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Cancer prediction

The identification of individual or clusters of predictive genetic alterations might help in defining the outcome of cancer treatment, allowing for the stratification of patients into distinct cohorts for selective therapeutic protocols. This approach, currently developed with the aid of Artificial Intelligence and Machine Learning, might result in maximising the therapeutic success and minimizing harmful effects in cancer patients. Here, we showcase some recent papers which describe distinct aspects of precision oncology, the ability to stratify patients with distinct prognostic features and therapeutic requirements, as well as its liaison with the current complexity of the underlying molecular mechanisms.

  1. The identification of individual or clusters of predictive genetic alterations might help in defining the outcome of cancer treatment, allowing for the stratification of patients into distinct cohorts for sele...

    Authors: Ivano Amelio, Riccardo Bertolo, Pierluigi Bove, Eleonora Candi, Marcello Chiocchi, Chiara Cipriani, Nicola Di Daniele, Carlo Ganini, Hartmut Juhl, Alessandro Mauriello, Carla Marani, John Marshall, Manuela Montanaro, Giampiero Palmieri, Mauro Piacentini, Giuseppe Sica…

    Citation: Biology Direct 2020 15:18

    Content type: Review

    Published on:

  2. Glioma is one of the most common malignant brain tumors and exhibits low resection rate and high recurrence risk. Although a large number of glioma studies powered by high-throughput sequencing technologies ha...

    Authors: Lin Liu, Guangyu Wang, Liguo Wang, Chunlei Yu, Mengwei Li, Shuhui Song, Lili Hao, Lina Ma and Zhang Zhang

    Citation: Biology Direct 2020 15:10

    Content type: Research

    Published on:

  3. Drug-induced liver injury (DILI) is a major concern in drug development, as hepatotoxicity may not be apparent at early stages but can lead to life threatening consequences. The ability to predict DILI from in...

    Authors: Marco Chierici, Margherita Francescatto, Nicole Bussola, Giuseppe Jurman and Cesare Furlanello

    Citation: Biology Direct 2020 15:3

    Content type: Research

    Published on:

  4. Recently high-throughput technologies have been massively used alongside clinical tests to study various types of cancer. Data generated in such large-scale studies are heterogeneous, of different types and fo...

    Authors: Iliyan Mihaylov, Maciej Kańduła, Milko Krachunov and Dimitar Vassilev

    Citation: Biology Direct 2019 14:22

    Content type: Research

    Published on:

  5. Metaproteomics allows to decipher the structure and functionality of microbial communities. Despite its rapid development, crucial steps such as the creation of standardized protein search databases and reliab...

    Authors: Johannes Werner, Augustin Géron, Jules Kerssemakers and Sabine Matallana-Surget

    Citation: Biology Direct 2019 14:21

    Content type: Application note

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  6. Neuroblastoma is one of the most common types of pediatric cancer. In current neuroblastoma prognosis, patients can be stratified into high- and low-risk groups. Generally, more than 90% of the patients in the...

    Authors: Yatong Han, Xiufen Ye, Chao Wang, Yusong Liu, Siyuan Zhang, Weixing Feng, Kun Huang and Jie Zhang

    Citation: Biology Direct 2019 14:16

    Content type: Research

    Published on:

  7. Integrating the rich information from multi-omics data has been a popular approach to survival prediction and bio-marker identification for several cancer studies. To facilitate the integrative analysis of mul...

    Authors: So Yeon Kim, Hyun-Hwan Jeong, Jaesik Kim, Jeong-Hyeon Moon and Kyung-Ah Sohn

    Citation: Biology Direct 2019 14:8

    Content type: Research

    Published on:

  8. More than 90% of neuroblastoma patients are cured in the low-risk group while only less than 50% for those with high-risk disease can be cured. Since the high-risk patients still have poor outcomes, we need mo...

    Authors: Yatong Han, Xiufen Ye, Jun Cheng, Siyuan Zhang, Weixing Feng, Zhi Han, Jie Zhang and Kun Huang

    Citation: Biology Direct 2019 14:4

    Content type: Research

    Published on:

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