Recent studies have reported hundreds of genes linked to Alzheimer's Disease (AD). However, many of these candidate genes may be not identified in different studies when analyses were replicated. Moreover, results could be controversial. Here, we proposed a computational workflow to curate and evaluate AD related genes. The method integrates large scale literature knowledge data and gene expression data that were acquired from postmortem human brain regions (AD case/control: 31/32 and 22/8). Pathway Enrichment, Sub-Network Enrichment, and Gene-Gene Interaction analysis were conducted to study the pathogenic profile of the candidate genes, with 4 metrics proposed and validated for each gene. By using our approach, a scalable AD genetic database was developed, including AD related genes, pathways, diseases and info of supporting references. The AD case/control classification supported the effectiveness of the 4 proposed metrics, which successfully identified 21 well-studied AD genes (i.g. TGFB1, CTNNB1, APP, IL1B, PSEN1, PTGS2, IL6, VEGFA, SOD1, AKT1, CDK5, TNF, GSK3B, TP53, CCL2, BDNF, NGF, IGF1, SIRT1, AGER and TLR) and highlighted one recently reported AD gene (i.g. ITGB1). The computational biology approach and the AD database developed in this study provide a valuable resource which may facilitate the understanding of the AD genetic profile.

Studies to date have reported hundreds of genes connected to bipolar disorder (BP). However, many studies identifying candidate genes have lacked replication, and their results have, at times, been inconsistent with one another. This paper, therefore, offers a computational workflow that can curate and evaluate BP-related genetic data. Our method integrated large-scale literature data and gene expression data that were acquired from both postmortem human brain regions (BP case/control: 45/50) and peripheral blood mononuclear cells (BP case/control: 193/593). To assess the pathogenic profiles of candidate genes, we conducted Pathway Enrichment, Sub-Network Enrichment, and Gene-Gene Interaction analyses, with 4 metrics proposed and validated for each gene. Our approach developed a scalable BP genetic database (BP_GD), including BP related genes, drugs, pathways, diseases and supporting references. The 4 metrics successfully identified frequently-studied BP genes (e.g. GRIN2A, DRD1, DRD2, HTR2A, CACNA1C, TH, BDNF, SLC6A3, P2RX7, DRD3, and DRD4) and also highlighted several recently reported BP genes (e.g. GRIK5, GRM1 and CACNA1A). The computational biology approach and the BP database developed in this study could contribute to a better understanding of the current stage of BP genetic research and assist further studies in the field.

In recent years, numerous studies reported over a hundred of genes playing roles in the etiology of postmenopausal osteoporosis (PO). However, many of these candidate genes were lack of replication and results were not always consistent. Here, the authors proposed a computational workflow to curate and evaluate PO related genes. They integrate large-scale literature knowledge data and gene expression data (PO case/control: 10/10) for the marker evaluation. Pathway enrichment, sub-network enrichment, and gene-gene interaction analysis were conducted to study the pathogenic profile of the candidate genes, with four metrics proposed and validated for each gene. By using the authors' approach, a scalable PO genetic database was developed; including PO related genes, diseases, pathways, and the supporting references. The PO case/control classification supported the effectiveness of the four proposed metrics, which successfully identified eight well-studied top PO genes (e.g. TGFB1, IL6, IL1B, TNF, ESR2, IGF1, HIF1A, and COL1A1) and highlighted one recently reported PO genes (e.g. IFNG). The computational biology approach and the PO database developed in this study provide a valuable resource which may facilitate understanding the genetic profile of PO.

Background: Attention deficit hyperactivity disorder (ADHD) is a psychiatric disorder of the neuro-developmental type, marked by an ongoing pattern of inattention or hyperactivity/impulsivity, which interferes with functioning or development. The disorder affects approximately 5-7 % children and 2-5 % of adults worldwide. Numerous studies have indicated that genetic factors predominate the causes for ADHD. Nevertheless, no systematic study has summarized these findings and provided an objective and complete list of genes with a reported association to ADHD. Methods: Literature and enrichment metrics analyses were used to discover genes of specific significance associated with ADHD. We conducted a literature data mining (LDM) of over 2,410 articles covering publications from Jan. 1988 to Apr. 2016, where 235 genes were reported to be associated with the disease. Then we performed a gene set enrichment analysis (GSEA) and a sub-network enrichment analysis (SNEA) to study the functional profile and pathogenic significance of these genes associated with ADHD. Lastly, we performed a network connectivity analysis (NCA) to study the associations between the reported genes. Results: 181/235 genes enriched 100 pathways (p<1.1e-007), demonstrating multiple associations with ADHD. Twelve genes were discovered to be associated with ADHD, in terms of both functional diversity and replication frequency, including SLC6A3, DRD4, BDNF, DRD2, HTR2A, DBH, HTR1B, DRD5, GRM7, DRD3, TH and GRIN2A. In addition, one novel gene, SHANK2, was suggested worthy of further study. Moreover, SNEA and NCA results indicated that many of these genes form a functional network, playing roles in the pathogenesis of other ADHD related disorders. Conclusion: Our results suggest that the genetic causes of ADHD are linked to a genetic and functional network composed of a large group of genes. The gene lists, together with the literature and enrichment metrics provided in this study, could serve as groundwork for further biological/genetic studies in the field.

Background: Renal cancer (RC) originates in the cells of the kidneys. Worldwide, approximately 208,500 new cases of renal cancer are diagnosed annually. This accounts for just under 2 % of all cancers. Those with a family history of RC have an increased risk of developing the disease. Recent research has identified hundreds of genes which may relate to its development. No study has systematically summarized these findings or provided an objective view of the genes reportedly associated with RC. Methods: Literature data mining (LDM) was performed on more than 1,100 articles for publications between 1988 and April 2016 in which 423 genes were reported to be RC-associated. A gene set enrichment analysis (GSEA) and a sub-network enrichment analysis (SNEA) were performed to study the functional profile and pathogenic significance of these genes. A network connectivity analysis (NCA) to study the associations between the reported genes was done. Literature, and enrichment metrics, analyses were used to identify genes with specific RC significance. Results: Multiple RC associations for 329 of the 423 genes enriched 100 pathways (p < 1.2e-10) were demonstrated. Ten genes (IL6,