Update readme for MRL

README.md

@@ -147,6 +147,12 @@ configs:
    * 10-fold cross-validation split
 * Mean ribosome load prediction from Sample et al. (2019) [2]
    * input sequence: 5'UTR
+   * ouput: mean ribosome load
+   * the original data source: https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE114002
+   * Similar to the previous studies [2, 4], we also split the data into the following three 
+     * train: total 76.3k samples
+     * val: total 7600 samples (also called as Random 7600 in [4])
+     * test: total 7600 samples (also called as Human 7600 in [4])
 * Transcript abundance prediction from Outeiral and Deane (2024) [3]
    * 7 organisms: A. thaliana, D. melanogaster, E.coli, H. sapiens, S. cerevisiae, H. volcanii, and P. pastoris
    * input sequence: CDS
@@ -175,4 +181,4 @@ The datasets listed below are collected following the setting in Wang et al. (20
 1. Yanyi Chu, Dan Yu, Yupeng Li, Kaixuan Huang, Yue Shen, Le Cong, Jason Zhang, and Mengdi Wang. A 5 utr language model for decoding untranslated regions of mrna and function predictions. Nature Machine Intelligence, pages 1–12, 2024.
 2. Paul J Sample, Ban Wang, David W Reid, Vlad Presnyak, Iain J McFadyen, David R Morris, and Georg Seelig. Human 5 utr design and variant effect prediction from a massively parallel translation assay. Nature biotechnology, 37(7):803–809, 2019.
 3. Carlos Outeiral and Charlotte M Deane. Codon language embeddings provide strong signals for use in protein engineering. Nature Machine Intelligence, 6(2):170–179, 2024.
-4. Xi Wang, Ruichu Gu, Zhiyuan Chen, Yongge Li, Xiaohong Ji, Guolin Ke, and HanWen. Uni-rna: universal pre-trained models revolutionize rna research. bioRxiv, pages 2023–07, 2023.
+4. Xi Wang, Ruichu Gu, Zhiyuan Chen, Yongge Li, Xiaohong Ji, Guolin Ke, and HanWen. Uni-rna: universal pre-trained models revolutionize rna research. bioRxiv, pages 2023–07, 2023.