Artificial Intelligence (AI) has recently altered the landscape of cancer research and medical oncology using traditional Machine Learning (ML) algorithms and cutting-edge Deep Learning (DL) architectures. In this review article we focus on the ML aspect of AI applications in cancer research and present the most indicative studies with respect to the ML algorithms and data used. The PubMed and dblp databases were considered to obtain the most relevant research works of the last five years. Based on a comparison of the proposed studies and their research clinical outcomes concerning the medical ML application in cancer research, three main clinical scenarios were identified. We give an overview of the well-known DL and Reinforcement Learning (RL) methodologies, as well as their application in clinical practice, and we briefly discuss Systems Biology in cancer research. We also provide a thorough examination of the clinical scenarios with respect to disease diagnosis, patient classification and cancer prognosis and survival. The most relevant studies identified in the preceding year are presented along with their primary findings. Furthermore, we examine the effective implementation and the main points that need to be addressed in the direction of robustness, explainability and transparency of predictive models. Finally, we summarize the most recent advances in the field of AI/ML applications in cancer research and medical oncology, as well as some of the challenges and open issues that need to be addressed before data-driven models can be implemented in healthcare systems to assist physicians in their daily practice.

SHBG levels and the SHBG (TAAAA)(n) polymorphism are associated with follicle size.

FSHR/SHBG/CYP19 combined genotypes are associated with ovarian response to standard gonadotrophin stimulation of women undergoing medically assisted reproduction.

Retroelement transcripts are present in male and female gametes, where they are typically regulated by methylation, noncoding RNAs and transcription factors. Such transcripts are required for occurrence of retrotransposition events, while failure of retrotransposition control may exert negative effects on cellular function and proliferation. In order to investigate the occurrence of retrotransposition events in mouse epididymal spermatozoa and to address the impact of uncontrolled retroelement RNA expression in early preimplantation embryos, we performed in vitro fertilization experiments using spermatozoa preincubated with plasmid vectors containing the human retroelements LINE-1, HERVK-10 or the mouse retroelement VL30, tagged with an enhanced green fluorescence (EGFP) gene-based cassette. Retrotransposition events in mouse spermatozoa and embryos were detected using PCR, FACS analysis and confocal microscopy. Our findings show that: (i) sperm cell incorporates exogenous retroelements and favors retrotransposition events, (ii) the inhibition of spermatozoa reverse transcriptase can decrease the retrotransposition frequency in sperm cells, (iii) spermatozoa can transfer exogenous human or mouse retroelements to the oocyte during fertilization and (iv) retroelement RNA overexpression affects embryo morphology and impairs preimplantation development. These findings suggest that the integration of exogenous retroelements in the sperm genome, as well as their transfer into the mouse oocyte, could give rise to new retrotransposition events and genetic alterations in mouse spermatozoa and embryos.Reproduction (2016) 152 [185][186][187][188][189][190][191][192][193]