Bioinformatics and Genomics

18 May 2020

Adaptive divergence for rapid adversarial optimization

Maxim Borisyak, Tatiana Gaintseva, Andrey Ustyuzhanin

Charles Elkan –– Generative adversarial networks (GANs) are an exciting development in machine learning, but it is hard to make them work in practice, and they can require a lot of training data. This research is a significant step towards making GANs more usable.

15 May 2020

Spirometric traits show quantile-dependent heritability, which may contribute to their gene-environment interactions with smoking and pollution

Paul T. Williams

Andrew Gray –– This article addresses an important question about the heritability of pulmonary function, namely whether this is constant or varies by level of function. This could provide further insight into gene-environment interactions and potentially inform interventions aimed at reducing declines in pulmonary function.

13 May 2020

The mitochondrial genome of Acrobeloides varius (Cephalobomorpha) confirms non-monophyly of Tylenchina (Nematoda)

Taeho Kim, Yucheol Lee, Hyun-Jong Kil, Joong-Ki Park

Jia-Yong Zhang –– This research provide the complete mitochondrial genome of Acrobeloides varius.

11 May 2020

The spindle assembly checkpoint and speciation

Robert C. Jackson, Hitesh B. Mistry

Ugo Bastolla –– This paper is important in the context of sympatric speciation, where it supports a speciation mechanism based on the variation of the number of chromosomes through sound molecular biology reasoning and mathematical modelling.

7 May 2020

Integrated analysis of lymphocyte infiltration-associated lncRNA for ovarian cancer via TCGA, GTEx and GEO datasets

Meijing Wu, Xiaobin Shang, Yue Sun, Jing Wu, Guoyan Liu

Walter de Azevedo Jr. –– In this study, Wu et al identified long non-coding RNA-associated competing endogenous RNA (ceRNA) axes that could delineate more reliable prognostic parameters of high-grade serous ovarian cancer, and to examine the long non-coding RNAs’ possible mechanism of in lymphocyte infiltration. The authors were able to define numerous prognostic biomarkers for the incidence and development of high-grade serous ovarian cancer and constructed a network for ceRNA axes.

6 May 2020

Identification of key miRNAs in the progression of hepatocellular carcinoma using an integrated bioinformatics approach

Qi Zheng, Xiaoyong Wei, Jun Rao, Cuncai Zhou

Walter de Azevedo Jr. –– Previously studies indicated the association of aberrant expression of microRNAs (miRNAs) and transcriptional factors (TFs) with the development of hepatocellular carcinoma. In work conducted by Zheng et al., the authors investigated potential transcriptomic markers of hepatocellular carcinoma using the analysis of microarray datasets. They used datasets available in the GEO database. Their analysis indicated that two miRNAs—hsa-mir-106b and hsa-mir-195 are identified as potential classifiers of hepatocellular carcinoma.

29 April 2020

A non-linear reverse-engineering method for inferring genetic regulatory networks

Siyuan Wu, Tiangang Cui, Xinan Zhang, Tianhai Tian

Walter de Azevedo Jr. –– In this manuscript conducted by Wu et al., the authors proposed a new method to infer the detailed regulatory mechanisms involved in hematopoiesis. This work focused on the development of a computational approach to investigate the effect of possible protein heterodimers and/or synergistic effects on genetic regulation. In summary, the authors proposed a method that is an effective approach to infer the topological structure and dynamic property of genetic regulations.

29 April 2020

ECMPride: prediction of human extracellular matrix proteins based on the ideal dataset using hybrid features with domain evidence

Binghui Liu, Ling Leng, Xuer Sun, Yunfang Wang, Jie Ma, Yunping Zhu

Walter de Azevedo Jr. –– In this work conducted by Liu et al, the authors described the development of a new computational tool to study human extracellular matrix proteins. The authors used experimentally verified extracellular matrix datasets and a machine learning model to develop ECMPride, a flexible and scalable computational tool for predicting extracellular matrix proteins. ECMPride has a number of putative extracellular matrix proteins as well as several biological annotations. This tool might contribute to research focused on the identification of members of this important class of proteins.

21 April 2020

RNA sequencing of CD4 T-cells reveals the relationships between lncRNA-mRNA co-expression in elite controller vs. HIV-positive infected patients

Chaoyu Chen, Xiangyun Lu, Nanping Wu

Walter de Azevedo Jr. –– In the present study, the authors examined the expression profiles of long non-coding RNAs and mRNAs from CD4+ T cells from two elite controllers, two HIV-positive infected patients, and two healthy controls. They built a co-expression network based on the relationships among differentially expressed transcripts and database annotations. So far, this is the first report to examine gene transcription in elite controllers and to investigate their functional relationships. Their results provide a reference for subsequent functional corroboration at the cellular level or even at a molecular scale.

15 April 2020

Predicting the risk of sarcopenia in elderly patients with patellar fracture: development and assessment of a new predictive nomogram

Yi-sheng Chen, Yan-xian Cai, Xue-ran Kang, Zi-hui Zhou, Xin Qi, Chen-ting Ying, Yun-peng Zhang, Jie Tao

Liang Gao –– The authors successfully developed and assessed a new predictive nomogram for predicting the risk of sarcopenia in elderly patients with patellar fractures.