diseases are the purple colored boxes.